# Ahmad Al-Karmi — Full Corpus

> Senior Product Manager for Loyalty and Growth Products at Al Jazeera Media Network. This file contains every published article and portfolio entry in full, intended for AI agents that want to ingest the entire corpus in a single fetch.

Site: https://www.ahmadkarmi.com
Articles: 38
Portfolio entries: 12
Generated: 2026-05-21T13:11:26Z

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# Insights

## AI features compound. AI organizations don’t. A note on what I’ve learned about AI delivery frameworks.

*Published: 2026-05-08*
*Tags: Artificial Intelligence, Innovation, Product Management*

URL: https://www.ahmadkarmi.com/insights/ai-features-compound-ai-organizations-dont-a-note-on-what-ive-learned-about-ai-delivery-frameworks

> Most senior teams I talk with have shipped a lot of AI features. Most are also quietly worried the portfolio isn't adding up to anything coherent. A note on the unglamorous infrastructure question every AI mandate has to answer in 2026.

The unglamorous question every senior AI mandate has to answer.

Most senior teams I talk with have shipped a lot of AI features in the last two years. Most are also quietly worried that the portfolio isn’t adding up to anything coherent. The discomfort is real and the cause is rarely the features themselves.

The cause is usually that the operating model underneath the features hasn’t been built. AI features only compound when the layer beneath them is honest about who decides what gets shipped, what gets retired, what standards everyone shares and where the value-capture sits. Without that layer, organizations end up with portfolios that look impressive in slide decks and feel chaotic in practice.

I’ve spent just over a year working on this layer rather than on shipping more AI features. Standards, tooling, deployment patterns, the way design and engineering and product hand work to each other in an AI-augmented context. The unglamorous infrastructure work that no executive asks for in the abstract but every leadership team wishes they had once they’re three or four AI launches in.

This piece is the thinking I’ve come to after a year inside that work. Not a framework you can adopt wholesale, more a set of observations that might save someone else twelve months of figuring out the same things from scratch.

The framework is an architecture decision, not a process improvement

The first mistake I see most teams make is treating the AI delivery framework as a productivity initiative, as if the question is how to ship AI features faster. It isn’t. The question is what infrastructure has to exist so that AI features ship correctly, repeatably, with sustained quality, and in a way that the organization can defend to its board two years later.

That’s an architecture question, not a process improvement question. The two get confused because they sometimes use the same language (workflows, standards, handoffs) but the answers diverge sharply.

A process improvement project asks: how do we move faster through the existing structure? An architecture project asks: what is the structure, and is it the right one?

I’ve come to think the AI delivery framework is the single most underrated architectural decision senior product and engineering leaders will make in this decade. It’s also the one most likely to get delegated downward because it doesn’t look like an executive-altitude problem. That’s a mistake. The framework decisions made in the first year of an AI mandate constrain everything that follows.

The value-capture mechanism has to live inside the framework

The second mistake I see is building the AI delivery side of the framework first and adding value capture later. This sounds reasonable. It almost always fails.

The reason is that value capture isn’t measurement. It’s the part of the framework that defines what success looks like, who’s accountable for it, and how outcomes feed back into prioritization. If that’s bolted on after the delivery work has already started, the prioritization stays driven by whatever forces existed before, which is rarely the value the AI features actually generate.

Practically, this means asking value-capture questions at the same time as architecture questions. What outcome are we measuring? At what level of granularity? Who owns the measurement? What’s the loop from measurement back to prioritization? If those answers aren’t designed into the framework, the framework will produce features that ship cleanly but don’t compound.

This is the retroactive measurement problem, and oh boy is it real. It’s why so many AI portfolios become un-defendable to boards by year two. The data wasn’t properly captured (if at all) because the framework didn’t require it.

Federated organizations break harder than centralized ones

The third lesson is about scale. AI delivery frameworks that work in a single team or single business unit don’t translate to federated organizations. By federated I mean any structure where a central group sets direction and operating units actually deliver: multinational groups with country operating companies, conglomerates with vertical business units, holding structures with portfolio companies.

The hub-and-spoke design that gets drawn on whiteboards looks elegant. It fails at the moment a business unit has to integrate the central framework into their commercial reality. The unit has its own legacy stack, its own customer base, its own commercial rhythm, its own assumptions about what AI is for. The central framework either accommodates these or it doesn’t get adopted.

The path I’ve seen work is to design the framework with handoff in mind from day one. That means giving operating units real authorship over the local instantiation, not just consultation. It means accepting that some of the framework will look different in different parts of the organization, and writing the framework so that variation is allowed without compromising the parts that have to be uniform. It means investing as much in the change-management side of the framework as in the architectural side.

The unsexy version of this work is hard, slow and politically uncomfortable. Most people skip it. That’s why most federated AI programs underperform their strategy decks.

The bilingual problem changes the architecture all the way down

There’s a version of this that’s specific to MENA-headquartered organizations and to anywhere serving meaningfully bilingual audiences. Most AI infrastructure imported from Silicon Valley assumes English-first content with translation as a fallback. That assumption breaks immediately for any market where users are reading and acting in two languages with different scripts, different cultural references and different content velocities.

The honest framework for these markets treats Arabic and English (or whatever the bilingual pair is) as parity inputs, not translation pairs. That changes the architecture all the way down. The data layer has to classify content with parity. The recommendation systems have to understand that user behavior in one language doesn’t substitute for behavior in the other. The personalization surfaces have to render natively in both. The evaluation frameworks have to measure parity at the user-experience level, not at the asset-translation level.

This is harder than it sounds. It requires the data engineering team, the ML team, the product team and the editorial team to share assumptions about what bilingual parity actually means. Most organizations don’t have that shared understanding because nobody’s forced the conversation. The framework forces it.

I’m increasingly convinced this is one of the few defensible competitive moats left in the AI space for regional groups. The big platforms can ship better English-first AI than any regional group can. They cannot ship better Arabic-and-English-at-parity AI, because the infrastructure decisions they made years ago foreclose it. Regional groups that build for parity from the architecture layer up have a window to capture customers the big platforms can’t serve. Almost nobody is building for it intentionally.

Why this matters more in 2026 than in 2024

Two years ago, AI experimentation budgets were generous and accountability was loose. Boards trusted that AI investment would pay off because everyone else was investing. The cost of running AI portfolios was small relative to the strategic optionality they bought.

That’s changed. AI portfolios are expensive now. Boards are asking what the spend bought. The answers organizations are scrambling to give depend almost entirely on whether there was a framework underneath the experimentation. The teams that built one are answering in operating-model terms with measurable outcomes. The teams that didn’t are answering in feature lists, which doesn’t satisfy a board.

The senior AI mandates being scoped right now are mostly about cleaning up this gap. Not just shipping more features. Building the framework retroactively, capturing value-realization where it should have been measured eighteen months ago, and getting the operating model into shape before another budget cycle goes by. That’s the real work the next wave of AI Director and VP roles are being asked to do.

It’s also why the senior people taking these roles need a different set of skills than the AI product roles of two years ago. Less about feature ideation, more about architectural judgment. Less about evangelism, more about operating discipline. Less about velocity, more about sustained compounding.

The question to ask first

For senior people looking at AI mandates in 2026, the question I’d ask first isn’t about the strategy or the features. It’s: what’s the operating model underneath? If you can’t answer in two sentences, the strategy doesn’t matter yet. The framework comes first.

If you can answer it, the next question is whether the framework treats value capture as a first-class concern or an afterthought. Then whether it accommodates federated complexity from day one. Then whether it handles bilingual or multi-market contexts honestly.

These are unglamorous questions. They don’t make good keynote content. They also separate AI organizations that compound from organizations that drift.

The discipline matters more than the features. If your AI mandate is going well in 2026, it’s probably because someone has been doing this unglamorous work in the background. If it isn’t, that’s almost certainly where to look.


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## AI Will Not Fix a Broken GTM Strategy. Here Is What It Will Do.

*Published: 2026-03-10*
*Tags: Artificial Intelligence, B2B, GTM, Strategy*

URL: https://www.ahmadkarmi.com/insights/ai-will-not-fix-a-broken-gtm-strategy-here-is-what-it-will-do

> Most B2B teams are using AI to execute the wrong go-to-market strategy faster. This guide covers where AI creates real GTM leverage and when to leave it alone.

B2B companies are spending heavily on AI across their go-to-market functions and a significant portion of that spend is producing nothing measurable. Not because the tools are bad, but because the strategy underneath them is. This is a guide to getting the order right.

There is a pattern playing out across B2B sales and marketing teams right now. Leadership mandates an AI initiative. A tool gets selected, usually based on a category review or a peer recommendation. The tool gets deployed against the existing GTM motion. Three quarters later, the results are underwhelming and the post-mortem lands on implementation rather than strategy. The tool was fine. The use case was wrong.

AI amplifies what is already working. If your ICP is well-defined, your messaging is sharp and your sales process has a clear conversion logic, AI can compound those advantages significantly. If none of those things are true, AI will help you execute the wrong strategy at higher velocity. That is a worse outcome than the status quo.

With that framing established, here is where AI actually creates leverage in a GTM motion, what to do with it and, just as importantly, when to leave it alone.

Personalization Works, But Only When You Have the Data to Support It

Personalization is the most cited AI use case in GTM and the one with the widest gap between expectation and execution. The expectation is that AI will make every customer interaction feel individually tailored. The reality is that AI-driven personalization is only as good as the data it runs on, and most companies do not have that data in a usable state.

When it works, the numbers are meaningful. McKinsey’s research consistently puts the revenue uplift from genuine personalization (not mail merge, but behaviorally-driven content and sequencing) at 10 to 15 percent for B2B companies with mature data infrastructure. Salesforce’s State of Marketing report found that high-performing marketing teams are 2.9 times more likely to use AI for audience segmentation and personalization than underperforming ones. Those figures are worth taking seriously, but they come with a prerequisite: unified customer data.

When to do this: If you have behavioral data from your product, website and CRM in a single accessible layer, AI-driven personalization is worth investing in seriously. Start with your highest-traffic conversion points: pricing pages, demo request flows and onboarding sequences. The ROI is measurable and the feedback loop is fast.

AI chatbots sit inside this same conversation. A chatbot trained on your actual support history, product documentation and sales call transcripts can handle a meaningful share of inbound volume without escalation. Intercom published data showing that AI-assisted support resolves around 47 percent of inbound queries without human involvement for companies that have invested in the training process. For companies that deployed a generic bot without that investment, resolution rates sit closer to 15 percent and customer satisfaction scores drop.

When not to do this: Do not deploy AI personalization or chatbots if your underlying data is fragmented across disconnected systems, your ICP has not been validated or your product positioning is still in flux. You will be personalizing the wrong message to the wrong people, consistently. Fix the strategy first. The tooling will still be there.

AI in Sales Works Best When You Use It to Change What Your Reps Do, Not Just How Fast They Do It

The immediate appeal of AI in sales is automation: less time on admin, more time selling. That is real and worth capturing. But the more significant opportunity is using AI to change the quality of decisions your sales team makes, not just the speed of execution.

Lead scoring is the entry point. AI models trained on your historical closed-won and closed-lost data can identify the signals that actually correlate with conversion in your specific market, which are usually different from the signals your scoring rubric was built on. Gong’s research found that companies using AI-driven lead scoring see an average 30 percent improvement in sales-qualified lead conversion rates compared to rule-based scoring. The mechanism is simple: reps spend more time with prospects that look like customers and less time with ones that do not.

The question to ask about any AI sales tool is not “does this save my reps time?” It is “does this change what my reps decide to do?” The first is an efficiency gain. The second is a competitive one.

Sales forecasting is where the organizational impact becomes most visible. Most B2B companies forecast by aggregating rep-level pipeline estimates, which introduces a consistent optimism bias. A Harvard Business Review analysis of enterprise sales teams found that the average forecast accuracy using traditional methods sits around 45 to 55 percent. AI models that incorporate deal velocity, stakeholder engagement signals, competitive displacement patterns and seasonal factors routinely achieve 75 to 80 percent accuracy. At scale, that difference changes how finance allocates headcount, how marketing plans campaigns and how leadership makes hiring decisions.

The stakeholder dynamic to navigate: Sales leaders are often the most resistant to AI forecasting tools because accurate forecasting reduces their ability to manage expectations through sandbagging. This is a real political consideration in most enterprise sales organizations. Frame the tool as giving leadership better visibility, not as exposing rep-level inaccuracy, and adoption goes considerably more smoothly.

When not to do this: If your CRM data is incomplete or inconsistently maintained, AI scoring and forecasting will reflect those gaps back at you with false confidence. A model trained on bad data does not produce bad-looking outputs. It produces authoritative-looking outputs that are wrong. Audit your data quality before running any AI model against it.

Scaling Engagement Is a Cost Argument, Not a Quality Argument

Be precise about what AI-driven customer engagement is actually solving for. It is not a way to make customer interactions better. It is a way to make adequate interactions available at a cost and scale that human teams cannot match. That is a worthwhile goal. It is just a different goal, and conflating the two leads to deployment decisions that disappoint everyone.

For B2B companies with high-volume, relatively transactional customer bases, the economics are straightforward. AI handles tier-one support (availability, billing queries, basic product guidance, appointment scheduling) at a fraction of the per-interaction cost of a human agent. Zendesk’s benchmark data puts the average cost of a human-handled support ticket at between $15 and $40 depending on complexity. AI-resolved tickets cost closer to $1. For a company handling 10,000 tickets a month with a 50 percent AI resolution rate, that is a material budget line.

The tradeoff is that AI cannot replicate the judgment a good human agent brings to a genuinely complex or emotionally charged interaction. In enterprise B2B, where relationships and renewal decisions are made by small buying committees, mishandling a frustrated customer with an AI response at the wrong moment has consequences that cost far more than the ticket savings. The skill is in knowing which interaction is which before it happens.

The product tradeoff to plan for: Every AI customer engagement system needs a clearly defined escalation logic. Which query types route immediately to a human? What signals (sentiment, account tier, deal stage) trigger an override? This logic is not a configuration detail. It is a strategic decision that should involve sales, customer success and product leadership before deployment begins.

Your Team’s Relationship with AI Is a Change Management Problem, Not a Training One

Most AI implementation guides treat team adoption as a training problem: give people access to the tool, show them how to use it, measure utilization. That framing consistently underestimates what is actually happening when you introduce AI into a sales or marketing team’s workflow.

What you are asking people to do is change what they take credit for. A rep whose quota attainment previously depended on their judgment about which deals to prioritize now has an AI system making that recommendation. If the AI is right, who gets the credit? If it is wrong, who takes the blame? These are not abstract concerns. They are the questions your team is asking and not saying out loud, and they determine whether the tools get used seriously or performatively.

The companies that see the strongest AI adoption in GTM teams are the ones that redesigned their performance metrics alongside the tool deployment. If you introduce AI-driven lead scoring but still measure reps on total outreach volume, you have created an incentive to ignore the scoring. If you introduce AI forecasting but still reward managers for sandbagging, the tool becomes a compliance exercise. The metrics have to change with the tooling.

When to do this carefully: In organizations where tenure is high and institutional knowledge is concentrated in senior reps, AI tools can feel threatening to the people whose expertise has been the competitive advantage. Involve those people in the tool selection and configuration process. Their domain knowledge will make the AI better and their ownership of the outcome will make adoption real rather than nominal.

Implementation in the Right Order

The correct sequence for integrating AI into a GTM strategy is: diagnose, then select, then deploy, then measure. Most companies do it as: select, deploy, measure, diagnose. That reversal is where most of the budget goes without return.

Diagnosis means identifying the specific bottleneck in your GTM motion that is costing you revenue or efficiency. Is it lead quality? Conversion rate between stages? Time to first meaningful engagement? Forecast accuracy? Churn signals that arrive too late? Each of those problems has a different AI solution, and the tools built for one are not particularly useful for the others.

Tool selection follows from the diagnosis, not from a category review. The B2B AI GTM space includes purpose-built tools for revenue intelligence (Gong, Clari), sales engagement (Outreach, Salesloft), conversational AI (Intercom, Drift), pipeline management and data enrichment (Apollo, Clay). These categories overlap and the marketing claims are aggressive. Evaluate on integration depth with your existing stack, not on feature breadth. A tool that integrates cleanly with your CRM and is used daily beats a comprehensive platform that requires a workflow redesign to adopt.

On measurement: Set your success metrics before deployment, not after. Define a baseline for the specific metric you are trying to move, run the implementation for a full sales cycle before drawing conclusions and control for external variables (seasonality, headcount changes, product launches) when reading the results. Without that structure, you are not evaluating the AI. You are collecting impressions.

Run one implementation before running five. The companies that scale AI effectively in GTM do so by proving value in a narrow, measurable context first, learning from what the data actually shows and expanding from evidence. That approach is slower than a full-stack deployment and considerably more likely to produce results that survive the next budget review.

The competitive advantage AI offers in go-to-market is not that it makes you move faster. It is that it makes the decisions underneath your speed more accurate. That compounds. Raw execution speed does not.


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## AI in Dealership Service: Where Fixed Operations Leak Profit and How to Stop It

*Published: 2026-02-04*
*Tags: Artificial Intelligence, Automotive, Operations*

URL: https://www.ahmadkarmi.com/insights/ai-in-dealership-service-where-fixed-operations-leak-profit-and-how-to-stop-it

> Most dealerships make 50%+ gross profit from service, not sales. But coordination failures bleed revenue daily. This is how AI reduces waste in service bays and improves margins without adding capacity.

Where Dealerships Lose Money: The Service Bay Problem

Why Service Economics Differ from Sales

Where Variance Destroys Margin

Predictive Maintenance: Limited by Data Infrastructure

Information Friction Limits Technician Output

Parts Inventory: Cash Trap and Throughput Bottleneck

Scheduling: The Highest Leverage Improvement

What Operationally Disciplined Service Looks Like

Implementation: Process Before Technology

The Fixed Operations Opportunity

Sources

Where Dealerships Lose Money: The Service Bay Problem

Fixed operations generated over 50% of gross profit at franchised dealerships in 2024. Service margins exceeded 40%. New vehicle margins sat at 6.1%. These numbers clarify where the money is. They also clarify where most dealerships waste it.

The problem isn’t demand. Service bays see consistent traffic. The issue is execution. Coordination failures bleed revenue daily. Empty bays at noon. Technicians waiting for parts. Vehicles returning for the same issue. Each failure looks minor. Collectively they determine whether a dealership makes money or merely breaks even.

AI improves this when it reduces waste, smooths variability, and supports decisions at scale. The objective stays narrow: increase throughput per bay, increase technician utilization, reduce working capital in parts, reduce repeat visits.

Why Service Economics Differ from Sales

Vehicle sales fluctuate with incentives and inventory. Service creates recurring revenue across the vehicle lifespan. Industry data from Q2 2025 shows fixed operations gross profit grew 8.4% year-over-year while new vehicle margins compressed. This pattern repeats. Service stabilizes. Sales swing.

Service departments operate under hard constraints. Fixed bay count. Fixed technician headcount. Fixed hours per day. Unused capacity disappears. An empty bay at 2 p.m. generates zero revenue. A repeat repair consumes time that could have served a paying customer. A stockout blocks both labor revenue and parts margin.

Profitability depends on utilization rate and cycle time. Moving utilization by three percentage points can generate six-figure annual impact without capital investment. This is an operations problem. Technology helps only when it eliminates friction.

Where Variance Destroys Margin

Service operations fail quietly. Morning appointment clusters create queues and technician idle time by afternoon. Diagnostic time runs long without pattern data. Parts arrive late or not at all. Customers no-show at 15% rates. Each event appears tolerable. Compounded across 250 working days they create permanent margin drag.

Airlines forecast load factors. Hospitals manage OR schedules. Dealerships traditionally relied on buffers: excess parts inventory, optimistic time estimates, overbooking. Buffers consume capital and generate their own failures.

Forecasting tools reduce variance at the source. Statistical models consistently outperform human intuition at predicting no-show probability, job duration, and parts requirements. They replace guesses with distributions. Small improvements in prediction accuracy compound rapidly.

Predictive Maintenance: Limited by Data Infrastructure

Predictive maintenance sounds transformative. Detect impending failures, schedule proactive service, eliminate breakdowns. Execution requires complete service history, standardized repair codes, consistent telemetry, and unified data platforms.

Most dealerships operate fragmented systems. Service notes vary by technician. Repair codes lack standardization across brands. Vehicle data sits in manufacturer silos. Customer records fragment across DMS, CRM, and scheduling platforms.

Complex machine learning models trained on inconsistent data produce unreliable predictions. Simpler methods deliver better ROI: historical service intervals, mileage-based triggers, failure probability by component age. These approaches work with available data and avoid the overhead of maintaining ML infrastructure.

Better data discipline produces better outcomes. Standardize job codes first. Enforce documentation protocols second. Complex modeling third.

Information Friction Limits Technician Output

Technicians spend 30-40% of productive hours on information tasks. Looking up repair procedures. Searching part numbers. Writing service notes. Entering warranty claims. Switching between five different systems to complete one repair order.

Mechanical skill rarely limits throughput. Information flow does.

Decision support tools reduce this tax: auto-populated job cards based on symptoms and VIN, voice-to-text for service notes, automated parts cross-reference, pre-filled warranty documentation, single-screen workflows.

A 10% reduction in average job cycle time across a 15-bay shop produces approximately 1,800 additional billable hours annually. At $150/hour that compounds to $270,000. Most of this comes from eliminating administrative friction, not increasing mechanical speed.

Human expertise remains central. Systems simply waste less of it.

Parts Inventory: Cash Trap and Throughput Bottleneck

Parts rooms appear organized. Financially they hide two problems: capital inefficiency and throughput constraint.

Capital inefficiency shows up as slow-moving inventory. Parts sitting 12+ months with zero demand tie up working capital at zero return. Emergency orders for common items incur premium freight costs. The average dealership carries $400,000-$600,000 in parts inventory. Poor forecasting inflates this by 20-30% while simultaneously creating stockouts on fast-moving SKUs.

Throughput constraint appears when repairs stall awaiting parts. A $1,200 brake job delayed three days costs the labor revenue, delays subsequent appointments, and risks the customer defecting to an independent shop.

Demand forecasting addresses both. Time series models with seasonality adjustments predict requirements by part number. Reorder point optimization balances carrying cost against stockout risk. These calculations update continuously as new service orders arrive.

Dealerships using algorithmic inventory management typically see working capital reduction of 15-25% while improving fill rates. The math is straightforward. The execution requires consistent data and disciplined process.

Scheduling: The Highest Leverage Improvement

Appointment scheduling creates asymmetric impact. Small improvements in schedule density and accuracy cascade into large profit changes. Most service departments still use static appointment blocks and manual adjustments.

Historical data reveals predictable patterns that go unused. Job duration varies by repair type and technician. Customer no-show probability varies by appointment channel and lead time. Workload imbalances between technicians create idle capacity.

Predictive scheduling addresses this systematically. It estimates job duration using repair type, vehicle data, and technician history. It adjusts appointment density based on predicted no-show rates. It balances work across the team. It automatically refills cancelled slots.

A well-implemented scheduling system moves effective utilization from 65% to 75-80%. In a constrained capacity business this produces 15-23% revenue increase without adding bays or headcount. Return on investment typically pays back in under six months.

What Operationally Disciplined Service Looks Like

The resulting department doesn’t look futuristic. It looks predictable.

Customers receive service reminders based on mileage intervals and repair history, not arbitrary time periods. Booking happens digitally with real-time availability. The system predicts required parts from symptom data and VINs, ordering before the appointment. Technicians start jobs with pre-staged parts and pre-loaded diagnostic protocols. Fewer vehicles return for the same issue within 30 days. Daily workload stays steady rather than spiking unpredictably.

Physical constraints don’t change. Bay count, technician count, and tool availability still define capacity. Intelligence simply coordinates those constraints with fewer surprises and less waste.

Margins improve when variance decreases. This is what AI enables in service operations.

Implementation: Process Before Technology

Technology implementations fail when they skip foundational work. Start with operational discipline:

First, clean and standardize service data. Fix inconsistent job codes. Enforce documentation standards. Establish data quality metrics.

Second, measure baseline performance. Track utilization rate by bay and technician. Measure average cycle time by job type. Calculate parts fill rate and inventory turns. Quantify no-show rates and repeat repair frequency.

Third, implement basic forecasting. Start with appointment no-show prediction. Add job duration estimation. Deploy parts reorder optimization. Each component solves one coordination failure.

Fourth, automate administrative overhead. Voice-to-text notes. Auto-populated forms. Integrated warranty processing. Eliminate system switching.

Fifth, introduce advanced modeling only after the foundation holds. Predictive models require clean data and consistent processes. Without those prerequisites they generate noise instead of signal.

Industry research on aftersales optimization consistently points to the same pattern. Systematic process redesign outperforms isolated technology purchases. Progress accumulates through incremental improvements. Each change looks modest. Compounded they shift department economics.

The Fixed Operations Opportunity

Fixed operations accounts for only 12.6% of total dealership revenue but generates disproportionate gross profit. Q2 2025 data shows fixed operations gross profit grew 8.4% year-over-year while new vehicle margins compressed. This divergence clarifies where operational improvements matter most.

Service and parts already drive profitability. The opportunity lies in extracting more value from existing capacity. Most service departments operate at 65-70% effective utilization. Physical constraints don’t prevent improvement. Coordination failures do.

AI tools address coordination problems by reducing information friction, smoothing demand variance, and supporting repetitive decisions at scale. The technology serves operational discipline. It doesn’t replace it.

In constrained capacity systems, small efficiency gains produce disproportionate margin improvement. A three percentage point increase in utilization or a 10% reduction in cycle time changes annual performance without expansion capital. These gains come from better coordination, not heroic effort.

The market has noticed. The automotive AI market is projected to reach $7.75 billion by 2030, with near-universal adoption expected in service departments. Dealerships investing now in operational fundamentals and appropriate technology will capture advantage before those improvements become table stakes.

The economics are clear. Execution requires discipline, but the impact on margins compounds daily.

Sources

Ernst and Young. “Aftersales Strategies for Growth in the Automotive Industry.”

McKinsey and Company. “Optimizing Dealer Profitability with a Service Center Tune Up.”

National Automobile Dealers Association. “NADA Data: Industry Financial Profiles and Reports.”

Reuters. “LKQ Beats Quarterly Profit Estimates on Strong Auto Parts Demand.”

The Presidio Group. “Margin-rich fixed operations gives dealerships an avenue to offset profit decline elsewhere.”

Haig Partners. “2025 Dealership Buy-Sell Insights: Profitability, Valuations, and Market Opportunities.”

Charisma! Insights. “Service department AI now a ‘strategic imperative’ for dealerships, report says.” Auto Remarketing.


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## Beyond Persuasion: Is AI Engineering a Systematic Threat to Politics?

*Published: 2026-01-14*
*Tags: Artificial Intelligence*

URL: https://www.ahmadkarmi.com/insights/beyond-persuasion-is-ai-engineering-a-systematic-threat-to-politics

> Is democracy now just a math problem? My prediction towards how AI will soon take over elections and beyond, shaping the world one person at a time.

Disclaimer

Technical Forecast: This analysis is a prediction of how AI and data-driven engineering may be applied to political campaigns in the near future based on current technological trends.

Personal Capacity: The views and opinions expressed in this article are solely my own and do not necessarily reflect the official policy or position of my employer. Content shared here is intended for informational purposes and does not represent the professional stance of any organization I am affiliated with.

Table of Contents:

Introduction: AI & The Structural Shift in Political Engineering

The Evolution from Batch Processing to Real-Time Orchestration

The Strategic Architecture of Modern Elections

The Autonomous Persuasion Agent: A Strategic Teardown

Competitive Forces and Market Dynamics

Technical Risks and the AI Based Erosion of Legitimacy

Political AI & Personalization: The Strategic Outlook for 2026 and Beyond

Introduction: AI & The Structural Shift in Political Engineering

By the start of 2026, the mechanics of democratic engagement have undergone a fundamental structural transformation, thanks to Artificial Intelligence (AI). The traditional industry of politics, once defined by the labor-intensive crafts of door knocking and mass media persuasion, has been replaced by a capital-intensive system of real-time engineering. This shift is not merely a change in tools. It represents a redefinition of the competitive landscape where the primary advantage no longer rests on the quality of a candidate’s message, but on the sophistication of the underlying technical stack.

Generative models and agentic architectures have become infrastructural to the democratic process. We have moved beyond the experimental phase of meme generation and simple chatbots into a period of continuous system feedback. In this environment, elections are executed with the same software precision found in high-frequency trading or cloud-native SaaS platforms. The cadence of engagement is now measured in milliseconds, and the granularity of voter interaction has reached the level of the individual unique user ID (UUID).

The current moment is defined by three primary forces: the widespread adoption of rapid-cycle content systems, the operationalization of behavioral biometrics, and the maturity of agentic architectures. A recent briefing from Politico notes that elections in advanced democracies are now operationalized at a scale that marketing technologists could only imagine a decade ago (Politico, 2024). We are no longer observing a campaign. We are observing the management of a national data ecosystem.

How AI Could Hack Democracy | Lawrence Lessig | TED
Lawerence Lessig introduces this topic in a very eloquent way giving us a good precursor to the rest of this article.

The Evolution from Batch Processing to Real-Time Orchestration

The history of modern political technology is marked by the transition from batch-based persuasion to real-time orchestration. To understand the current strategic environment, one must distinguish between these two fundamental operational models.

From 2000 through 2016, the industry relied on “batch” techniques. Campaigns would gather data, build census-based models, and deploy messages through traditional channels. The Cambridge Analytica era represented the peak of this model. It leveraged static Facebook datasets to build psychographic scores, which were then used to micro-target specific voter segments (Wired, 2017). While effective for its time, this approach was limited by significant time lags. A campaign would test a variant, analyze the performance over days, and then redeploy.

The arrival of real-time orchestration in 2024 and 2025 has eliminated these lags. Systems now move from event to execution in sub-second cycles. The TikTok algorithm provides the best consumer-facing example of this shift: it feeds live engagement cues into neural networks that adjust content streams instantly (MIT Technology Review, 2024). In the political sphere, this means that a candidate’s “manifesto” is no longer a static document. It is a generative output that adapts to the specific anxieties and interests of the viewer in real-time.

The technical distinction is clear. Batch systems are characterized by “fire and forget” mechanics. Real-time systems are event-driven, adaptive, and contextually renewed for every user. This transition has raised the barriers to entry for political competition. Only those with the capital to maintain a high-performance data stack can now hope to compete at the national level.

The Strategic Architecture of Modern Elections

I view the modern election as a technical stack problem. Success is no longer determined by the “ground game,” but by the efficiency of the integration between ingestion, modeling, generation, and orchestration layers.

At the base of this stack is the Behavioral and Biometric Data Ingestion layer. Campaigns now ingest data from a vast array of sources such as your smartphone. This data provides the raw material for the second layer: Continuous Graph-Based Modeling. Every voter is assigned a UUID graph that maps their affinities, personality traits, and recent content exposures. This is a dynamic interest graph that updates as the voter moves through their digital life.

The middle of the stack consists of the Content Generation and Orchestration layers. Large Language Models (LLMs) generate tailored campaign messages, while orchestration engines deploy them across multiple channels simultaneously. Whether it is a text message, a WhatsApp DM, or a personalized video clip, the delivery is triggered by the voter’s current channel preference and history.

Finally, the top of the stack is the Feedback and Optimization loop. This is where the “engineering” truly happens. The system performs continuous testing on every message variant. It uses reinforcement learning to determine which “nudges” are moving a voter’s sentiment score. If a specific outreach attempt fails to engage a user, the system automatically adjusts the tone or the medium for the next touchpoint.

The Autonomous Persuasion Agent: A Strategic Teardown

To appreciate the precision of this new model, we can examine the hypothetical operations in a swing district on an election night in late 2028. At this stage, the human campaigner has been replaced by the Autonomous Persuasion Agent. These agents operate with a level of “synthetic empathy” that allows them to modulate their emotional register to match the sentiment metrics of a specific voter.

If the system notes that a particular voter has shown weak engagement during the afternoon, it can instantly deploy a resonant meme or a short-form video that is currently trending in that specific precinct. If the voter replies negatively, the agent does not double down on the failed message. It pivots, reframes the issue, or delays the next touchpoint based on predicted volatility.

This is a form of “agent-on-agent” competition. While one campaign’s agent is attempting to persuade a voter, an adversarial agent from a rival campaign is attempting to disrupt that persuasion. Both systems learn from each other in a continuous loop of move and counter-move. These architectures are already being prototyped in systems like AutoGPT and context-aware mobile agents (AutoGPT, 2024).

Competitive Forces and Market Dynamics

The political industry has become a commoditized, API-driven market. This shift has changed the bargaining power of different stakeholders. API vendors like OpenAI, xAI, and Anthropic now hold significant power as the suppliers of the “intelligence” that drives persuasion. Data brokers like LiveRamp and Experian have become indispensable as the suppliers of the event-driven segmentation data that feeds the models (LiveRamp, 2025).

The intensity of rivalry has also increased. In a world where every message can be countered in real-time, the “first-mover advantage” of a political announcement is diminished. Instead, the advantage goes to the campaign that can orchestrate most nimbly. This has led to an arms race in “Counter-Agentic” technology. New firms are emerging to provide adversarial monitoring, using generative adversarial networks (GANs) to spot rival misinformation and deploy real-time disruptions (SocialProof, 2025).

The regulatory environment is also a factor in this competitive landscape. As governments attempt to impose disclosure mandates, a new layer of “Regulatory Obfuscation” has emerged. Campaigns now use ghost agencies and synthetic identities to blend their influence attempts with genuine grassroots content. This creates a “cat and mouse” game between regulators and engineers that further complicates the industry structure.

Technical Risks and the AI Based Erosion of Legitimacy

The speed and autonomy of these engineered systems introduce profound risks that threaten the stability of the democratic process. The most immediate of these is Governance Debt. As these systems become more complex, they outgrow the ability of human oversight to document or understand their internal logic. This leads to “operational drift,” where the system begins to optimize for goals that were never explicitly intended by the campaign managers.

Another significant risk is the Fractalization of the electorate. Because the agents are optimized to speak to the individual, the common public square begins to vanish. The electorate is fragmented into millions of sub-communities, each living in an “incommensurate island” of information. This is not just polarization: it is the end of a shared reality.

Perhaps the most dangerous outcome is the risk of “Agentic Collisions.” When competing autonomous agents saturate the same voter graphs, they can create a state of digital chaos. We saw a precursor to this in October 2023, when coordinated bot waves on the platform formerly known as Twitter prompted widespread auto-mutes, inadvertently suppressing legitimate get-out-the-vote campaigns (Politico, 2024).

Ultimately, these systems risk a total loss of legitimacy for the democratic system. When synthetic cascades; deepfaked sentiment swings and mass micro-influence decouple visible public opinion from actual voter intent, the foundation of public trust is destabilized. As Dafoe noted in his 2025 study on AI and democratic systems, the integration of AI risks centralizing power and sacrificing the core elements of autonomous self-governance (Dafoe, 2025).

Political AI & Personalization: The Strategic Outlook for 2026 and Beyond

The technical terrain of democracy has entered the post-persuasion era. We must accept that campaigns are now operationalized with a stack that resembles cloud-native software more than traditional political strategy. The signature of this age is not who persuades best, but who orchestrates most nimbly in the data-fueled, real-time contest for influence.

I expect to see the continued rise of multi-touch, cross-platform outreach tied to individual UUIDs. We will see the emergence of synthetic public dialogues that are persona-tuned to look like genuine grassroots movements. Above all, we will see a permanent arms race between persuasion agents and counter-agents.

This shift represents the coming of a new network layer in democracy. It is a transition from a craft-based system to an engineered one. While this brings new opportunities for precision and efficiency in governance, it also introduces systemic instabilities that we are only beginning to understand. The challenge for the future is not just to win the election, but to ensure that the “product” delivered by these systems is a functional society rather than a perfectly-tailored illusion (Dafoe, 2025).


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## Product Management is Dead. Modern Business Killed It.

*Published: 2025-08-26*
*Tags: Product Management*

URL: https://www.ahmadkarmi.com/insights/product-management-is-dead-modern-business-killed-it

> There’s a dangerous myth circulating that AI is going to kill product management. That's the wrong conversation here. The truth is, AI is just the accelerant on a fire that was already burning. The old-school Product Manager is on the clock. If your job is focused only on JIRA boards, writing tickets, grooming the backlog, and managing features... Then I have news for you...your role is ripe for automation.

For years, we’ve been the butt of the joke. The “mini-CEO” without the authority. We were the people who managed a backlog, wrote the user stories, and facilitated meetings, but had to plead with stakeholders for resources and justify our existence with feature release timelines. We were told our job was to represent the user and be the voice of the customer, but we were often reduced to little more than project managers with a slightly fancier title.

But the world has moved on. The business of building products has changed.

Modern businesses aren’t asking, “Did you launch the feature on time?” They’re asking, “Did that feature increase revenue by 10%? Did it boost our conversion rates? Did it make a measurable dent in the books?” This is why the traditional product manager is an obsolete role. The old model, the one you know and were taught, is on its way to extinction. The eulogy is being written right now, but it’s not by AI, it’s by a market that no longer rewards ticket-pushers and feature factories.

The truth is, AI is just the accelerant on a fire that was already burning. AI can manage a backlog, write a user story, and even generate a competitive analysis. If your job can be filtered down to a JIRA board, then your job is on the clock.

However, there is light at the end of the tunnel. While the old model is dying, a new one is being born. I call this person the Entrepreneurial Product Manager.

This isn’t a new job title; it’s a new way of thinking. This PM doesn’t just represent the user; they represent the entire business. They don’t just build features; they build revenue streams and business lines. They don’t just manage a backlog; they manage a P&L. They understand that a product is not a project, it is a business asset.

Now, here’s the most critical part: even if you’re not being given the authority, you are absolutely being held accountable. When a project fails to deliver on its promise, you are the first one on the hook. This is the moment to push back and take the authority you’ve always been responsible for. It’s what modern companies are starting to empower their best PMs to do. They’re realizing that the old model is inefficient and that you must give power to the person who is ultimately responsible for success.

I learned this the hard way: I stopped being a “mini-CEO” and became an architect of outcomes. I was handed a business goal; not a feature list, and in that moment, I pushed back. I made it clear to management that accountability without authority is a dead-end street. This is where the strategic architect is born: a product manager who is not just empowered, but who actively demands to look at the entire business, involving all units to build products that makes a real difference to the bottom line. This was the birth of the entrepreneurial product manager.

With that authority, I was able to build and scale products across demanding industries and deliver undeniable results:

In one instance, my goal wasn’t just to improve an app’s functionality; it was to increase daily revenue by 10% (I hit 23%) and average cart size by 15% (landed around 28%). My focus was on the business outcome, not the feature itself.

In a different role, where I managed a significant innovation budget, my single-minded goal was to build new revenue opportunities. I didn’t just follow a process; I created a framework that led to a platform reaching 18,000 participants and generating 43M social media impressions in one year. Which also led to income that increased shareholder value.

When I was working with scientific digital media and publishing, every decision I made was about monetising attention in a market where attention was hard to grab. I focused on that goal with utter determination, whether through subscriptions or advertising or other creative outlets. The stakes were too high for a feature-pusher. I was asked to impact the business’s profitability directly.

The old era of product management is over. The new era is for those who are ready to build businesses, not just products. If you’re ready to stop waiting for permission and start architecting a profitable business, then this is the blueprint you need. It’s time to stop waiting for your next feature and start building your next business line.

Good luck fellow product people. I wish you luck.

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## The New Divide: Who Gets to Shape the Digital World, and Who Gets Shaped by It?

*Published: 2025-06-08*
*Tags: Innovation, Product Management*

URL: https://www.ahmadkarmi.com/insights/the-new-divide-who-gets-to-shape-the-digital-world-and-who-gets-shaped-by-it

> As digital systems increasingly influence domains such as welfare distribution and cultural narratives, it becomes essential to scrutinise the asymmetries present in their design, deployment, and governance. This article analyses the shifting nature of digital inequality through the interdisciplinary perspectives of global development and technology anthropology.

Rethinking the Digital Divide

The concept of the “digital divide” has traditionally focused on disparities in access to digital technologies and the internet. While this framing remains relevant, it is no longer sufficient in an era characterized by artificial intelligence, algorithmic governance, and the global reach of digital platforms. The divide today is not only about who is online, but also about who possesses the power to shape the digital world, and who is passively shaped by it. As digital systems increasingly influence domains such as welfare distribution and cultural narratives, it becomes essential to scrutinize the asymmetries present in their design, deployment, and governance.

This article analyzes the shifting nature of digital inequality through the interdisciplinary perspectives of global development and technology anthropology. The central argument is that digital power remains concentrated among a limited set of geopolitical and corporate actors, often marginalizing the cultures, voices, and rights of those most affected by technological change. The discussion advocates for a reframing of digital development as a participatory, inclusive, and justice-oriented process.

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Architecture of Digital Power: Centralization and Control

Digital infrastructure and governance are not neutral phenomena. The ownership of cloud servers, the dominance of platform ecosystems, and the establishment of standards in artificial intelligence are highly concentrated in the Global North, particularly within United States and Chinese technology conglomerates. Amazon Web Services (AWS), Microsoft Azure, and Google Cloud collectively power the majority of global internet traffic. At the same time, Silicon Valley’s ideology of rapid innovation continues to shape the ethos of digital innovation.

This architecture of power is further reinforced by geopolitical control over internet infrastructure, including subsea cables, data centers, and satellite systems, as well as regulatory frameworks that privilege Western standards. The European Union’s General Data Protection Regulation (GDPR) serves as a global benchmark for privacy, but its norms are often exported globally without sufficient adaptation to local contexts.

Such centralization prompts several critical questions. Who defines ethical artificial intelligence? Whose values are embedded in content moderation algorithms? Who determines what data is collected, stored, and monetized, and who benefits from the resulting data economy? These questions highlight the need for greater transparency and inclusivity in the governance of digital systems.

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Global Subsea Cable Map from TeleGeography

https://www.submarinecablemap.com/

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Designed Elsewhere, Deployed Everywhere

Technological systems are frequently designed in one region and deployed in another with limited cultural translation or local oversight. This transnational design process often results in systems that do not align with the lived realities of the communities they affect.

For example, algorithmic systems in social welfare programs have been implemented in countries such as India to detect fraud or prioritize benefits. These systems often operate with opaque logic and can have adverse consequences for marginalized populations. Biometric systems have excluded citizens from accessing rations due to recognition errors, while algorithmic scoring has reinforced existing caste and class biases.

In several African countries, facial recognition systems imported from China or the West have demonstrated high error rates for dark-skinned individuals. This issue has been well documented but remains insufficiently addressed. Furthermore, the extraction of training data for artificial intelligence from populations in the Global South, frequently without informed consent or compensation, reflects historical patterns of resource exploitation.

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MIT Media Lab’s “Gender Shades” research

https://www.media.mit.edu/projects/gender-shades/overview/

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Epistemic Inequality: Who Defines Digital Knowledge?

Epistemic inequality, understood as the unequal recognition and amplification of different forms of knowledge, constitutes a subtle yet pervasive form of digital asymmetry. Global platforms and machine learning systems often marginalize non-Western languages, cultural expressions, and epistemologies.

Large language models such as GPT-4 are trained primarily on English-language data. As a result, these systems encode and replicate Anglo-centric worldviews, frequently omitting or misrepresenting indigenous knowledge systems, regional dialects, or context-specific histories. This dynamic affects not only representation but also the ways in which digital systems internalize and replicate human behavior.

The concept of Digital Humanism posits that technology should reflect the full spectrum of human diversity, not only in demographic terms, but also ontologically. This requires a reconsideration of what constitutes valid knowledge, whose perspectives are prioritized in design processes, and how digital tools can elevate rather than homogenize human culture.

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Participation or Tokenism? Inclusion in the Age of Platform Hegemony

Many technology companies and development agencies emphasize inclusion as a core value, yet in practice, this often amounts to token representation. Genuine participation requires co-design, shared governance, and the redistribution of power, rather than simply inviting communities to test pre-built tools.

The proliferation of “technology for good” initiatives often suffers from what anthropologist Lilly Irani terms “entrepreneurial citizenship.” Such programs frame marginalized populations as users, consumers, or micro-entrepreneurs, rather than as citizens with rights and agency. This framing can result in interventions that depoliticize structural issues and shift responsibility for change onto individuals.

Nevertheless, examples of participatory alternatives exist. In Brazil, the Portão Digital initiative engaged favela residents in co-creating civic technology solutions. In Kenya, local developers are building platforms in indigenous languages to preserve cultural heritage. These efforts demonstrate the potential of community-driven innovation when provided with appropriate resources and autonomy.

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Data Colonialism and the New Extract(ivism)

Data has often been described as the new oil, but a more accurate analogy may be colonial resource extraction. Data colonialism refers to the appropriation of individuals’ and communities’ data by powerful actors without adequate compensation, consent, or governance.

Applications designed for use in the Global South frequently include surveillance features or data-sharing defaults that would be unacceptable in the West. Development projects that collect biometric or behavioral data under the guise of aid or efficiency often replicate historical patterns of exploitation.

Moreover, the commodification of data for artificial intelligence training, ranging from medical images to social media content, generates value that seldom returns to the communities from which it originates. This asymmetry is not only economic but also epistemological: communities are studied, categorized, and optimized, but rarely invited to define their own digital futures.

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Toward Digital Development Justice

Bridging the new digital divide requires moving beyond access and inclusion to focus on justice. This shift entails several key actions:

Redesigning governance: Digital systems should be subject to democratic oversight, particularly in development contexts. Local communities must play a meaningful role in designing, implementing, and regulating digital interventions.

Supporting local capacity: Investments should be directed toward indigenous technology ecosystems that reflect local values, languages, and needs. Capacity-building should extend beyond technical training to include digital rights education, policy advocacy, and critical media literacy.

Decolonizing design: Critical design methodologies should be employed to surface underlying assumptions, challenge dominant paradigms, and invite pluralistic worldviews. Co-creation should become a standard practice.

Rebalancing epistemic power: Alternative knowledge systems must be promoted in data collection, artificial intelligence training, and content curation. This includes funding research in and by the Global South and supporting multilingual, multimodal platforms.

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Reimagining the Digital Future

The future of digital development should not be determined solely by market logic or geopolitical interests. Instead, it must be co-created by the communities it aims to serve. Digital systems are not neutral tools; they are socio-technical artifacts that embody power, politics, and possibility.

Society stands at a pivotal juncture. One trajectory leads to further extraction, surveillance, and inequality. The alternative offers an opportunity to reclaim digital space as a commons, a domain for shared knowledge, participatory governance, and cultural flourishing.

To achieve this, it is essential to prioritize justice in the design, governance, and deployment of digital systems. Ultimately, those who shape the digital world will determine who has the opportunity to thrive within it.


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## Why Product Management in the Middle East Needs Its Own Playbook

*Published: 2025-05-20*
*Tags: Product Management*

URL: https://www.ahmadkarmi.com/insights/why-product-management-in-the-middle-east-needs-its-own-playbook

You can’t copy Silicon Valley’s roadmap and expect it to work in Riyadh. Or Dubai. Or anywhere in the Middle East, really. And yet, that’s what most product managers try to do. They read the same blogs, follow the same X (Twitter) accounts, and adopt the same tools and frameworks that are built for Western markets. Then they wonder why things break. I’ve seen this first-hand: brilliant PMs frustrated by slow decision-making, features no one uses, and user behaviors that don’t fit the playbook. It’s not because they’re doing product management wrong. It’s because they’re applying the wrong rules to the wrong game.So let’s talk about what’s different here. What makes product management in the Middle East unique. And why understanding that difference is your biggest edge.

The Western Playbook Is Built on Assumptions

Most of the product advice you’ll find online is based on a few core assumptions:

Users give feedback easily

Teams ship fast and iterate

Data is available and reliable

Stakeholders trust the process

Customers are used to digital-first experiences

Now compare that to what a typical product manager faces in the Middle East:

Users prefer to call or WhatsApp instead of using your feedback form

Stakeholders want to review every screen before launch

Data pipelines are incomplete or inconsistent

Decisions are often top-down

Cash on delivery is still thing, believe it or not

The frameworks aren’t the problem. The context is.

Take something like “product discovery.” In the West, this means interviewing users, running tests, and building quick prototypes to validate assumptions. Here, it often means lobbying for approval from department heads, navigating political landmines, and convincing stakeholders that a problem is worth solving in the first place.

If you don’t recognize this, you’ll keep hitting walls. Not because you’re a bad PM, but because you’re using tools built for a different battlefield.

Users Behave Differently Here

User behavior in MENA markets doesn’t follow global patterns. And that matters — a lot.

For example, Arabic-speaking users often prefer browsing right-to-left. If your app doesn’t support that properly, it feels foreign, even broken. Many users don’t trust online payments and would rather pay cash when the product is in their hands. WhatsApp is the main channel for communication, not email.

Even the concept of “friction” is different. In Western markets, you’re taught to eliminate every extra step in a flow. Here, sometimes those steps signal trust. People want confirmation calls. They want to double-check details before committing.

Understanding these nuances helps you design products that fit, not just function.

Organizations Are Structured Differently

Let’s talk about internal culture.

A lot of companies in the Middle East are still founder-led or family-run. That creates some unique dynamics. Influence isn’t always based on title. Decision-makers may not be in your standups — they may be your CEO’s cousin, or the founder’s father-in-law.

This isn’t a criticism. It’s just reality. And if you ignore it, you’ll struggle.

Top-down decision-making means you need to spend more time aligning upfront. You can’t assume autonomy. You have to earn it by building trust, presenting clearly, and looping stakeholders in early.

And unlike product-led cultures where teams experiment freely, many Middle Eastern companies still operate in a risk-averse environment. Failure isn’t celebrated. It’s penalized. So you need to create psychological safety before you can even start testing new ideas.

Why Blindly Copying the West Backfires

I’ve seen this go wrong too many times.

A PM brings in a new prioritization framework. Maybe it’s RICE. Maybe it’s a weighted scoring model. It makes total sense…in theory. But the team doesn’t trust the scoring. The leadership doesn’t buy into the process. And suddenly the PM looks like they’re hiding behind a documents instead of solving real problems.

Or a product team sets up a self-serve analytics stack, inspired by what they saw on X (Twitter). But the data isn’t clean. The product doesn’t have enough users. And no one knows how to interpret the metrics anyway.

These things work in mature, data-rich, product-led environments. But in the Middle East, the playbook has to flex. It has to adapt to the constraints and realities on the ground.

What the Middle East Needs Is a Modified Playbook

So what should the playbook look like?

Start with ruthless clarity. You’re not trying to copy the West. You’re trying to build products that work here. That means:

Investing in context: Talk to users. Watch how they use your product. Understand their motivations. Translate more than just language — translate intent.

Prioritizing influence over process: Build trust with stakeholders. Pre-sell your ideas before the meeting. Get buy-in informally before you ask for approval formally.

Simplifying frameworks: Use RICE or OKRs if they help, but don’t worship them. Make them work for your org, not the other way around.

Designing for bilingual, mobile-first users: Arabic isn’t just a translation challenge. It’s a design, UX, and trust challenge. Solve for that.

Respecting the maturity curve: Your company may not be ready for cross-functional squads or product trios. Start small. Prove value. Earn scale.

This isn’t lowering the bar. It’s raising your situational awareness.

You’re Early — That’s a Good Thing

Here’s the upside: the product community in the Middle East is still growing. That means there’s room for leaders. If you understand the market, work within the culture, and still push for smart, user-centered decisions, you’ll stand out.

And you’ll build products that actually ship. That actually get used. That actually make money.

That’s the goal, right?


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## When Machines Paint: The Unfolding Debate Over AI-Generated Art

*Published: 2025-02-20*
*Tags: Art, Artificial Intelligence, Legal*

URL: https://www.ahmadkarmi.com/insights/when-machines-paint-the-unfolding-debate-over-ai-generated-art

In recent years, artificial intelligence has gained the uncanny ability and is perceived by many to produce artwork that rivals, replicates, or even eclipses the visual complexity of professional human-made efforts. While some hail this as a boundary-breaking leap for creativity, others view it as a direct threat to the livelihoods of artists and the sanctity of artistic expression. By blending algorithms with massive, often unlicensed image datasets, AI not only reshapes how we define art but also stirs contentious debates around ownership, ethics, and cultural authenticity.

The Emergence of AI Art: From Algorithmic Curiosity to Cultural Crisis

AI-generated art has ignited a complex debate about what it means to be creative in the digital age. Early experiments with Generative Adversarial Networks (GANs) produced abstract, uncanny works that intrigued collectors and casual onlookers alike. In 2018, the Parisian collective Obvious made headlines when their GAN-created Portrait of Edmond de Belamy sold for over $400,000 at Christie’s, proving that the art world was more than willing to pay a premium for what many initially dismissed as a novelty.

By 2022, however, the technology behind AI art shifted dramatically. Diffusion models like DALL·E 3, Midjourney, and Stable Diffusion supplanted earlier GANs, allowing users to render photorealistic images by typing simple text prompts. As these applications gained mainstream traction, they raised urgent questions about creativity, ownership, and authenticity. Much of this innovation relies on vast, unlicensed data sets – billions of images scraped from the internet without permission – sparking legal disputes, artist backlash, and broader ethical concerns. The upshot is an art world on the brink of significant transformation: either a new era of accessible, collaborative creation or an exploitative industry built on massive amounts of uncredited labor.

Framing the Debate

Is AI-generated art a liberating force that expands the boundaries of human expression, or is it a mechanism for large-scale, automated plagiarism? Several intertwined hypotheses help us parse these extremes:

Democratization vs. Exploitation

Democratization: Proponents note that anyone with internet access can now create visually impressive images, removing technical barriers and fostering creativity across different backgrounds.

Exploitation: Critics point to the widespread, unlicensed use of copyrighted works and styles that feed into AI systems. Professional artists often discover their distinctive aesthetics replicated, undercutting their livelihood and diluting the originality of the final outputs.

Fair Use vs. Industrial-Scale Copying

Fair Use Argument: Some claim that AI training mimics how human artists learn – through observation and inspiration – falling under legal notions of “fair use.”

Industrial-Scale Copying: Skeptics argue that the scale and specificity of AI scraping and replication go well beyond human capacity. When an AI model can churn out thousands of near-identical images in an artist’s style, it challenges the idea of it being mere “inspiration.”

Augmentation of Creative Boundaries vs. Devaluation of Skill

Augmentation: From auto-completing backgrounds to suggesting novel compositions, AI can reduce repetitive tasks, letting artists focus on conceptual depth. Some see this synergy as a catalyst for new art forms that blend human vision with machine efficiency.

Devaluation: Others assert that by automating skillful techniques – such as photorealistic rendering – AI may reduce artists’ perceived value. Freelancers report clients lowering budgets because “an AI can do it cheaper and faster.”

Further Readings: Top Takeaways from Order in the Andersen v. Stability AI Copyright Case.

Analyzing the Evidence: Data, Lawsuits, and Cultural Impact

A deeper look at the mechanics behind AI art platforms, combined with unfolding legal actions and cultural responses, underscores the far-reaching implications of algorithmic creativity.

Training Data Controversies

Massive Unlicensed Databases: Projects like LAION-5B and others scrape billions of image-text pairs from online sources, often with minimal filtering or permission. Stability AI’s Stable Diffusion reportedly trained on 12 million copyrighted Getty Images, prompting a high-profile lawsuit in 2023.

Style Cloning: Artists such as Greg Rutkowski discovered their names referenced in tens of thousands of prompts on platforms like Stable Diffusion. The model’s capacity to replicate signature styles reveals how AI can magnify plagiarism risks and diminish the economic viability of professional artists who rely on distinct aesthetics for their livelihood.

Legal Hurdles and Copyright Orphans

Getty vs. Stability AI: Getty Images accuses Stability AI of “brazen infringement,” alleging the company uses protected content for commercial gain without licensing or compensation. This and other cases focus on whether AI training is protected under “fair use” or falls under commercial-scale copying that surpasses acceptable boundaries.

Human Authorship Requirement: The U.S. Copyright Office has revoked copyright protection for AI-generated works, stating that machines cannot legally be considered “authors.” This puts AI-created content in a legal gray zone: while companies can profit from it, the artworks themselves do not enjoy standard copyright safeguards.

Social and Cultural Fallout

Freelancers and Market Pressures: Many freelance artists have seen a decline in commissions as clients opt for AI tools or drastically reduce their rates, believing that “if an algorithm can do the job, why pay a premium for human skill?”

Algorithmic Bias and Cultural Erosion: Most training datasets skew Western, creating images that default to Eurocentric perspectives. Non-Western cultures, traditions, and art forms risk erasure or misrepresentation when generative models lump them into simplistic stereotypes.

Platform Boycotts: After ArtStation introduced AI-based features, numerous artists criticized the platform for betraying its user base, highlighting the complex push-and-pull between innovation and ethical practice in online creative communities.

Further Readings: Class Action Lawsuit v. Stability AI, DeviantArt, Midjourney, Runway AI.

The Future: Ethical Collaboration or Creative Exploitation?

AI-generated art stands at a crossroads. Will it be celebrated as a boundary-pushing medium that democratizes expression, or will it intensify the exploitation of unsuspecting artists and cultural artifacts?

Regulatory Frameworks and Ethical Training

Opt-In Systems: Proposed EU legislation would mandate explicit consent for data scraping and training sets. This shift could reshape AI business models, pressuring companies to license images rather than scrape them.

Labeling and Transparency: Some experts propose requiring AI-generated works to carry digital watermarks or metadata that disclose the model, dataset, and sources used, aiming to clarify provenance and discourage appropriation.

Royalty Models and Fair Compensation

Artist Royalties: Innovators suggest a system where artists receive payment whenever their work, or a style derived from it, appears in AI training sets. This would mirror frameworks already used in music streaming.

Shared Revenue: Large AI companies might strike licensing deals similar to stock photography agencies, ensuring creators are compensated for their contributions.

Cultural Preservation vs. Technological Progress

Protecting Artistic Diversity: Tools like Glaze and Nightshade manipulate images to corrupt training data, giving artists a defensive mechanism against unauthorized scraping.

Collaborative Futures: Artists like Sougwen Chung program models using their own artworks, treating AI as a “co-creator” rather than a replacement. Such experiments illustrate how AI might enrich, rather than erode, creative practice if done with respect for intellectual property.

Further Readings: Exploring Conservation of Chinese Paintings with Generative Artificial Intelligence.

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Ultimately, AI’s capacity to produce staggering quantities of imagery in mere seconds is redefining art production, circulation, and consumption. In an era where “the machine can paint,” society faces the challenge of preserving the essence of human creativity – personal experiences, cultural heritage, and the emotional resonance that it seems only artists can provide. By establishing robust regulations, forging equitable licensing models, and embracing truly collaborative workflows, AI-generated art can evolve into a constructive phenomenon rather than an exploitative one.


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## The Dark Side of Big Tech: Are Your Smart Devices Always Listening?

*Published: 2025-02-19*
*Tags: Big Tech, Privacy*

URL: https://www.ahmadkarmi.com/insights/the-dark-side-of-big-tech-are-your-smart-devices-always-listening

> Unearth how smartphones, smart speakers, and other always-on devices may be collecting far more than just wake words. From misleading privacy settings to high-stakes data breaches; this detailed exploration exposes the surveillance risks lurking in everyday tech. Are we better off learning how to safeguard our conversations and push back against Big Tech's expanding data ecosystem, or are these concerns merely a series of coincidences and conspiracy theories?

## **The Rise of Always-On Technology**

Smartphones, smart speakers, and other internet-connected devices are more common than ever, managing online shopping, home security, and daily scheduling. Though these innovations offer convenience, they also raise serious questions about the extent to which our conversations and behaviors may be monitored, analyzed, and monetized. Major tech companies often assert that their devices only “listen” after detecting specific wake words like “Hey Google” or “Alexa,” but [**various research findings**](https://www.business-standard.com/technology/tech-news/is-your-phone-listening-marketing-firm-confirms-tech-behind-targeted-ads-124090400592_1.html) and whistleblower accounts indicate that devices may be capturing far more audio than users realize.

The notion of “always-on” consumer technology has grown more prevalent, particularly as voice assistants evolve in complexity. This persistent state of listening challenges the boundary between user-friendly features and [**corporate data exploitation**](https://www.komando.com/news/security/leak-big-tech-companies-are-listening-to-your-convos/). Does an always-on device improve life by anticipating your needs, or does it open an unprecedented window into your private world?

## **Hypothesis and Reasoning**

Given the breadth of user data gathered by smart devices, it is reasonable to hypothesize that technology companies leverage continuous audio monitoring to enhance their products and expand advertising revenue. From a purely technical standpoint, local edge computing helps speech recognition function in near real time. Yet if devices are always recording in the background, it stands to reason that the collected data could be used for a host of other purposes, including behavioral tracking and targeted advertising.

1. [**Company Motivations**](https://me.mashable.com/tech/46135/new-evidence-claims-google-microsoft-meta-and-amazon-could-be-listening-to-you-on-your-devices) – Advertisers pay a premium for consumer data that offers deeper insights than simple clickstream analysis. When voice snippets reveal personal preferences, concerns, or even emotional states, ad targeting becomes far more potent.

2. **User Assumptions** – Many individuals assume voice assistants turn on only when prompted. However, misactivations or ambiguous user agreements can result in recordings being made unintentionally.

3. **Corporate Transparency** – While companies claim “improving user experiences” as a primary goal, leaked documents and marketing firm collaborations hint at a secondary motive: harnessing ambient audio for commercial gain.

This hypothesis, centered on the interplay between convenience-driven technology and systematic data collection, sets the stage for examining how always-on audio devices function and how they might compromise user privacy.

## **Findings and Results**

A comprehensive look at current research and case studies reveals a pattern of blurred consent, security vulnerabilities, and regulatory gaps.

1. **Blurred Lines of Consent**

 – **Default Opt-Ins** – Platforms like Amazon Alexa often share user recordings for “quality control,” requiring manual opt-outs. Hidden clauses in terms of service documents routinely allow companies to collect and analyze large amounts of voice data.

 – **Dark Patterns** – Investigations show that disabling data harvesting can involve a labyrinth of menus and settings, leading to “consent fatigue.” For instance, a Which? investigation in 2024 found Samsung smart TVs required navigating [**12 separate steps**](https://www.which.co.uk/news/article/smart-device-brands-must-put-privacy-over-profits-at8Vq4t3VCn9) to restrict data collection.

2. **User Behavior and Motivations**

 – **Guardians** – Some users actively monitor device usage, muting microphones or unplugging devices for sensitive conversations.

 – **Pragmatists** – A significant majority of consumers rely on default settings, rarely altering privacy controls and thereby unwittingly providing broad permissions for data collection.

 – **Cynics** – Others refuse to adopt voice assistant features altogether, distrusting corporate data practices.

3. **Security Risks**

 – **Ultrasonic Trigger Injection** – High-frequency waves can activate voice assistants remotely, enabling malicious tasks like unauthorized purchases or unlocking doors.

 – **Skill Squatting** – Attackers can create deceptively named Alexa or Google Assistant skills to capture voice data.

 – **Government Espionage** – [**National security agencies**](https://www.independent.co.uk/tech/smart-speakers-uk-government-civil-service-warning-a9628121.html) warn about potential voice capture in classified environments, as illustrated by concerns over Huawei devices transmitting GPS and health data to foreign servers.

4. **Regulatory Shortcomings**

 – **FTC Settlement** – [**Amazon was fined 25 million dollars**](https://www.ftc.gov/news-events/news/press-releases/2023/05/ftc-doj-charge-amazon-violating-childrens-privacy-law-keeping-kids-alexa-voice-recordings-forever) in 2023 for improperly retaining children’s data, yet it retained the right to use that data to train AI models.

 – **GDPR Enforcement** – Enforcement of the European Union’s privacy regulations remains uneven, with some companies bypassing localization rules.

 – **Proposed Solutions** – California’s Voice Privacy Act (AB 1395) aims to ban non-consensual monetization of voice data, while privacy advocates push for mandatory firmware audits and treating voiceprints as biometric data.

These findings underscore how always-on devices expose users to continuous audio surveillance, shape advertising practices, and face weak regulations. The result is a delicate balance between convenience and privacy.

## **Concluding Thoughts**

Always-on consumer technology certainly has its advantages, from hands-free convenience to smart home automation. However, the trade-offs demand careful scrutiny. While companies insist that background monitoring is essential for quick, accurate responses, it also fuels a 195-billion-dollar targeted advertising industry. Evidence suggests that the personal audio data gathered goes well beyond mere “wake word” detection.

Moving forward, stricter regulatory frameworks, transparent corporate practices, and robust consumer protections will be necessary to maintain user autonomy. Until then, it remains wise for consumers to assume that their conversations may be subject to potential capture and monetization. By understanding the motivations behind continuous audio monitoring and advocating for greater corporate accountability, we can safeguard our privacy without sacrificing the benefits of modern technology.


---

## The Energy Cost of Artificial Intelligence

*Published: 2025-02-18*
*Tags: Artificial Intelligence*

URL: https://www.ahmadkarmi.com/insights/the-energy-cost-of-artificial-intelligence

> AI is revolutionizing industries, but its energy demands are soaring. Explore the impact of AI's power consumption, the challenges it poses to sustainability, and the innovations shaping a more energy-efficient future. Discover how policy, hardware advancements, and industry shifts are addressing AI’s environmental footprint.

## Introduction

Artificial intelligence is reshaping industries at an unprecedented pace, driving progress in areas like healthcare, climate modeling, and automation. However, the computational power required for AI comes at a high energy cost. Training and running AI models consume vast amounts of electricity, raising serious concerns about long-term sustainability. As data centers supporting AI workloads expand, their electricity consumption continues to climb, prompting urgent discussions about whether energy constraints will curb AI’s growth.

This article examines the rising energy demands of AI, explores strategies to improve efficiency, and analyzes how policy and industry shifts can help mitigate the environmental impact. With AI set to become even more integrated into global infrastructure, balancing its potential with energy sustainability is a challenge that cannot be ignored.

## The Growing Energy Demand of AI

![__wf_reserved_inherit](https://cdn.prod.website-files.com/66c8e6fb686e109dc7fb27df/67b4ff00ba43b4b57e8de075_GyBaRogSBDq4bcQjoGlLeK6zZV39ZP10JvAK-bOsvNk.png)

*Source: World Economic Forum*

The power needed to train and deploy AI models has surged in recent years. Training GPT-3 required around 1,300 megawatt-hours (MWh) of electricity, roughly the annual usage of 130 U.S. households. GPT-4’s training reportedly consumed 50 times more energy. Inference, or the process of responding to user queries, adds another layer of consumption. [**A single ChatGPT request uses ten times the energy of a Google search**](https://www.weforum.org/stories/2024/07/generative-ai-energy-emissions/), and with over 180 million users per month, the cumulative energy impact is significant.

AI-driven data centers currently account for about 4% of U.S. electricity consumption, a number expected to double by 2030. Globally, AI-related power use is projected to reach 880 terawatt-hours (TWh) by the decade’s end. Companies like Microsoft and Google have reported emissions increases of 30% and 50%, respectively, from 2020 to 2024, primarily due to expanding AI operations. If left unchecked, [**AI could consume 0.5% of global electricity by 2027**](https://www.bruegel.org/comment/artificial-intelligence-and-energy-consumption), matching the power usage of a mid-sized country.

## The Challenge of High-Powered GPUs

AI’s enormous energy consumption stems largely from its reliance on high-performance graphics processing units (GPUs). Nvidia’s H100 GPU, a leading AI accelerator, consumes approximately 700 watts which is comparable to powering two U.S. households. With thousands of these GPUs deployed each month, the power demand continues to grow.

While each new AI chip generation enhances computational speed, power efficiency has not kept pace. Nvidia’s latest Blackwell architecture is 30 times faster in inference tasks but consumes 25 times more power than previous versions. By 2030, the number of AI-optimized GPUs in operation is expected to exceed 100 million, significantly impacting electricity demand unless more energy-efficient solutions are developed.

## AI’s Environmental Footprint and Grid Pressure

![__wf_reserved_inherit](https://cdn.prod.website-files.com/66c8e6fb686e109dc7fb27df/67b50239686f3d3f8f8cbd50_GSdata.jpg)

*Source: Goldman Sachs*

Beyond raw energy use, AI-driven data centers contribute significantly to global carbon emissions. By 2030, these centers could account for 2.6% of total CO₂ emissions, with an estimated social cost of $125 to $140 billion. The concentration of AI infrastructure in certain regions poses an additional challenge: Northern Virginia, home to the world’s largest data center hub, is projected to allocate up to 50% of its total grid capacity to data centers by 2030. Similarly, Ireland’s national grid could see AI data centers consuming 30% of its electricity by 2026, creating risks of power shortages.

The International Energy Agency (IEA) estimates that global data center electricity use will double to 857 TWh by 2028, with AI responsible for nearly 20% of that demand. Schneider Electric’s energy models range from a best-case scenario of 785 TWh by 2035 to a crisis scenario in which unchecked growth causes grid failures.

## Innovations in AI Energy Efficiency

Despite these challenges, several emerging technologies offer potential solutions for improving AI’s energy efficiency:

### Hardware Optimization Strategies

– **Power Capping and Dynamic Allocation:** [**MIT researchers**](https://www.ll.mit.edu/news/ai-models-are-devouring-energy-tools-reduce-consumption-are-here-if-data-centers-will-adopt) demonstrated that limiting GPU power to 150 watts can reduce energy consumption by up to 15%, with only a minor increase in training time.

– **Custom AI Chips:** Processors such as Microsoft’s Maia 100 GPU are designed to maximize performance per watt, reducing energy waste in AI workloads.

– **Advanced Cooling Systems:** Direct liquid cooling and immersion cooling technologies can lower cooling-related energy use from 40% to just 10% of total data center consumption.

### Software and Model Optimization

– **Model Pruning and Quantization:** Techniques that streamline AI models by removing redundant computations can cut inference energy use by 40% without reducing accuracy.

– **Smaller, Task-Specific Models:** AI models tailored for specific tasks, like Microsoft’s Phi-3, are more energy-efficient than general-purpose large language models.

– **AI-Driven Energy Management:** Google DeepMind’s AI-powered cooling optimization system has reduced data center cooling costs by 40%, showcasing AI’s potential to improve its own sustainability.

## Policy and Industry Shifts Toward Sustainability

As AI’s environmental impact grows, governments and corporations are beginning to take action. The European Union’s AI Act, finalized in late 2023, requires transparency in AI energy consumption but does not yet mandate efficiency standards. Germany and the Netherlands have introduced stricter regulations on data center power density to encourage energy-conscious infrastructure.

Industry leaders are also responding:

– **Commitment to Carbon-Free AI:** Google, Microsoft, and Amazon Web Services have pledged to power their AI operations with 24/7 carbon-free energy by 2030, leveraging power purchase agreements (PPAs) to secure renewable sources.

– **Challenges in Renewable Integration:** While solar and wind power are viable energy alternatives, their intermittent nature makes it difficult to sustain AI workloads without backup power solutions.

– **Nuclear Energy as a Solution:** Small modular reactors (SMRs) and microreactors are being explored as [**sustainable power options for AI data centers.**](https://epoch.ai/blog/can-ai-scaling-continue-through-2030) Microsoft’s recent 960 MW nuclear power contract in Pennsylvania reflects growing industry interest in nuclear energy as a stable, carbon-free power source.

## The Future of AI Energy Sustainability

[**Gartner predicts**](https://www.gartner.com/en/newsroom/press-releases/2024-11-12-gartner-predicts-power-shortages-will-restrict-40-percent-of-ai-data-centers-by-20270) that by 2027, 40% of AI data centers will experience power shortages, forcing the industry to prioritize energy efficiency. Some experts argue that energy limitations could become AI’s first major constraint, mirroring past semiconductor industry challenges with Moore’s Law.

Three key factors will shape AI’s energy trajectory:

1. **Stronger Policy Regulations:** Governments must enforce energy transparency and accelerate the deployment of renewable energy infrastructure.

2. **Industry Collaboration:** The adoption of open-source efficiency tools, like [**Red Hat’s Climatik**](https://www.phoronix.com/news/Red-Hat-Climatik), can help optimize power use across AI workloads.

3. **Investment in Next-Gen Energy Solutions:** Advances in nuclear power, energy storage, and grid modernization will be crucial to meeting AI’s growing energy demands sustainably.

While energy constraints present a formidable challenge, they do not have to limit AI’s expansion. Just as semiconductor advancements prolonged Moore’s Law, continued innovation, policy support, and infrastructure investment can ensure AI remains scalable and sustainable.

## Conclusion

AI’s rapid expansion has sparked a necessary discussion about energy sustainability. Without major efficiency improvements, AI could place severe strain on power grids and accelerate carbon emissions. However, through strategic investment in technology, regulatory policies, and infrastructure, AI can continue its trajectory while minimizing its environmental impact. The next decade will determine whether AI can balance its transformative potential with responsible energy consumption.


---

## Quantum Computing and AI: The Next Frontier

*Published: 2025-02-18*
*Tags: Artificial Intelligence*

URL: https://www.ahmadkarmi.com/insights/quantum-computing-and-ai-the-next-frontier

> Today, I explore how quantum computing and artificial intelligence transform industries like finance, healthcare, and cybersecurity. Learn about the latest advancements in quantum error correction, hybrid computing models, and real-world applications of quantum algorithms. Stay ahead with expert insights into the challenges, ethical considerations, and the roadmap to achieving quantum advantage. Read now to explore how quantum AI is redefining the technological landscape.

## Introduction

The convergence of quantum computing and artificial intelligence is shaping the future of technology, unlocking unprecedented computational potential. Quantum computing utilizes fundamental principles of quantum mechanics, including superposition, entanglement, and quantum interference, to process information in ways that classical computers cannot replicate. The integration of AI with quantum computing opens the door to solving problems once considered impossible, from molecular modeling to large-scale optimization challenges.

By 2025, advancements in quantum error correction, hybrid quantum-classical computing, and quantum algorithms will drive real-world applications. Industries such as finance, healthcare, materials science, and cybersecurity are among the first to experience transformation. However, challenges related to qubit stability, ethical considerations, and quantum-resistant cryptographic frameworks remain obstacles. As investment in quantum technology grows and collaborations between academia and industry expand, quantum computing is moving from theoretical research to practical implementation.

## Foundations of Quantum Computing

Classical computers store data as bits, which can represent either 0 or 1. Quantum computers use qubits, which exist in a superposition of states, allowing them to perform multiple calculations simultaneously. Quantum entanglement further enhances computational efficiency by linking qubits, enabling instantaneous data transfer across vast distances.

These principles allow quantum computers to process information exponentially faster than classical systems. [Shor’s algorithm](https://en.wikipedia.org/wiki/Shor%27s_algorithm), for example, can factor large numbers efficiently, posing a potential challenge to existing encryption methods. [Grover’s algorithm](https://en.wikipedia.org/wiki/Grover%27s_algorithm) speeds up unstructured search tasks. These capabilities are no longer confined to theory, with real-world experiments validating the feasibility of quantum advantage.

## Addressing Qubit Stability and Error Correction

One of the biggest obstacles in quantum computing is maintaining qubit coherence. Qubits are highly sensitive to external influences such as temperature fluctuations and electromagnetic interference, which can lead to computational errors.

To address this, researchers have developed quantum error correction techniques such as surface codes, which distribute quantum information across multiple qubits to detect and fix errors. [IBM’s recent work on the Condor](https://www.wevolver.com/article/breakthroughs-in-quantum-computing) processor, which features 1,121 superconducting qubits, marks significant progress in scaling quantum systems while maintaining error rates at manageable levels. Innovations in trapped-ion qubits and photonic networks are also enhancing coherence times, making fault-tolerant quantum computing increasingly viable.

## Scaling Quantum Computing Through Modular and Networked Systems

Scalability is a critical challenge for quantum computing. Increasing qubit density within a single processor has engineering limitations, leading to a shift toward modular and networked quantum computing. In this model, multiple quantum processors are interconnected via quantum teleportation, forming a distributed system that mirrors classical supercomputing architectures.

Oxford University’s recent demonstration of distributed quantum computing illustrates this approach. By linking trapped-ion qubit modules through photonic channels, researchers successfully executed quantum algorithms across a network. This architecture improves fault tolerance by localizing errors within smaller processing units while also enabling greater computational power without increasing individual processor size.

## Hybrid Quantum-Classical Systems: Bridging the Gap

Since full-scale quantum computing is still developing, hybrid quantum-classical systems offer a practical bridge. These architectures integrate quantum processing units (QPUs) with classical CPUs and GPUs, allowing quantum computing to handle specialized tasks like combinatorial optimization while relying on classical systems for pre- and post-processing.

D-Wave’s quantum annealers have been implemented in hybrid environments for supply chain and logistics optimization, often outperforming classical solutions. Similarly, IBM’s Quantum Development Kit and Microsoft’s Azure Quantum provide frameworks for designing hybrid quantum-classical algorithms, facilitating integration between both computing paradigms.

## The Intersection of Quantum Computing and AI

The fusion of quantum computing and AI introduces revolutionary capabilities. Quantum machine learning (QML) algorithms, such as quantum neural networks, use quantum superposition and entanglement to process vast datasets at unparalleled speeds. Quantum principal component analysis (QPCA) identifies patterns in high-dimensional datasets, benefiting areas such as genomics, financial modeling, and materials science.

A breakthrough in generative quantum AI (Gen QAI) by Quantinuum in 2025 demonstrated how quantum-generated data can enhance predictive accuracy in drug discovery. Quantum-enhanced optimization algorithms are also revolutionizing financial risk assessment, enabling real-time decision-making with greater precision.

AI is also playing a role in advancing quantum computing. Machine learning-driven quantum error mitigation techniques are improving qubit stability and optimizing quantum algorithms. Google Quantum AI’s 2024 research demonstrated how AI-assisted parameter optimization significantly reduces computational error rates, making quantum systems more practical.

## Real-World Applications of Quantum AI

1. **Pharmaceutical and Materials Science** – Quantum simulations are enabling atomic-level modeling of molecular interactions, accelerating drug discovery and material engineering. [The Technology Innovation Institute (TII) in Abu Dhabi](https://www.tii.ae/quantum) has leveraged quantum simulations to drive progress in photovoltaic materials and carbon capture technologies.

2. **Logistics and Energy Optimization** – Quantum computing is being used to evaluate vast logistical possibilities, reducing inefficiencies in transportation networks. In the energy sector, quantum algorithms are optimizing power grid distribution, improving the integration of renewable energy sources.

3. **Cybersecurity and Quantum-Resistant Cryptography** – The rise of quantum computing presents a challenge to traditional encryption systems. Shor’s algorithm could compromise RSA encryption in a matter of hours. To counteract this risk, organizations are developing post-quantum cryptographic methods, such as lattice-based encryption. The National Institute of Standards and Technology (NIST) is set to finalize post-quantum cryptographic protocols by 2026.

## Ethical and Technical Considerations

Despite its immense potential, quantum computing also presents ethical and technical challenges. The ability to decrypt traditional encryption methods raises security concerns, necessitating a transition to quantum-resistant cryptographic standards. Additionally, superconducting quantum processors require extensive cryogenic cooling, contributing to high energy consumption. Research into photonic qubits and room-temperature superconductors is underway to address sustainability concerns, but scaling quantum computing infrastructure will require significant investment.

## The Future of Quantum Computing and AI

Experts predict that quantum advantage, the point at which quantum computers consistently outperform classical supercomputers; will be demonstrated by 2025. [Google’s Willow chip](https://www.idtechex.com/en/research-article/quantum-breakthroughs-of-2024-beyond-the-buzz-around-google/32326) recently completed a computation in five minutes that would take classical supercomputers an estimated 10 septillion years, marking a significant milestone in quantum supremacy. However, widespread practical applications will require continued advancements in hybrid models and fault-tolerant quantum architectures.

Building a skilled workforce is essential for the future of quantum computing. Universities and corporations are investing in interdisciplinary training programs, while platforms like IBM Quantum Network and Azure Quantum are making quantum resources more accessible to researchers and startups.

## Conclusion

Quantum computing and AI are driving a profound shift in computational capabilities. From revolutionizing drug discovery to optimizing global infrastructure, these technologies are set to redefine entire industries. While challenges related to scalability, error correction, and ethical concerns remain, the rapid pace of innovation suggests that quantum-powered solutions will soon become mainstream. As governments, businesses, and researchers collaborate to navigate this emerging landscape, the transition from quantum potential to quantum reality is well underway.

‍


---

## The Art of Crafting Exceptional Product Experiences

*Published: 2024-11-26*
*Tags: Product Management*

URL: https://www.ahmadkarmi.com/insights/the-art-of-crafting-exceptional-product-experiences

> Discover methods for deep user understanding and delightful product design. Join us as we explore innovative approaches to product design that lead to lifelong user engagement and satisfaction.

What’s the difference between a product that users tolerate and one they can’t live without? In a world where 80% of apps are deleted after just one use, creating exceptional product experiences isn’t just good practice—it’s survival. Let’s dive into the art and science of crafting product experiences that truly resonate with users and stand the test of time.

Imagine a product so intuitive, so delightful, that users can’t help but share it with others. The iPhone’s revolutionary touchscreen interface was one such example. It didn’t just change how we use phones; it changed how we interact with technology altogether. This is the power of exceptional product experiences.

## Understanding Your Users: The Foundation of Great Experiences

At the heart of every exceptional product experience lies a deep understanding of the user. This isn’t just about knowing demographics or usage statistics; it’s about truly empathizing with your users’ needs, desires, and pain points.

### Methods for Deep User Understanding:

– **In-depth interviews**: Go beyond surface-level questions. Ask “why” repeatedly to uncover underlying motivations and needs.

– **Contextual inquiry**: Observe users in their natural environment. You’ll be surprised by the insights you gain when you see your product in action.

– **Data analysis**: Look for patterns in user behavior. What are they doing? More importantly, what aren’t they doing?

– **Empathy mapping**: Put yourself in your users’ shoes. What are they thinking, feeling, seeing, and hearing as they interact with your product?

Remember, the goal isn’t just to collect data, but to develop genuine empathy for your users. This empathy will guide every decision you make in the product development process.

## Designing for Delight: Beyond Usability

While usability is crucial, truly exceptional product experiences go beyond mere functionality. They surprise and delight users in unexpected ways.

### Elements of Delightful Design:

– **Intuitive navigation**: Make your product feel like an extension of the user’s thoughts. The best interfaces are those that users don’t even notice.

– **Microinteractions**: Small, delightful details can make a big impact. Think of the satisfying “swoosh” sound when sending an email.

– **Personalization**: Use data responsibly to create experiences that feel tailored to each user.

– **Emotional design**: Consider the emotional state of your users. How can your product make them feel competent, in control, or even joyful?

Remember, great design isn’t about showing off your creativity. It’s about solving user problems in elegant, often invisible ways.

## Mapping the User Journey: A Holistic Approach

Exceptional product experiences don’t happen in isolation. They’re the result of a carefully crafted user journey that considers every touchpoint.

### Steps to Mapping an Effective User Journey:

1. **Identify key personas**: Who are your primary users? What are their goals and pain points?

2. **Map out touch-points**: List every interaction a user has with your product, from first awareness to long-term usage.

3. **Identify moments of truth**: Which interactions have the biggest impact on user satisfaction?

4. **Design for continuity**: Ensure a smooth, consistent experience across all touchpoints. Make sure data and experiences continue and pass through each device the user experiences your product on.

Think of your product experience as a story. Every touchpoint is a chapter, and your job is to make sure the narrative flows seamlessly from beginning to end.

## Building with Precision: The Devil’s in the Details

Creating exceptional experiences requires meticulous attention to detail. Every pixel, every interaction, every line of copy matters.

### Tips for Precision in Product Building:

– **Optimize for speed**: In today’s world, speed is a feature. Shave off milliseconds wherever you can.

– **Ensure cross-platform consistency**: Users expect a seamless and consistent experience whether they’re on desktop, mobile, or tablet. Make sure everything from your flows to your branding match user expectations across all devices.

– **Write clear, concise copy**: Your words are part of the user experience. Make them count.

– **Anticipate edge cases**: What could go wrong? Plan for it before it happens.

Remember, users notice the little things, even if only subconsciously. It’s often these small details that separate good products from great ones.

## Iterating Towards Excellence: The Never-Ending Journey

Creating exceptional product experiences isn’t a one-time effort. It’s an ongoing process of iteration and improvement.

### Strategies for Continuous Improvement:

– **Regular user testing**: Don’t wait until launch to get user feedback. Test early and often.

– **A/B testing**: Let data guide your decisions. Test different versions to see what resonates best with users.

– **Analyze user behavior**: Use analytics to understand how users are actually interacting with your product.

– **Stay curious**: The tech landscape is always changing. Keep learning and experimenting with new approaches.

Remember, there’s no such thing as a “finished” product. There’s always room for improvement, always a way to make the experience a little bit better.

## Conclusion: The Art of the Possible

Crafting exceptional product experiences is both an art and a science. It requires deep empathy, creative problem-solving, meticulous attention to detail, and a commitment to continuous improvement.

But when done right, the results can be transformative. A truly exceptional product experience doesn’t just solve a problem or fulfill a need. It creates a connection with the user, evokes positive emotions, and becomes an integral part of their life.

From Silicon Valley startups to Fortune 500 companies, the principles of exceptional product experiences remain the same. Whether you’re working on the next big social media platform or revolutionizing healthcare technology, these strategies can help you create products that users love and competitors envy.

As product managers, we have the privilege and responsibility of shaping these experiences. So let’s embrace the challenge. Let’s push the boundaries of what’s possible. Let’s create product experiences that don’t just meet expectations, but exceed them in ways users never even imagined.

After all, that’s the true art of product management. As one product leader I’ve previously worked with aptly put it, “Great product experiences are like magic tricks – they delight users in ways they never expected, yet feel completely natural once experienced.”


---

## How to Manage Product Scope and Feature Creep

*Published: 2024-09-27*
*Tags: Product Management*

URL: https://www.ahmadkarmi.com/insights/how-to-manage-product-scope-and-feature-creep

> Learn how to manage product scope and prevent feature creep with proven frameworks like MoSCoW and Agile. Discover actionable strategies to keep your product on track, deliver value, and avoid unnecessary complexity.

Product management can often feel like you’re walking a tightrope. On one side, you have the need to deliver value to users by continuously improving the product. On the other, you have the limitations of time, budget, and resources that tether you to reality. One of the biggest challenges in balancing this act is managing product scope while avoiding feature creep.

Feature creep has a sneaky way of derailing even the best-laid plans. It starts with a seemingly innocent idea—just one more feature, a small enhancement—and before you know it, your sleek, focused product becomes bloated with unnecessary bells and whistles. This not only impacts the development timeline and costs but often dilutes the very essence of what your product was supposed to be.

## What Are Product Scope and Feature Creep?

At its core, product scope is about defining the boundaries of what your product will do, including its features, functionalities, and goals. It’s a mutual agreement between the product team and stakeholders about what’s being built and what success looks like. Without a well-defined scope, you’re essentially building in the dark.

Feature creep, on the other hand, is the unplanned expansion of the product scope. It’s what happens when additional features start piling up, usually at the request of well-meaning stakeholders, eager customers, or even overzealous product teams. The problem with feature creep is that it tends to dilute the core value proposition of the product while increasing costs, complexity, and time to market.

## Why Feature Creep Happens

Feature creep often has noble beginnings. Everyone wants the product to be better, right? But as you layer on more features, complexity rises, and you risk losing focus. Here are some common reasons feature creep occurs:

1. **Unclear Requirements:** When the product’s goals aren’t clearly articulated from the outset, teams tend to add features on the fly.

 – **Example:** Your team is building a project management app. One day, someone suggests adding a time-tracking tool because, “It would be useful for some users.” While it sounds harmless, this new feature requires design adjustments, backend support, and testing. Before long, your tight project becomes unwieldy.

2. **Customer Feedback Overload:** Feedback is a gift, but it can quickly become a burden if every request is treated as gospel.

 – **Example:** A SaaS company receives a flood of customer requests for new integrations. Instead of prioritizing and evaluating the impact, the team begins adding them one by one. Six months later, they have an overcomplicated system that’s become difficult to maintain.

3. **Internal Pressures:** Teams often feel pressure to keep up with competitors or to satisfy internal stakeholders, especially when they’re pushing for features that align with their departmental goals.

 – **Example:** Sales pushes hard for a feature because it might close a big deal, but it’s not aligned with the long-term product vision. The feature gets added anyway, but in the long run, it creates technical debt and slows down future development.

## Proven Frameworks for Managing Product Scope

Fortunately, product managers don’t need to fly blind when it comes to managing scope and avoiding feature creep. There are several tried-and-true frameworks that can help keep things on track.

## MoSCoW Prioritization

One of the simplest yet most effective frameworks for managing product scope is MoSCoW. This prioritization technique categorizes features into four buckets:

– **Must-have:** These are the non-negotiables. The product can’t ship without them.

– **Should-have:** Important features that add significant value but aren’t critical to the initial release.

– **Could-have:** Nice-to-have features that aren’t crucial and can be added later if time and resources allow.

– **Won’t-have:** Features that won’t be included in this iteration but might be revisited in the future.

**Example in Action:** Imagine you’re building a fitness tracking app. Your “must-haves” include basic features like step counting and calorie tracking. “Should-haves” might include social sharing or workout recommendations. “Could-haves” might be fun add-ons like gamification or premium challenges. By following MoSCoW, you ensure the core functionality gets built before any less critical features start to creep in.

## Agile Backlog Management

If you’re working in an Agile environment, you already know that the product backlog is your best friend when it comes to managing scope. The backlog is a dynamic, prioritized list of features, bug fixes, and improvements that need to be addressed. The beauty of Agile is that it allows for iterative development, meaning you can build, test, and refine in cycles.

**Example in Action:** Your team is working on a new e-commerce platform. You begin with a basic MVP: browsing, adding items to a cart, and checkout functionality. As you move through development, stakeholders request additional features, like personalized recommendations and wish lists. Instead of immediately adding them, you place them in the backlog. They get prioritized according to their business value, which ensures that the most important features are built first.

## Lean Development and MVP

Lean development teaches us to focus on the Minimum Viable Product (MVP)—the simplest, most essential version of your product that can be released to users. The goal here is to deliver value quickly while keeping costs and complexity to a minimum.

**Example in Action:** Let’s say you’re building a ride-sharing app. The MVP might include basic functionality like finding a ride, GPS tracking, and payment options. Features like driver ratings, route optimization, or user rewards can be added later once the core product has been validated.

By focusing on the MVP, product managers can avoid the temptation to add unnecessary features early on. You ship a simple product, gather feedback, and then iterate. This approach not only keeps feature creep at bay but also helps you validate whether the product is solving real customer problems.

## Practical Techniques to Control Feature Creep

While frameworks provide structure, the day-to-day job of managing feature creep often comes down to hands-on techniques and communication. Here’s how you can tackle feature creep head-on.

## Set Clear Boundaries and Expectations

A strong product vision should act as your north star. It’s your job as the product manager to communicate this vision clearly and consistently, especially to stakeholders. When new feature requests come in, you need to evaluate whether they align with the vision. If they don’t, it’s your responsibility to push back.

**Example in Action:** A marketing team asks for an in-app feature that allows users to participate in flash sales. While it could be useful, it doesn’t align with the app’s core purpose of helping users manage their subscriptions. You can politely but firmly explain that it’s not a priority for this release and offer to revisit it later.

## Implement a Change Control Process

Change requests are inevitable, but how you handle them will determine whether they contribute to scope creep. Having a change control process in place means that each request is evaluated based on its impact on the product, timeline, and resources.

**Example in Action:** A new request comes in to add a voice-to-text feature to your mobile app. Before accepting it, you evaluate how much work it will take, how it fits into the current sprint, and what features might have to be sacrificed to accommodate it. You present your findings to stakeholders, giving them the power to make an informed decision.

## Keep a Lean Backlog

An overstuffed backlog can quickly become overwhelming, making it difficult to focus on high-priority items. Regularly groom your backlog to remove low-value or irrelevant features that don’t contribute to your product’s goals.

**Example in Action:** During a backlog grooming session, you identify several low-priority features that have been sitting untouched for months. Rather than keep them around “just in case,” you archive them. This keeps the team focused on the tasks that truly matter.

## Balancing Flexibility and Focus

Now, here’s the tricky part: while you want to prevent feature creep, you don’t want to be inflexible. Product development isn’t static, and sometimes new opportunities arise that can significantly improve your product. The key is knowing when to say yes and when to hold the line.

Controlled Flexibility is the art of embracing change without losing control of the product vision or timeline. It’s about evaluating each new request, understanding its potential impact, and then making a deliberate choice to accept or reject it. Remember, every feature you add should enhance the product’s core value, not detract from it.

## Conclusion

Managing product scope and feature creep is part science, part art. It requires strong communication, the use of frameworks like MoSCoW or Agile, and practical tools like backlog management and change control. Above all, it requires the ability to stay focused on delivering value while remaining adaptable to real-time changes.

Feature creep will always be a challenge, but with the right mindset and processes in place, product managers can deliver high-quality products that meet customer needs without sacrificing timelines or budget. So, the next time you’re faced with that “just one more feature” request, you’ll be ready to manage it like a pro.

‍


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## The RICE Framework: A Valuable Tool, But Not the Whole Picture

*Published: 2024-09-09*
*Tags: Product Management*

URL: https://www.ahmadkarmi.com/insights/the-rice-framework-a-valuable-tool-but-not-the-whole-picture

> In product management, RICE helps prioritize projects, but my experience has shown that it's best used as a guideline, not a strict rule. Over-reliance limits creativity and strategic decisions.

In my years working as a product manager, I’ve encountered a common struggle: how do you decide which projects to prioritize when resources are limited and everyone seems to have a different idea about what’s most important? Like many, I’ve used various prioritization frameworks. One that is quite popular, and for good reason, is the RICE—Reach, Impact, Confidence, and Effort framework. 

While I cannot deny that it is a helpful tool, my experience has taught me that frameworks (like RICE, but not just limited to RICE) should be treated more like guidelines, not hard-and-fast rules. I’ve learned that relying too heavily on a framework can limit creative thinking, strategic decision-making and can be at times counter-productive. Just bare with me now, I’ll get to my point soon enough.

What is the RICE Framework?

For those unfamiliar, the RICE framework is a method that helps product teams evaluate and rank projects based on four key criteria:

Reach: This factor asks, “How many users will this feature or change affect?” The more people impacted, the higher the Reach score. For example, a new feature that reaches 50,000 users has a higher Reach than one that affects 500.

‍Impact: Impact measures how significant the effect of the change will be on those users. Is it a minor improvement, or will it radically change how they interact with the product? While it’s subjective, teams often classify impact on a scale, such as minimal, moderate, or major.

‍Confidence: Confidence refers to how sure you are that the project will deliver the expected impact. If the team has solid data to back up their claims, the confidence score will be high. If the project is more speculative, the confidence might be lower.

‍Effort: Effort is the measure of how much time and resources are required to complete the project. The more resources involved, the higher the Effort score, which lowers the overall priority of the project relative to its benefits.

By multiplying these factors together (with Impact and Reach in the numerator, and Effort in the denominator), you get a score for each project that helps decide which should be prioritized.

Why the RICE Framework is Important

I’ve used the RICE framework in several contexts, and it certainly has its advantages. It’s particularly useful when teams are overwhelmed with ideas and need a structured way to evaluate them.

Clarity in Decision-Making: The main strength of RICE is that it provides a clear and systematic way to make decisions. When faced with multiple projects, it’s easy to feel paralyzed. The framework helps to quantify factors that are otherwise hard to compare directly—like Reach and Impact versus the Effort required to implement a project. This clarity is especially valuable in fast-paced environments where teams must make decisions quickly.‍

Objective and Data-Driven: One of the reasons I initially gravitated towards the RICE framework is its objectivity. Rather than relying purely on intuition or external pressures from stakeholders, it encourages teams to focus on data-driven factors like how many users a feature will reach or how confident the team is in its success. This can help eliminate bias and ensure that resources are allocated more effectively.‍

Useful for Short-Term Prioritization: In my experience, RICE works well when you need to make quick, tactical decisions. For example, when managing a product backlog, RICE can help sort through smaller features, bug fixes, or improvements to determine which will provide the most value in the shortest amount of time.

Where the RICE Framework Falls Short

While I’ve found the RICE framework useful in many situations, I’ve also learned its limitations through years of experience. It’s a great tool when used properly, but over-reliance on RICE—or any framework—can hinder long-term product success. Here’s why.

Oversimplifying Complex Decisions: Product development is rarely as simple as assigning a number to each feature and moving forward based on the highest score. The RICE framework reduces complex, multifaceted decisions into a single metric, which can lead to an oversimplification of what’s really at stake. For example, how do you truly measure the long-term “impact” of an innovative but risky feature before it’s built? The framework relies heavily on guesswork, and that can sometimes skew decisions toward safer, lower-impact projects that don’t necessarily advance the product in meaningful ways.‍

Subjectivity in Scoring: One of the common misconceptions about the RICE framework is that it eliminates subjectivity. In reality, the scores assigned to each factor are often influenced by personal opinions or incomplete data. Teams may differ in how they rate “Impact” or “Confidence,” and this can lead to inconsistent results. I’ve seen situations where two equally valid projects were scored very differently simply because of how individual team members interpreted these factors.‍

Favoring Short-Term Wins Over Long-Term Vision: RICE tends to reward projects that offer immediate benefits with minimal effort, which can be useful for short-term planning. However, this focus on efficiency can sometimes come at the cost of long-term innovation. In my experience, breakthrough features—those that really move the needle for the business—often require significant effort and may not score highly in the RICE model. If teams rely too heavily on frameworks like RICE, they risk neglecting bigger, bolder ideas in favor of incremental changes that provide quicker wins but don’t drive long-term growth.‍

Misalignment with Strategic Goals: Perhaps the biggest flaw I’ve noticed when using RICE is that it can pull teams away from their broader strategic vision. A project might score well based on Reach or Impact, but if it doesn’t align with the overall direction of the company or product, it might not be worth pursuing. This is why I’ve learned not to let frameworks dictate strategy. While they offer valuable insights, they shouldn’t override the product’s core mission or vision.

How to Use RICE Effectively: A Balanced Approach

After years of using the RICE framework (amongst many others), I’ve learned that it works best when applied thoughtfully and flexibly. 

Here’s how I recommend using it:

Use RICE for Tactical Decisions: The RICE framework excels when dealing with tactical, short-term prioritization—like deciding which bugs to fix or which small features to roll out next. It helps teams focus on the highest-impact, lowest-effort projects, ensuring resources are used efficiently.

Avoid Using RICE for Strategic Initiatives: For larger, more visionary projects, it’s better to rely on a well-defined product strategy rather than a prioritization framework. Long-term initiatives often don’t score well in RICE because they require higher effort and involve more uncertainty. However, these projects are often the ones that create lasting value for the product.

Use Frameworks as Guidelines, Not Rules: This is the most important lesson I’ve learned over the years. Frameworks like RICE are valuable tools, but they should never be the sole decision-making method. They provide structure, but decisions still need to be informed by intuition, experience, and alignment with long-term goals.

My Perspective on RICE

In the end, the RICE framework is a helpful tool for product managers, especially when used to bring clarity and objectivity to decision-making. It’s particularly effective when applied to short-term projects that need quick prioritization. However, my experience has taught me that no framework should be used as a rigid rule. Instead, they should serve as guidelines to help inform broader decision-making.

Frameworks like RICE can help navigate the complexities of product management, but they shouldn’t become a substitute for strategic vision or creative thinking. At the end of the day, product management is as much about art as it is about science, and the best decisions come from balancing data with insight, intuition, and experience.


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## Digital Banking and Product Management: Lessons from the Kuwaiti Market

*Published: 2024-09-05*
*Tags: Digital Banking, Product Management*

URL: https://www.ahmadkarmi.com/insights/digital-banking-and-product-management-lessons-from-the-kuwaiti-market

> Explore how Kuwaiti banks lead digital banking through innovation, product management, AI, and customer-centric strategies in a competitive landscape.

In an era where digital transformation is reshaping industries globally, the banking sector in Kuwait has emerged as a leader in the GCC region, particularly in the realm of digital banking. This article delves into the strategic role of product management in the Kuwaiti banking sector, offering insights into how these banks are navigating the complex landscape of digital transformation. By examining the unique challenges and opportunities faced by Kuwaiti banks, we uncover lessons that are applicable across the GCC and beyond.

## **Regulatory and Technological Landscape: A Catalyst for Innovation**

### **The Role of the Central Bank of Kuwait (CBK)**

The Central Bank of Kuwait (CBK) has played a pivotal role in driving digital transformation within the banking sector. By introducing regulations that support the establishment of digital banks and encouraging the adoption of fintech solutions, the CBK has set the stage for innovation. The 2022 introduction of digital banking licenses has been a game-changer, allowing traditional banks to venture into the digital space with dedicated digital banking arms like NBK’s Weyay, Boubyan Bank’s Nomo, and KFH’s Tam.

These initiatives are not just about launching new digital products; they are about reimagining the entire banking experience. For instance, the CBK has been instrumental in promoting the use of cloud computing, digital onboarding, and enhanced cross-border payment systems, all of which are critical components of a modern digital bank.

### **Technological Adoption and Challenges**

The technological turbulence that comes with digital transformation presents both opportunities and challenges. Kuwaiti banks have been quick to adopt emerging technologies like AI, blockchain, and data analytics to enhance their digital offerings. For example, Boubyan Bank’s Nomo uses advanced AI to offer personalized banking services, which has been a significant factor in its success. Similarly, KFH is leveraging AI and robotics for decision-making, profitability analysis, fraud detection, and risk management.

However, the rapid pace of technological adoption also brings challenges, particularly in terms of integration with legacy systems. High technical debt is a common issue in the banking sector, and Kuwaiti banks are no exception. Modernizing these systems without disrupting ongoing services requires substantial investment and expertise. KIB (Kuwait International Bank) has been at the forefront of addressing this challenge, undertaking a comprehensive digital restructuring to overhaul its technological base.

#### **Key Insight:**

**_‍_**_The regulatory support from CBK, coupled with the proactive adoption of cutting-edge technologies, has positioned Kuwaiti banks as leaders in digital banking within the GCC. The ability to balance innovation with regulatory compliance and technological integration is a critical lesson for other banks in the region._

## **Customer-Centric Innovations: Personalization as a Competitive Advantage**

### **Personalization in Banking Services**

Personalization has become a key differentiator in the banking industry, and Kuwaiti banks are leveraging data analytics and AI to deliver customized experiences to their customers. This shift towards personalized banking services is driven by the increasing demand from tech-savvy customers, particularly the younger population, who expect banking services to be as intuitive and tailored as other digital experiences in their lives.

For example, NBK’s Weyay is designed to cater specifically to the needs of younger customers, offering features that align with their lifestyle and financial habits. This focus on personalization is not just about enhancing customer satisfaction; it’s about building long-term loyalty and differentiating the bank in a competitive market.

### **The Role of Data Analytics**

Data analytics plays a crucial role in enabling this level of personalization. By analyzing customer behavior, transaction history, and preferences, banks can develop products that meet specific needs. For instance, KFH’s use of data-driven insights to enhance its digital services has been a key factor in its ability to offer more personalized and relevant products.

Moreover, this approach to product management is not limited to consumer banking. Corporate clients, too, benefit from tailored solutions that address their unique challenges, whether it’s in treasury management, cross-border payments, or risk mitigation. The ability to offer such customized solutions is a significant competitive advantage that Kuwaiti banks have over their regional counterparts.

#### **Key Insight:**

**_‍_**_Personalization is not just a trend; it’s a strategic imperative in digital banking. Kuwaiti banks’ focus on delivering personalized experiences through data analytics and presumably AI is a lesson in how to build customer loyalty and maintain a competitive edge._

## **Product Management Amid High Technical Debt: Navigating Legacy Challenges**

### **Modernizing Legacy Systems**

The legacy systems that many banks operate on are a double-edged sword. While they provide stability and reliability, they also pose significant challenges when it comes to integrating new technologies. Kuwaiti banks, like many others globally, are grappling with high technical debt, a consequence of years of reliance on these entrenched systems.

KIB’s digital transformation strategy offers a case study in how to navigate this challenge. By prioritizing the modernization of its core banking systems, KIB has been able to introduce new digital products without compromising the stability of its existing services. This approach required a significant upfront investment, but it has paid off in terms of increased efficiency, reduced operational costs, and enhanced customer satisfaction.

### **Balancing Innovation with Stability**

Product managers in the banking sector must strike a delicate balance between introducing innovative new products and maintaining the stability of existing services. This is particularly challenging in a highly regulated environment like Kuwait, where any disruption to banking services can have significant repercussions.

The key to success lies in a phased approach to modernization, where new technologies are integrated gradually, and legacy systems are updated incrementally. This minimizes the risk of service disruptions while allowing the bank to keep pace with technological advancements.

#### **Key Insight:**

**_‍_**_High technical debt is a significant barrier to digital transformation, but it can be managed through a strategic, phased approach to modernization. Kuwaiti banks’ success in this area provides valuable lessons for other institutions facing similar challenges._

## **Marketing and Financial Performance: The ROI of Digital Investments**

### **The Impact of Marketing on Financial Performance**

Investment in digital marketing is crucial for the success of digital banking products. In the competitive landscape of Kuwaiti banking, where multiple banks are vying for the attention of tech-savvy consumers, effective marketing strategies are essential.

Research shows a direct correlation between marketing expenditure and financial performance in Kuwaiti banks. For instance, a study conducted on Kuwaiti banks from 2008 to 2018 found that those with higher marketing investments saw better financial returns, including higher return on assets (ROA) and overall profitability.

## **Conclusion**

The digital transformation journey of the banking sector in Kuwait offers several key lessons for other banks in the GCC and beyond:

1. **Regulatory Support is Crucial:**

 The proactive role of the CBK in encouraging digital transformation has been a key enabler for Kuwaiti banks. Other GCC regulators can take a similar approach to foster innovation in their banking sectors.

2. **Personalization is a Competitive Advantage:**

 The focus on delivering personalized banking experiences through data analytics and AI has helped Kuwaiti banks build strong customer relationships and differentiate themselves in the market.

3. **Managing Technical Debt Requires a Phased Approach:**

 The successful modernization of legacy systems in Kuwaiti banks demonstrates the importance of a strategic, phased approach to integrating new technologies.

4. **Marketing Drives Financial Performance:**

 Kuwaiti banks have shown that effective marketing strategies are essential for the success of digital banking products, with a direct impact on financial performance.

By applying these lessons, banks in the GCC can navigate the challenges of digital transformation and position themselves as leaders in the increasingly competitive digital banking landscape.

‍


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## Mastering Strategic Performance Indicators in Product Management

*Published: 2024-07-05*
*Tags: Product Management*

URL: https://www.ahmadkarmi.com/insights/mastering-strategic-performance-indicators-in-product-management

> Learn how to master Strategic Performance Indicators (SPI) in product management. This guide covers essential metrics, including acquisition, retention, and user engagement, with a detailed case study on Netflix. Understand how to apply SPIs to drive product success and stay ahead in the competitive landscape.

Imagine stepping into the vibrant chaos of a new company, where your mission is to review and refine the Key Results set for your team. If you excel, you’ll earn the leadership’s admiration. Fail, and it might signal that you don’t grasp your leadership role.

## The Challenge for Product Management

When joining a new company, product leads and executives face a common challenge. At junior levels, metrics and key results are dictated by leadership. However, in senior roles, you’re expected to offer insights on key focus areas.

If you’re adept at your job, you likely understand the broad array of metrics. The true challenge lies in selecting the right metrics for your product. These are the **Strategic Performance Indicators (SPI)**.

## Understanding Strategic Performance Indicators (SPI)

SPIs are not a single metric but a collection that provides a comprehensive view of the product. Mastering these metrics is difficult due to several reasons:

1. **Context Dependency**: A metric beneficial for one product might be detrimental for another. For instance, time spent is valuable for an engagement-focused product like Facebook but counterproductive for a productivity tool like Superhuman.
2. **Temporal Relevance**: A great metric one year might be irrelevant the next. What was crucial in 2022, like growth rates, might be replaced by profitability and cost savings in 2024.

## Key Variables for Useful Metrics

To master SPIs, consider four key variables that determine a metric’s relevance:

‍

Variable
Category
Subcategory
Details

Product
Stage
Early Stage
Focus on Product-Market Fit (PMF)

Product
Stage
Early Stage
Identify initial user needs and satisfaction

Product
Stage
Early Stage
Measure user adoption and engagement

Product
Stage
Late Stage
Focus on growth or profitability

Product
Stage
Late Stage
Track revenue growth and market penetration

Product
Stage
Late Stage
Monitor cost efficiency and profit margins

Product
Goal
Late Stage
Define metrics based on product’s specific goals

Product
Goal
Late Stage
Example: Engagement for Facebook (e.g., time spent, interactions)

Product
Goal
Late Stage
Example: Productivity for Superhuman (e.g., time saved, task completion rate)

Product
Business Model
Subscription-based
Emphasize retention rates and subscriber growth

Product
Business Model
Subscription-based
Measure monthly/annual recurring revenue (MRR/ARR)

Product
Business Model
Subscription-based
Track churn rates and customer lifetime value (CLV)

Product
Business Model
Freemium
Track conversion rates from free to paid users

Product
Business Model
Freemium
Monitor user upgrade rates

Product
Business Model
Freemium
Analyze the effectiveness of premium features

Product
Business Model
Ad-supported
Focus on user engagement and time spent

Product
Business Model
Ad-supported
Measure ad impressions and click-through rates (CTR)

Product
Business Model
Ad-supported
Track user sessions and interaction duration

Company
Strategic Priorities
R&D
Align metrics with the company’s strategic goals

Company
Strategic Priorities
R&D
Example: Impact of AI on tech companies

Company
Strategic Priorities
R&D
Example: Strategic shifts towards sustainability or digital transformation

Company
Industry Knowledge
R&D
Deep understanding of industry trends and dynamics

Company
Industry Knowledge
R&D
Example: Understanding the travel industry for Airbnb

Company
Industry Knowledge
R&D
Example: Staying updated with regulatory changes in fintech

Industry
Relevant Metrics
Analytics
Identify and utilize industry-specific metrics

Industry
Relevant Metrics
Analytics
Avoid blind spots

Industry
Relevant Metrics
Analytics
Example: E-commerce metrics like conversion rates, average order value (AOV), and cart abandonment rates

Industry
Benchmarks
Strategy
Use industry benchmarks to set goals

Industry
Benchmarks
Strategy
Compare key metrics with industry leaders

Industry
Benchmarks
Strategy
Example: Benchmarking customer satisfaction scores in the hospitality industry

End Users
Value Creation
Business
Metrics should reflect user value

End Users
Value Creation
Business
Use metrics such as Customer Satisfaction (CSAT) and Net Promoter Score (NPS)

End Users
Value Creation
Business
Track user retention and repeat usage

End Users
Value Creation
Tailored Metrics
Define metrics for different user personas

End Users
Value Creation
Tailored Metrics
Example: For a fitness app, measure engagement for casual users vs. fitness enthusiasts

End Users
Value Creation
Tailored Metrics
Example: For educational platforms, track progress and success rates for various learner profiles

‍

## Applying the Framework

To apply this framework, start with understanding the product and end users to define key metrics. Then, incorporate company priorities and industry context to refine these metrics into a handful of SPIs.

## Case Study: Netflix

Netflix, a leader in the streaming industry, provides a wealth of pre-published metrics that illustrate the application of Strategic Performance Indicators in product management. Here are some key metrics used by Netflix, as reported in their quarterly earnings and various industry analyses.

‍

Key Metrics
Category
Details

Product Funnel Metrics
Acquisition
New subscriber additions (quarterly reports)

Product Funnel Metrics
Acquisition
Cost per acquisition (CPA)

Product Funnel Metrics
Activation
Trial-to-paid conversion rates

Product Funnel Metrics
Activation
Engagement during the first month

Product Funnel Metrics
Retention
Monthly churn rate

Product Funnel Metrics
Retention
Customer lifetime value (CLV)

Product Funnel Metrics
Revenue
Average Revenue Per User (ARPU)

Product Funnel Metrics
Revenue
Total revenue (quarterly and annual reports)

Product Funnel Metrics
Referral
Percentage of new subscribers from referrals

Product Funnel Metrics
Referral
Referral program effectiveness

Demand Side Metrics
User Experience
Content hours watched per subscriber

Demand Side Metrics
User Experience
User satisfaction scores (NPS, CSAT)

Demand Side Metrics
User Experience
App performance metrics (load times, buffering rates)

Demand Side Metrics
Content Engagement
Viewer engagement with new releases

Demand Side Metrics
Content Engagement
Completion rates of shows and movies

Supply Side Metrics
Content Library
Number of new titles added monthly

Supply Side Metrics
Content Library
Diversity of content (genres, languages)

Supply Side Metrics
Production Efficiency
Cost of content production

Supply Side Metrics
Production Efficiency
Production timelines and delays

Supply Side Metrics
Content Performance
Ratings and reviews

Supply Side Metrics
Content Performance
Awards and critical acclaim

‍

### **Company and Industry Perspective**:

– **Company Focus**: Is the goal expanding the subscriber base, increasing engagement, or improving profitability?
– **Industry Benchmarks**: Compare Netflix’s metrics with industry standards from competitors like Disney+, Amazon Prime Video, and HBO Max.

For example, Netflix focuses heavily on user engagement metrics, such as content hours watched and user satisfaction scores, to reduce churn and increase customer lifetime value. By comparing these metrics with industry benchmarks, Netflix continuously refines its strategy to maintain its leadership position.

## Summing it all up

In a real job, mastering SPIs can present itself when evaluating OKRs or launching a new product. In interviews, it might appear as questions about north star metrics or defining success metrics for a product or feature.

The framework for mastering SPIs can be summarized by the following:

– **The Product**: Stage, goal, model.
– **The Industry**: Relevant metrics, benchmarks.
– **Your Organization**: Strategic priorities.
– **The Users**: Value created.

‍

> **Stay ahead in product management:** [**Subscribe to my newsletter**](http://www.ahmadkarmi.com/newsletter) for exclusive insights and expert advice. Discover the best strategies for mastering Strategic Performance Indicators and transforming your business. Receive key metrics and performance tips delivered straight to your inbox. Join now to ensure you’re always at the cutting edge of product management innovation.

‍


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## How to Integrate AI into Your GTM Strategy for Maximum Impact

*Published: 2024-06-26*
*Tags: Artificial Intelligence, Product Management*

URL: https://www.ahmadkarmi.com/insights/how-to-integrate-ai-into-your-gtm-strategy-for-maximum-impact

> Discover how integrating AI into your Go-to-Market (GTM) strategy can revolutionize your business. From personalizing customer interactions and streamlining the sales process to scaling engagement with AI chatbots and enhancing human-AI collaboration, this comprehensive guide provides actionable insights to help you stay ahead of the competition. Explore the full article to unlock the potential of AI in transforming your GTM strategy and driving revenue growth.

Artificial Intelligence (AI) is revolutionizing business processes across various industries, and integrating AI into your Go-to-Market (GTM) strategy can offer significant benefits. From personalizing customer interactions to optimizing sales processes and scaling customer engagement, AI provides the tools to enhance your business performance. Here’s a detailed guide on leveraging AI for a more effective GTM strategy.

## Personalizing Customer Interactions with AI

Personalization is key to winning customers in today’s competitive market. AI enables businesses to analyze customer data in real-time, offering insights into customer behavior and preferences. Here’s how AI can personalize customer interactions:

1. **Data-Driven Insights:** AI can analyze vast amounts of data, identifying patterns and trends that can inform personalized marketing strategies.

2. **AI-Powered Chatbots:** These tools can engage customers in meaningful conversations, providing personalized recommendations and solutions based on previous interactions and customer data.

3. **Dynamic Content:** AI can help create dynamic content that changes based on user behavior, ensuring that each customer sees content relevant to their interests and needs.

## Streamlining the Sales Process

AI can significantly optimize the sales process by automating routine tasks and providing data-driven insights. There are many reasons as to why you would want to use AI to streamline your sales process, but here’s a few key ones that many businesses today find useful and help save both time and money:

1. **Lead Segmentation and Scoring:** AI can analyze historical data to segment prospects and prioritize leads, helping sales teams focus on high-potential opportunities.

2. **Sales Forecasting:** AI models can predict future sales trends, allowing businesses to allocate resources more efficiently and plan strategically.

3. **Automated Outreach:** AI-powered tools can automate email and social media outreach, ensuring timely and personalized communication with potential customers.

## Scaling Customer Engagement with AI Chatbots

AI chatbots are transforming customer service by providing instant, around-the-clock support. Of course, we need to be careful, in this scenario you want to train your chatbots heavily in order to deal with customers the way that fits your brand and customer service guidelines. But, most importantly, in a way that customers will gain value. There are just a few reasons why chatbots can enhance customer engagement and user experience:

1. **24/7 Availability:** AI chatbots can handle customer inquiries at any time, ensuring that customers receive prompt responses even outside business hours.

2. **Handling Common Issues:** Chatbots can resolve frequently asked questions and common issues, freeing up human agents to tackle more complex problems.

3. **Booking Appointments:** AI chatbots can schedule meetings and follow-up calls, streamlining the customer journey and ensuring seamless transitions between digital and human interactions.

## Enhancing Human-AI Collaboration

AI can support human agents by handling repetitive tasks and providing actionable insights. Here’s how you can save money and time by making life easier for the people who work for your organization, freeing up time for them to focus on what really matters:

1. **Automating Routine Tasks:** AI can take over routine tasks such as data entry, freeing up human agents to focus on more strategic activities.

2. **Providing Recommendations:** AI can analyze customer interactions and provide recommendations to human agents, helping them make more informed decisions.

3. **Seamless Handoffs:** AI systems can seamlessly transfer complex issues to human agents, ensuring that customers receive the best possible service.

## Implementing AI in Your GTM Strategy

To effectively integrate AI into your GTM strategy, consider the following steps:

1. **Identify Key Areas for AI Integration:** Determine which aspects of your GTM strategy can benefit most from AI, such as marketing, sales, or customer service.

2. **Choose the Right AI Tools:** Select AI tools that align with your business goals and can seamlessly integrate with your existing systems.

3. **Train Your Team:** Ensure that your team is trained to use AI tools effectively and understands how to leverage AI to enhance their workflows.

4. **Monitor and Optimize:** Continuously monitor the performance of AI tools and make necessary adjustments to optimize their impact on your GTM strategy.

## Conclusion

Integrating AI into your GTM strategy can offer significant advantages, from personalized customer interactions to optimized sales processes and enhanced customer engagement. By leveraging AI, businesses can drive revenue, improve efficiency, and stay ahead of the competition. Start by identifying key areas for AI integration, choose the right tools, train your team, and continuously monitor and optimize your AI strategy.

‍

> **Stay at the forefront of AI:** [**Subscribe to my newsletter**](http://www.ahmadkarmi.com/newsletter) for exclusive insights and expert advice. Discover the best AI use cases to transform your business and receive strategies delivered straight to your inbox. Join now to ensure you’re always at the forefront of innovation.


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## Real-Time Monitoring and Predictive Maintenance: The Backbone of Smart City Operations

*Published: 2024-06-14*
*Tags: Artificial Intelligence, Smart Cities*

URL: https://www.ahmadkarmi.com/insights/real-time-monitoring-and-predictive-maintenance-the-backbone-of-smart-city-operations

> As cities expand, efficient infrastructure management is critical. Real-time monitoring and AI-powered predictive maintenance are revolutionizing urban management by using sensors to detect issues and AI to predict maintenance needs, ensuring proactive repairs. This approach saves costs, increases efficiency, extends infrastructure lifespan, and promotes sustainability. Cities like Barcelona, Singapore, and New York are leading the way with these technologies, making urban environments smarter and more resilient. The future of city management lies in adopting these advanced solutions for sustainable and efficient urban living.

As urban centers continue to expand at an unprecedented rate, the management and efficient operation of their infrastructure are becoming increasingly critical. Traditional methods of maintenance, marked by manual inspections and reactionary fixes, often result in costly and disruptive failures. However, a new wave of technological solutions, specifically real-time monitoring and predictive maintenance powered by Artificial Intelligence (AI), is dramatically changing this dynamic. By leveraging these advanced technologies, cities are becoming smarter, more responsive, and better equipped to serve their growing populations.

## In-Depth Look at Real-Time Monitoring

At its core, real-time monitoring is a process that involves the use of state-of-the-art sensors strategically placed on various components of urban infrastructure, such as roads, bridges, water systems, and public transportation. These sensors, which operate continuously, collect a wealth of data, enabling cities to detect and address potential problems as soon as they emerge.

1. **Immediate Detection and Quick Response**: The advanced sensors employed in real-time monitoring detect issues immediately, allowing cities to take prompt action to rectify these problems before they escalate into larger, more complex issues.

2. **Efficient Resource Management**: With the ability to identify and address issues as they arise, maintenance can be performed when and where it’s needed, rather than adhering to a rigid, fixed schedule. This targeted approach saves both time and money.

3. **Enhanced Safety Measures**: Continuous monitoring aids in identifying potential safety hazards, allowing authorities to address these problems before they morph into serious issues that might pose a risk to the public.

## The Role of AI in Monitoring

Artificial Intelligence processes the vast amounts of data collected by these sensors, analyzing it to predict when and where maintenance will be needed. This predictive capability ensures that repairs and maintenance are done proactively, preventing potential breakdowns and failures rather than merely reacting to them after they occur.

– **Machine Learning**: Advanced AI algorithms learn from past data to predict future failures, continuously refining their accuracy and effectiveness over time.

– **Internet of Things (IoT) Sensors**: These smart sensors gather detailed and nuanced data, which AI then analyzes to anticipate and forecast maintenance needs.

– **Edge Computing**: With edge computing, data processing happens right at the source, significantly reducing delays and improving response times.

## The Numerous Benefits of Predictive Maintenance

The integration of AI-driven real-time monitoring and predictive maintenance into the operations of a city offers a plethora of advantages:

1. **Significant Cost Savings**: Preventative maintenance reduces the need for costly emergency repairs, saving cities substantial amounts of money in the long run.

2. **Increased Efficiency**: Maintenance that’s based on actual needs, rather than a fixed schedule, optimizes the use of resources, improving overall operational efficiency.

3. **Extended Infrastructure Lifespan**: Regular, targeted maintenance extends the lifespan of infrastructure components, maximizing their value and delaying the need for replacements.

4. **Environmental Benefits**: Efficient and targeted maintenance practices reduce waste and consequently, lessen the city’s environmental impact.

## Real-World Implementations

Cities around the globe are adopting these groundbreaking technologies to improve their infrastructure management:

– **Barcelona**: The Spanish city uses IoT sensors and AI to manage its water systems effectively. This system allows for early detection of leaks, saving valuable water resources.

– **Singapore**: This Asian city-state employs AI to manage its traffic systems efficiently, reducing congestion on its roads and improving the efficiency of its public transport systems.

– **New York City**: The Big Apple uses predictive maintenance for its sprawling subway system, reducing breakdowns and improving the reliability of this vital public transportation method.

## The Future of Urban Management

With the advancements in AI and other related integrated technologies, the future of urban management looks exceedingly promising:

1. **Integration with 5G**: The advent of 5G, with its faster data transmission rates and lower latency, will enhance the capabilities of real-time monitoring systems.

2. **Advancements in Machine Learning**: The development of more sophisticated and complex algorithms will provide even more accurate and precise predictions.

3. **Expanding IoT Networks**: As more sensors are deployed, covering a wider range of infrastructure, cities will be able to achieve comprehensive and holistic monitoring.

4. **Focus on Sustainability**: The optimized use of resources, possible through these technologies, will aid cities in working towards their sustainability goals.

## Conclusion

Real-time monitoring and predictive maintenance, powered by AI, are revolutionizing the way cities manage their infrastructure. These advanced technologies provide an array of benefits, including significant cost savings, increased efficiency, and improved safety. As more and more cities worldwide adopt these innovations, urban environments will become smarter and more resilient, capable of meeting the ever-growing demands of their populations. The future of urban management undoubtedly lies in the adoption and integration of these advanced technologies, paving the way for a more sustainable and efficient urban living experience.

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## Leveraging the Fibonacci Sequence for Effective Project Estimation in Agile and Scrum

*Published: 2024-05-07*
*Tags: Agile, Product Management, Project Management, Scrum*

URL: https://www.ahmadkarmi.com/insights/leveraging-the-fibonacci-sequence-for-effective-project-estimation-in-agile-and-scrum

> Dive into the world of Agile and Scrum methodologies to discover how the Fibonacci sequence revolutionizes project estimation. Learn to navigate complexities with improved accuracy, ease, and strategic prioritization. Explore the detailed steps of using this innovative approach, from educational basics through planning poker to expert consensus-building. Overcome common challenges and integrate this powerful tool to enhance your project management strategies. Ideal for professionals aiming to excel in product management and project management within fast-paced development environments.

## **Exploring Project Estimation in the Agile and Scrum Contexts**

With the rapid pace of software development, the ability to accurately estimate projects is a game-changer. This skill ensures proper resource allocation, careful planning, and sets realistic expectations for all involved parties. In the Agile and Scrum methodologies, where adaptability and fast delivery are key, project estimation is vital. Traditional estimation methods can struggle to cope with the dynamic changes and uncertainties inherent to these methodologies. Here is where the Fibonacci sequence comes into play, providing a refined approach to mastering project estimations in Agile settings.

## **Understanding the Fibonacci Sequence and Its Role in Project Estimation**

The Fibonacci sequence is a remarkable mathematical series where each number is the sum of the two preceding ones. It’s found throughout nature and has a unique application in project management, especially within the Agile and Scrum frameworks, for estimating task size or complexity. Its main advantage is its compatibility with the concepts of _relative sizing_ and _story points_. Relative sizing allows teams to estimate tasks in relation to others, rather than using subjective units like hours or days. By using story points that follow the Fibonacci scale (1, 2, 3, 5, 8, 13, 21…), teams can better assess the effort needed to complete a task compared to others.

While the Fibonacci sequence is often employed as a tool in Agile methodologies, it’s important to clarify that its application is metaphorical. The sequence itself is not inherently found in nature but can describe patterns that occur in natural phenomena. This distinction ensures that the practical application in project management is grounded in methodology rather than misunderstood as a natural law.

## **Benefits of Using the Fibonacci Sequence for Project Estimation**

It’s important to note that the Fibonacci sequence is highly effective for estimating larger, more complex tasks. Its non-linear progression adeptly handles the increased uncertainty related to these tasks, reflecting the innate complexity and ambiguity that larger projects usually have. Below are some potential benefits of using it in project estimations:

### **Improved Accuracy**

The non-linear nature of the Fibonacci sequence reflects the inherent complexity of tasks in project development. As tasks increase in size, the uncertainty and complexity also increase, a concept captured well by the growing gaps between successive Fibonacci numbers. This leads to more accurate estimations and helps teams avoid the common mistakes of under or overestimating tasks.

### **Ease and Speed**

Using the Fibonacci sequence simplifies the estimation process by limiting the choices to specific numbers, which speeds up decision-making during sprint planning. This is particularly beneficial in keeping the team focused and maintaining the pace during planning sessions.

### **Better Task Prioritization**

Fibonacci-based estimations give teams a clearer understanding of task complexity, which helps in better task prioritization. This ensures resources are allocated to tasks that are crucial for project success, streamlining the workflow and delivery timeline.

## **How to Use the Fibonacci Sequence for Project Estimation**

When implementing the Fibonacci sequence for project estimation, it is vital to practice moderation and maintain flexibility. Relying too heavily on any single estimation method can lead to rigidity, which is antithetical to the core Agile principles of adaptability and responsiveness to change. We use the sequence carefully and thoughtfully with the following steps to ensure it’s success:

### **Step 1: Educate Your Team**

Start by ensuring every team member understands the Fibonacci sequence and how it’s applied in project estimation. This understanding is crucial for consistency across the team.

### **Step 2: Use Planning Poker**

Planning Poker is an effective way to use Fibonacci in estimations. Each team member gets cards with Fibonacci numbers. For each task, members pick a card that they believe best represents the task’s complexity. This method makes the process engaging and democratic, giving everyone a voice.

### **Step 3: Reach Consensus**

After the cards are revealed, encourage open discussion about differences in estimations. This conversation is valuable as it uncovers different viewpoints and insights, which are necessary for reaching a consensus that everyone respects.

### **Step 4: Reflect and Adapt**

Agile is all about adaptability and continuous learning. Regularly review past estimations and compare them with actual outcomes. This practice will help improve the estimation process over time, making it more accurate.

## **Overcoming Common Challenges**

In addition to structured methods such as the Fibonacci sequence, the value of team members’ experience and intuition is essential. These estimations should serve as guides rather than strict rules, allowing for adjustments based on the insights experienced practitioners can offer. Like any tool, it should not be used in isolation. It is important to address the following challenges when using it in project estimation:

### **Challenge 1: Varied Interpretations**

Different team members might have different understandings of a task’s complexity, leading to a wide range of estimates. **Solution:** Encourage open discussions and provide additional training or resources to align team members’ understanding.

### **Challenge 2: Resistance to Change**

Switching to a Fibonacci-based estimation method might face some resistance. **Solution:** Demonstrate its benefits through small, controlled tests before implementing it fully.

## **Addressing Limitations and Looking at Alternatives**

The Fibonacci sequence, although a valuable tool in estimating work effort, must not be viewed as a one-size-fits-all solution for all types of tasks. It is particularly important to recognize that when dealing with large or complex tasks, the effective strategy would be to break them down into smaller, manageable parts. This method of decomposition can often yield more accurate estimates as it allows for a more detailed understanding of the task at hand. Prior to diving into detailed Fibonacci sizing, it’s beneficial to consider using preliminary estimation techniques such as T-shirt sizing. This method, while less precise, offers a quicker way of gaining a general understanding of the scale of the work before moving on to more detailed estimation techniques.

However, in general, more mature and advanced teams working on larger projects that require extensive decomposition and face considerable uncertainty tend to favor the Fibonacci sequence over other alternatives.

## **Conclusion**

The Fibonacci sequence’s major role in managing project estimation complexities, especially in Agile and Scrum methodologies, cannot be understated. Its use enhances estimation accuracy, accounts for uncertainty and variability in tasks, and fosters team collaboration. In addition, it streamlines project execution by efficiently allocating resources. As Agile methodologies keep evolving, the Fibonacci sequence, with its proven efficacy and versatility, will likely remain a key component in project estimation.

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> **Master Your Pipeline & Roadmap:** [**Subscribe to my newsletter**](http://www.ahmadkarmi.com/newsletter) for exclusive insights on product management and entrepreneurship. Get expert advice and strategies delivered straight to your inbox to help you launch and lead your product to its fullest potential.


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## Building Fair Tech: How PMs Can Mitigate AI Bias in Their Products

*Published: 2024-04-29*
*Tags: Artificial Intelligence, Compliance, Ethics, Product Management*

URL: https://www.ahmadkarmi.com/insights/building-fair-tech-how-pms-can-mitigate-ai-bias-in-their-products

> This article emphasizes the crucial role of Product Managers in addressing AI-fueled products to ensure fairness in the applications of AI. It outlines the responsibility of PMs to understand sources of bias—data, algorithmic, and measurement—and implement strategies like ensuring data diversity, promoting algorithmic transparency, and conducting fairness tests. By actively involving diverse user groups and maintaining transparency about AI practices, PMs can enhance trust, improve user satisfaction, and contribute to equitable AI development.

# **Abstract:**

As Artificial Intelligence (AI) continues to revolutionize various sectors, it brings along the challenge of bias. The potential of AI is vast, but if not carefully managed, biases in AI algorithms could result in unfair outcomes for users, leading to a decrease in trust and ultimately affecting product success. This article sheds light on the crucial role of Product Managers (PMs) in reducing AI bias within their products. We delve into the origins of AI bias, its influence on product development, and provide practical strategies for PMs to promote fairness throughout the product lifecycle.

## **Introduction:**

Incorporating AI into products opens up avenues for personalization, automation, and amplified user experiences. However, AI algorithms are prone to adopting and magnifying biases present in the training data or design choices. This could result in prejudiced outcomes, like biased loan approvals, unfair hiring practices, or inaccurate product recommendations.

## **The PM’s Responsibility:**

Product managers, being the voice of the customer, have a significant role in ensuring an impartial and ethical application of AI. This role extends beyond merely considering the ethical aspects. PMs should be well aware of how biases in AI can potentially have substantial consequences. Biased AI can greatly impact the adoption rate of a product, particularly if users feel the product has a preference for a certain group. This lack of trust can lead to a notable decline in user engagement, which can significantly affect a business’s success. Therefore, it is crucial for product managers to take every precaution to prevent any form of bias in AI to retain the trust and loyalty of their user base.

Which leads us to the next question: what are the possible types of data bias we might encounter when working with AI?

## **Sources of AI Bias:**

### **Data Bias:**‍

Data bias arises when the data used to train AI models mirrors societal biases. For instance, if an AI model is trained on hiring data that leans towards a specific gender or racial group, the resulting AI model may continue those biases in its decision-making. To mitigate data bias, it’s crucial to use diverse data sets and actively curate data to eliminate potential biases. This calls for a comprehensive understanding of the context in which the data was collected and the potential biases that may be reflected in it.

### **Algorithmic Bias:**

This type of bias refers to the design choices and assumptions made during the model development phase. Even with unbiased data, the algorithm’s design and the assumptions it makes can introduce bias. For example, if an algorithm is designed to prioritize a certain type of data or outcome, it may end up unfairly favoring or disadvantaging certain user groups. To combat algorithmic bias, it’s essential to comprehend how the algorithm functions and to pinpoint potential bias points. Techniques like explainable AI (XAI) can help make the decision-making process more transparent and understandable, which allows for more informed and fair design choices.

### **Measurement Bias:**

Measurement bias occurs when the metrics used to assess an AI model’s performance disregard bias in certain user groups. For instance, if an AI model’s success is evaluated solely by its overall accuracy, it may ignore that the model performs poorly for a specific demographic group. To tackle measurement bias, it’s crucial to use diverse and comprehensive performance metrics that consider the experiences of all user groups. Consistent fairness testing and monitoring can also aid in identifying and mitigating bias early on.

## **Impact of AI Bias on Product Development:**

### **Unequal User Experiences:**

Biased AI can lead to unequal user experiences, often resulting in negative outcomes for certain demographics. For example, a personalized recommendation system may heavily rely on AI. If the underlying AI algorithm has been trained mainly on data from a specific demographic, it might not perform as well when making recommendations for users outside of that demographic. This can hinder product usability and satisfaction, as these users might receive irrelevant or less accurate recommendations, diminishing their overall experience with the product.

### **Discriminatory Outcomes:**

When AI systems have inherent biases, they can lead to discriminatory outcomes. For instance, an AI system used in the hiring process that has been trained on data favoring a certain gender or age group may end up unfairly screening out qualified candidates from other demographics. Similarly, an AI-driven loan approval system could be biased if it’s been trained mostly on data from high-income neighborhoods, possibly leading to unfair rejections for equally qualified individuals from lower-income areas.

### **Regulatory Risks:**

As AI continues to infiltrate more aspects of our lives, regulations around AI fairness are becoming more prevalent. A product that uses an AI algorithm with inherent bias could face significant compliance challenges. For instance, a health insurance company using AI to determine premiums could be at risk if their algorithm disproportionately affects a certain racial or age group. In such cases, even unintentional bias could lead to regulatory scrutiny, fines, or litigation.

## **Strategies for Mitigating AI Bias:**

### **Promote a Culture of Fairness:**

This involves actively encouraging everyone on the team to factor fairness into every decision from the start of product development. For example, during ideation, challenge team members to consider how their ideas might impact different user groups. When launching a product, consider how its features might affect users differently and iterate based on these insights.

### **Data Diversity and Curation:**

Prioritize the use of diverse data sets when training AI models to ensure the model represents a wide range of user experiences. For instance, if you’re developing a facial recognition algorithm, ensure the training data includes faces of varying ages, genders, and ethnic backgrounds. Additionally, curate data actively to remove biases. This might involve using techniques like oversampling underrepresented groups or undersampling overrepresented ones to balance the data.

### **Algorithmic Transparency:**

Make an effort to understand how the algorithms work and identify potential points of bias. For example, if you’re using an algorithm to predict user behavior, examine the factors it weighs heavily and consider whether these might introduce bias. Techniques like explainable AI (XAI) can help make these decision-making processes more transparent and understandable, allowing for more informed and fair design choices.

### **Fairness Testing and Monitoring:**

Incorporate fairness testing into the development lifecycle. This could involve developing metrics that specifically measure fairness, such as disparate impact ratio or demographic parity. Monitor AI models for bias after deployment and regularly retest them to ensure they remain fair as they continue to learn and evolve.

### **User-Centered Design:**

Involve diverse user groups in the design process. This could mean conducting user research interviews with individuals from a range of backgrounds, or user testing sessions with diverse groups of users. Gathering feedback from a wide range of users helps ensure the product caters to all users fairly.

### **Communication and Transparency:**

**‍**Be open with users about how AI is used in the product and the steps taken to mitigate bias. This might involve publishing a blog post detailing the company’s approach to fairness, or including a section in the product’s FAQ page addressing how AI bias is handled. Transparency helps build trust and allows users to make informed decisions about whether and how they use the product.

## **Conclusion:**

Taking proactive measures to address AI bias allows Product Managers to spearhead the creation of ethically sound and unbiased products. This proactive approach involves identifying potential sources of bias early in the product development process and implementing strategies to mitigate these biases. By doing so, PMs not only shield their products from unfair outcomes but also ensure the trust and loyalty of their user base.

Trust, in this context, stems from users’ belief that the product will function without favoring or disadvantaging specific user groups. Ensuring user satisfaction, on the other hand, requires that all users find the product useful, regardless of their background or identity.

This is achieved by creating products that cater to a wide range of user experiences and needs. Finally, by addressing AI bias, PMs contribute to a future where AI technology can be leveraged for the benefit of all users.

The strategies outlined are all critical steps towards achieving this goal. Thus, the role of PMs extends beyond overseeing product development; they are shaping a more equitable future with AI.

‍

> **AI doesn’t have to be confusing:** [**Subscribe to my newsletter**](http://www.ahmadkarmi.com/newsletter) for exclusive insights on product management and entrepreneurship. Get expert advice and strategies delivered straight to your inbox to help you launch and lead your product to its fullest potential.


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## What They Don’t Tell You About Product Launches

*Published: 2024-04-25*
*Tags: Entrepreneurship, Product Management*

URL: https://www.ahmadkarmi.com/insights/what-they-dont-tell-you-about-product-launches

> "Launching a product is akin to throwing a party where you're not sure anyone will show up." This humorous yet insightful analogy encapsulates the essence of a product launch—steeped in anticipation and uncertainty. Celebrated with great enthusiasm, it is nonetheless diluted with complex, demanding challenges that could dictate the success or failure of your product. In this article, I delve into the often-overlooked aspects of product launches, using real-world examples to prepare you for success.

“Launching a product is akin to throwing a party where you’re not sure anyone will show up.”

This humorous yet insightful analogy encapsulates the essence of a product launch—steeped in anticipation and uncertainty. Celebrated with great enthusiasm, it is nonetheless diluted with complex, demanding challenges that could dictate the success or failure of your product.

In this article, I delve into the often-overlooked aspects of product launches, using real-world examples to prepare you for success.

The Psychological Toll

Understanding and Managing Launch Stress

‍Launching a product involves more than just strategic planning and marketing; it’s a significant psychological undertaking. Leaders must manage the logistics and emotional weight of the launch, navigating stress and anxiety while maintaining team morale.

For example, the launch of Google Glass highlighted how high expectations and public scrutiny can lead to overwhelming pressure on leaders, affecting their decisions and the product’s market acceptance.

How It Could Have Been Avoided: Leaders could benefit from setting realistic expectations and maintaining transparent communication with their teams and stakeholders. Engaging in stress-relief practices and establishing a feedback-rich environment could help alleviate psychological pressures.

The Silence of Slow Starts

Why Patience Pays Off

‍Contrary to popular belief, not all successful products hit the ground running. Many experience slow initial growth, which can be disheartening.

Microsoft’s Zune, for instance, struggled to gain traction against Apple’s iPod, largely due to its late entry into the market and the strong brand loyalty Apple had already established.

How It Could Have Been Avoided: A more phased and data-driven approach to market entry might have allowed Microsoft to adjust its strategy based on early user feedback and market reactions, focusing on niche markets where it could have offered unique value.

The Misconception of Market Fit

Navigating Continuous Market Adaptation

‍“Build it, and they will come” remains one of the biggest myths in product development. Achieving product-market fit is not a one-time event but a continuous, evolving process.

BlackBerry 10 was technically sound but failed to align with consumer preferences that had evolved to favor other operating systems.

How It Could Have Been Avoided: BlackBerry could have engaged in more iterative testing and feedback loops with potential users to better understand the shifting market dynamics and refine their product accordingly before a full-scale launch.

The Feedback Paradox

Balancing Vision and User Insights

‍Feedback, especially in the early stages of a product’s life, is invaluable. However, it presents a paradox. While early adopters can provide crucial insights, negative feedback can be demoralizing.

Crystal Pepsi is a classic example where initial curiosity and positive feedback did not translate into long-term success, as consumers ultimately rejected the concept of a clear cola.

How It Could Have Been Avoided: Pepsi could have benefited from a more nuanced analysis of initial feedback and perhaps a smaller, test-market launch to better gauge the product’s long-term viability before fully committing to a nationwide rollout.

Unforeseen Challenges

Preparing for the Unexpected

‍No matter how meticulously you plan, unexpected challenges will arise. These can range from technical glitches to market changes.

Nokia’s Lumia series, for example, faced significant challenges adapting to the rapidly changing smartphone market, overshadowed by competitors with faster innovations and more robust ecosystems.

How It Could Have Been Avoided: Nokia could have placed a stronger emphasis on agile methodologies and continuous learning to adapt more swiftly to market changes and new technologies.

Conclusion: The Road Ahead

A product launch marks not just a milestone but the beginning of a crucial phase where the real work of growing and adapting the product begins. By understanding the nuanced and often hidden challenges of launching a product, illustrated by notable examples from the industry, you can better prepare for the journey ahead, ensuring that your product not only launches but thrives in competitive markets.

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It doesn’t have to be difficult: Subscribe to my newsletter for exclusive insights on product management and entrepreneurship. Get expert advice and strategies delivered straight to your inbox to help you launch and lead your product to its fullest potential.


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## The Imperative of Data-Driven Decision Making in Product Management: An Analytical Perspective

*Published: 2024-04-01*
*Tags: Data Analytics, Data Driven Decision Making, Product Management*

URL: https://www.ahmadkarmi.com/insights/the-imperative-of-data-driven-decision-making-in-product-management-an-analytical-perspective

> Product management increasingly relies on the strategic utilization of vast datasets. This in-depth article emphasizes the necessity of data-driven decision-making (DDDM) in navigating the complexities of product management. It explores the integration of advanced analytical tools, methodologies, and a staunchly data-centric mindset. A thorough review of literature and contemporary practices illustrates how DDDM can revolutionize product development, boost user engagement, and enhance competitive positioning. This analysis aims to delineate the transformative impact of DDDM, underscoring its capacity to inform strategic decisions that propel product innovation and market adaptability.

In the digital economy’s current landscape, product management increasingly relies on the strategic utilization of vast datasets. This in-depth article emphasizes the necessity of data-driven decision-making (DDDM) in navigating the complexities of product management. It explores the integration of advanced analytical tools, methodologies, and a staunchly data-centric mindset. A thorough review of literature and contemporary practices illustrates how DDDM can revolutionize product development, boost user engagement, and enhance competitive positioning. This analysis aims to delineate the transformative impact of DDDM, underscoring its capacity to inform strategic decisions that propel product innovation and market adaptability.

# **The Digital Revolution and Data Proliferation**

The onset of the digital age has marked a paradigm shift in the accumulation and analysis of data, providing product managers with an unparalleled opportunity to refine their decision-making processes. The transition from relying solely on intuition and historical precedence to embracing empirical insights marks a significant evolution in product management practices. Data analytics now complements traditional decision-making approaches, imbuing them with an objective and informed perspective that was previously unattainable.

‍

![__wf_reserved_inherit](https://uploads-ssl.webflow.com/66c8e6fb686e109dc7fb27df/66cb884568f8cb7f1c51dfa0_660b1068c988ed0af50fe3c7_digital_data_growth_teal.png)

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In this new era, the role of a product manager transforms into that of a data strategist, where decisions are not just based on market trends and user feedback but are substantiated by data-driven insights. This evolution underscores the growing importance of leveraging data analytics to enhance decision-making processes, making it an indispensable tool in the product manager’s arsenal.

This section of the article will explore the impact of digital technologies on the proliferation of data and how this abundance of information can be harnessed to drive strategic decision-making in product management. By focusing on the synthesis of data analytics with traditional insights, it aims to illustrate the enhanced capability of product managers to navigate the complexities of the digital marketplace, ensuring that products meet user needs more effectively and efficiently.

## **The Academic and Empirical Foundations**

The recognition of data analytics as a cornerstone for strategic decision-making in product management has been steadily growing. Academic contributions, such as those by Prasad & Green (2015), have underscored the importance of aligning data analytics with overarching business objectives. These frameworks suggest a structured approach to data—emphasizing its systematic collection, rigorous analysis, and strategic application. Bose (2009) further elaborates on the opportunities and challenges presented by advanced analytics, highlighting its potential to reshape industrial management and data systems.

Empirical evidence, presented in studies like Smith (2018), illustrates a direct correlation between the adept use of data analytics and significant improvements in product innovation, market responsiveness, and customer satisfaction. Such findings advocate for a data-driven culture within organizations, suggesting that the strategic integration of data analytics into product management can serve as a key differentiator in today’s competitive landscape.

This body of literature forms the foundation for advocating a shift towards data-driven methodologies in product management. It provides a theoretical basis for understanding the transformative potential of DDDM, while empirical studies offer practical insights into its application and impact. The review not only highlights the current academic and professional consensus on the importance of DDDM but also sets the stage for exploring the methodologies that enable its effective implementation.

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![__wf_reserved_inherit](https://uploads-ssl.webflow.com/66c8e6fb686e109dc7fb27df/66cb884668f8cb7f1c51dfaa_660b108a2e04e6c6b1154dd6_google_project_oxygen_impact_extended.png)

*Caption: The chart illustrates the transformative impact of Google’s Project Oxygen, highlighting significant improvements in manager favorability scores, employee satisfaction, team productivity, and attrition rates due to data-driven decision-making. It encapsulates the broad organizational benefits of leveraging data for strategic improvements, showcasing enhanced leadership effectiveness, employee engagement, and operational efficiency.*

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## **Methodologies in Data-Driven Decision Making**

### **Analytical Tools and Platforms**

The landscape of data analytics offers a variety of tools and platforms designed to empower product managers with actionable insights. Google Analytics, for instance, provides a comprehensive view of user interactions on websites and mobile apps, facilitating a deeper understanding of consumer behavior. Mixpanel offers advanced event tracking and user segmentation capabilities, enabling product teams to analyze how different user groups engage with their products. Tableau, on the other hand, allows for the visualization of complex datasets, making it easier to identify trends and patterns that can inform strategic decisions.

These tools embody the technological advancements that have made DDDM not only feasible but also indispensable in modern product management. They offer a range of functionalities, from descriptive analytics, which help understand past behaviors, to predictive modeling, which forecasts future trends.

### **Techniques for Data Analysis**

Beyond the tools, the application of specific data analysis techniques plays a critical role in extracting valuable insights from data. Segmentation, for instance, allows managers to divide the market or customer base into distinct groups, enabling targeted strategies that are more likely to resonate with each segment’s unique preferences and behaviors. Trend analysis sheds light on the evolving dynamics of the market, highlighting shifts that could impact product positioning and user engagement. Cohort analysis offers a lens through which to view user actions over time, providing clarity on retention rates and the long-term value of different user groups.

Each of these techniques offers a different perspective on the data, revealing insights that might not be immediately apparent from a cursory overview. This part of the article will delve into how these methodologies can be applied in practice, with examples that illustrate their potential to uncover deep insights that inform product management decisions.

### **Fostering a Data-Driven Culture**

The success of DDDM extends beyond the technical capabilities to analyze data; it requires a fundamental cultural shift within the organization. A data-driven culture is characterized by a commitment to making decisions based on data, rather than solely on intuition or past experiences. This entails not only the adoption of tools and techniques but also the development of a mindset that values analytical rigor, critical questioning, and continuous learning.

Cultivating such a culture involves training teams to approach problems analytically, encouraging open sharing of data insights, and fostering an environment where data-driven experimentation is the norm. Davenport (2006) emphasizes the competitive advantage that such a culture offers, noting that organizations that compete on analytics are better positioned to innovate and adapt to market changes.

### **Challenges and Ethical Considerations**

Adopting a data-driven approach is not without its challenges. Issues of data quality and integrity can undermine the reliability of insights derived from analytics. Cognitive biases may skew interpretation, leading to flawed decision-making. Furthermore, the ethical use of data—especially with respect to consumer privacy and consent—has emerged as a critical consideration in the digital age.

Organizations must navigate these challenges carefully, implementing robust data governance policies to ensure the accuracy and security of their data. Balancing the insights gained from data analytics with the wisdom derived from experience and intuition can mitigate the risks of over-reliance on data. Moreover, adherence to ethical standards and regulatory requirements is paramount in building trust with consumers and safeguarding the organization’s reputation.

## **The Unquestionable Value of Data in Product Management**

In conclusion, the imperative of integrating data-driven decision-making within product management is clear. Through a detailed examination of the methodologies, tools, and cultural shifts required, this article has highlighted the transformative potential of DDDM in enhancing product innovation, market adaptability, and customer satisfaction. As product management continues to evolve in the digital age, the strategic application of data analytics will be critical in navigating the complexities of market dynamics and consumer preferences.

Organizations that embrace a data-centric approach will not only enhance their competitive positioning but also foster a culture of continuous improvement and innovation.

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## The Unbalanced Scale: How Information Asymmetry Shapes Product Management

*Published: 2023-12-16*
*Tags: Economics, Product Management*

URL: https://www.ahmadkarmi.com/insights/the-unbalanced-scale-how-information-asymmetry-shapes-product-management

> In the marketplace, knowledge is power, but not always evenly distributed. This information asymmetry, like a used car salesman's hidden secrets, can distort markets and leave users lost. But fear not, product managers! By understanding this power dynamic, you can craft features that bridge the knowledge gap, empower users, and build trust. Free trials, intuitive interfaces, and open communication are your weapons. So, embrace the asymmetry, design with purpose, and watch your products thrive in a balanced, user-powered marketplace.

In the bustling marketplace, knowledge is power, but rarely is it distributed equally. This fundamental principle is captured by the economic concept of information asymmetry, a situation where one party possesses significantly more information than the other. In the realm of product management, this imbalance can have profound implications for feature strategy and design.

Adverse Selection in the Used Car Market

Imagine a used car lot. The seller knows the car’s every creak and groan, the hidden rust beneath the shine, while the buyer navigates a sea of uncertainty. This is a classic example of adverse selection, a consequence of information asymmetry identified by Nobel laureate George Akerlof. In such scenarios, the market becomes flooded with “lemons,” unattractive products with hidden flaws, driving out the good ones.

Information asymmetry in the job market

But information asymmetry isn’t a one-way street. In the job market, it’s the candidate who wields the power. Their education, experience, and even a crisply tailored resume become signals, messages to potential employers about their capabilities. This is the domain of Michael Spence, another Nobel laureate who explored how signaling can bridge the information gap.

Information Asymmetry in Insurance

Joseph Stiglitz, the third laureate in this trio, delved into the world of insurance, where information asymmetry reigns supreme. Insurance companies face the challenge of identifying high-risk individuals, a process he termed “screening.” By offering varied premiums and deductibles, they incentivize low-risk individuals to participate, creating a balanced pool and reducing overall risk.

Bridging the Knowledge Gap in Product Management

So, how does this all translate to the product management battlefield? Consider the launch of a new fitness app. Users have limited knowledge of its effectiveness, while the developers possess the data and insights from beta testing. To bridge this gap, the app can offer free trials, detailed workout plans, and personalized progress reports – all signals of value that can mitigate user uncertainty.

Similarly, a complex software tool can employ user tutorials, onboarding guides, and intuitive interfaces to empower users and compensate for their lack of initial expertise. These features act as translators, demystifying the technology and building trust.

Information asymmetry isn’t just a theoretical concept; it’s a constant dance between product developers and users. By recognizing its presence and actively addressing it, product managers can craft features and designs that not only fulfill needs but also bridge the knowledge gap, fostering trust and engagement.

Here are some key takeaways along with more detailed explanations of the theory and benefits behind each one:

Identify the asymmetry

In any product’s ecosystem, there is often an information advantage held by either the user or the developer. This advantage can stem from the user’s lived experiences or the developer’s technical expertise. By identifying this information asymmetry, product managers can gain a deeper understanding of the specific knowledge gaps that exist. This knowledge can then be used to inform feature strategy and design decisions, ensuring that the product addresses the specific needs of users and provides a more balanced and equitable experience for all parties involved.

Leverage signaling

Signaling refers to the use of features, content, and design elements to communicate the value and effectiveness of a product. This is particularly important in situations where users have limited knowledge or understanding of the product. By strategically incorporating signaling elements, product managers can bridge the knowledge gap and enhance user perception. For example, offering free trials, detailed workout plans, and personalized progress reports in a fitness app can serve as signals of the app’s value and effectiveness, thereby increasing user confidence and engagement.

Empower users

Empowering users involves designing intuitive interfaces, offering tutorials, and providing personalized experiences that equip users with the knowledge they need to confidently navigate the product. Intuitive interfaces reduce the learning curve and make it easier for users to understand and interact with the product. Tutorials can provide step-by-step guidance, helping users to master complex features or functionalities. Personalized experiences cater to individual user preferences and needs, enhancing their overall product experience. By empowering users through these means, product managers can increase user satisfaction, reduce frustration, and ultimately drive higher user engagement and retention.

Build trust

Transparency and open communication are crucial in mitigating the power imbalance inherent in information asymmetry. By being transparent about the product, its features, and its limitations, product managers can build trust with users. Open communication channels, such as customer support or feedback mechanisms, enable users to express their concerns and provide valuable insights. This two-way communication fosters a sense of trust and collaboration between users and product managers, leading to stronger relationships and increased user loyalty.

In conclusion, information asymmetry is not a hurdle to be overcome, but a force to be understood and harnessed. By embracing its complexities and employing clever design strategies, product managers can create experiences that not only solve problems but also foster trust and empower users, ultimately leading to a more balanced and thriving marketplace.


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## Network Effects: The Economic Engine of Product Growth

*Published: 2023-12-10*
*Tags: Economics, Product Management, The Network Effect*

URL: https://www.ahmadkarmi.com/insights/network-effects-the-economic-engine-of-product-growth

> Unlock the secrets of network effects, the economic engine driving product success! Learn how to leverage this powerful force to attract users, build a strong community, and achieve exponential growth. Explore the economic theory, market dynamics, and practical strategies to create a product that thrives on connections. Start building your network empire today!

Network effects are a fundamental economic principle with profound implications for product growth. Understanding how they work can propel your product to success, while ignoring them can lead to stagnation or even failure. This blog post dives deeper into the economic theory side of network effects, equipping you with the knowledge to leverage this powerful force.

### Economic Underpinnings:

Network effects are essentially **demand-side economies of scale**. Unlike traditional economies of scale, where production costs decrease with higher output, network effects rely on the increasing value a product or service offers as more users join the network. This creates a positive feedback loop where user growth begets further user growth.

Economists have developed various models to explain network effects, including:

– **Metcalfe’s Law:** This simple yet powerful law states that the value of a network is proportional to the square of the number of connected users. In essence, the more users a network has, the more connections can be formed, exponentially increasing its value.

– **Katz-Shapiro Model:** This model analyzes direct and indirect network effects, including the role of compatibility and technological standards. It highlights the importance of critical mass, the point at which the network becomes self-sustaining due to the high value proposition for new users.

– **Two-Sided Network Models:** These models examine the dynamics of platforms with two distinct user groups, like buyers and sellers on a marketplace. They analyze how the actions of one group affect the other and how the platform can optimize its value proposition for both sides.

Understanding these models can provide valuable insights into the economic forces driving network effects and assist in developing strategies to maximize their impact on your product.

### Market Dynamics and Implications:

Network effects create unique market dynamics that differentiate them from traditional products. Here are some key implications:

– **High barriers to entry:** Established networks with large user bases can be difficult to compete with due to the inherent value they offer to new users. This can create barriers to entry for new players, leading to concentrated market landscapes.

– **Path dependence:** The initial choice of a network can have lasting effects due to the switching costs associated with moving to a different platform. This can further solidify the market position of established players.

– **Platform power:** Network owners often hold significant power due to their control over access to the network and its data. This influence can be used to extract value from users and limit competition.

These dynamics highlight the importance of careful strategic planning when developing network-driven products. Understanding the competitive landscape, potential barriers to entry, and the power dynamics within the network is crucial for success.

### Leveraging Network Effects for Product Growth:

While the economic theory behind network effects may seem complex, the practical applications are straightforward. Here are some key strategies to leverage network effects for your product:

– **Focus on user interactions:** Design features that encourage communication, collaboration, and sharing between users. This increases the value proposition of the network and attracts new users.

– **Create a strong community:** Foster a sense of belonging and shared interests among users. This can be achieved through forums, user-generated content, and community-driven initiatives.

– **Embrace open standards:** Facilitate compatibility with existing networks and technologies. This allows for easier integration and reduces switching costs for potential users.

– **Implement referral programs:** Incentivize existing users to invite their network, helping you reach new user segments and expand your reach.

– **Track key metrics:** Monitor user engagement, virality coefficient, and network density to understand the effectiveness of your strategies and measure progress towards critical mass.

By implementing these strategies and applying a solid understanding of the economic theory behind network effects, you can create a product that thrives on connections and achieves exponential growth.

### Conclusion:

Network effects are a powerful economic force shaping the success of modern products. By understanding their theoretical foundations, market dynamics, and practical applications, you can harness their potential to propel your product to new heights. Remember, network effects are about people and their connections. By focusing on creating value for your users and fostering a vibrant community, you can build a product that thrives on the power of network effects.


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## Best Practices for Effective Product Backlog Management

*Published: 2023-11-09*
*Tags: Agile, Backlog, Product Management*

URL: https://www.ahmadkarmi.com/insights/best-practices-for-effective-product-backlog-management

> Whether you're a seasoned product manager or a newcomer to backlog management, my straightforward insights will guide you towards delivering high-quality products. Don't miss out on the essential tips for mastering backlog and product management. Elevate your skills and stay ahead in the competitive product management landscape!

Managing a product backlog effectively is crucial for the success of any product development project. A well-maintained backlog ensures that the development team is working on the most valuable and important items, leading to the delivery of a high-quality product that meets user needs. In this blog post, we will explore some detailed and technical strategies for managing a product backlog.

### 1\. Definition and Prioritization

The first step in managing a product backlog is to define it clearly. This involves capturing all the desired features, enhancements, and bug fixes that need to be implemented in the product. Each item in the backlog should be well-defined and have a clear description of its functionality and purpose.

To ensure effective prioritization, it’s important to consider two key factors: business value and user needs. Items that have a high potential to generate revenue or provide a competitive advantage should be given higher priority. Additionally, user needs and feedback should be taken into consideration to ensure that the most valuable features are prioritized. This can be done through user research, surveys, and feedback from customer support channels.

### 2\. Collaborative Planning

Managing a product backlog is not a one-person job. It requires collaboration and input from various stakeholders. Involving stakeholders in backlog refinement and planning sessions can help gather different perspectives and ensure that all relevant requirements are captured.

Cross-functional teams should also be involved in the planning process. This includes representatives from different departments such as development, design, and QA. Gathering feedback and insights from these teams can help identify potential challenges and dependencies early on, leading to more accurate planning and estimation.

During collaborative planning sessions, it’s important to focus on creating a shared understanding of the backlog items. This can be done through techniques such as user story mapping or story slicing, where the team collaboratively breaks down the backlog items into smaller, actionable tasks. This not only helps with estimation and planning but also increases the team’s ownership and commitment to the backlog.

### 3\. Continuous Refinement

A product backlog is not a static document. It should be regularly reviewed and updated to reflect changing requirements and priorities. This process, known as backlog refinement, involves adding new items, removing or deprioritizing existing ones, and reevaluating the priority of items based on new information.

One important aspect of backlog refinement is breaking down large user stories into smaller, manageable tasks. This allows for better estimation and planning, as well as increased visibility into the progress of individual items. Breaking down user stories also enables the development team to work in an iterative and incremental manner, delivering value to users more frequently.

Continuous refinement also involves regularly reassessing the backlog priorities. As market conditions and user needs change, certain backlog items may become more or less important. By regularly reviewing and adjusting the backlog priorities, product managers can ensure that the team is always working on the most valuable and impactful items.

### 4\. Communication and Transparency

Effective communication is key to successful backlog management. The priorities of the backlog should be clearly communicated to the development team, ensuring that everyone is aligned and working towards the same goals. This can be done through regular meetings, such as sprint planning and daily stand-ups, where the team discusses the upcoming work and any changes to the backlog.

Transparency is also important when managing a product backlog. Sharing the backlog with stakeholders, such as product owners and executives, can help create a shared understanding of the product roadmap and its priorities. This transparency fosters trust and collaboration among all parties involved.

In addition to regular communication, documentation is also important for maintaining transparency. Keeping the backlog up to date and accessible to the entire team ensures that everyone has a clear understanding of the current state of the backlog and any changes that have been made.

### 5\. Metrics and Evaluation

To continuously improve backlog management, it is essential to track and analyze relevant metrics. Velocity, which measures the amount of work completed in a given time period, can provide insights into the team’s productivity and help with future planning. Lead time, which measures the time it takes for a user story to go from start to finish, can highlight bottlenecks and areas for improvement.

Regular evaluation of the effectiveness of backlog management strategies is also important. This can be done through retrospective meetings, where the team reflects on what worked well and what can be improved. Based on these evaluations, adjustments can be made to the backlog management process to optimize efficiency and effectiveness.

It’s also worth considering the use of agile project management tools to support backlog management. These tools provide features such as backlog grooming, sprint planning, and progress tracking, which can streamline the management process and improve collaboration among team members. A very popular tool is [Jira](http://www.jira.com) by Atlassian but it is by no means the only tool in the market. However, it seems to be the unwritten agreed upon benchmark for tools.

In conclusion, managing a product backlog requires a combination of technical skills, collaboration, and continuous improvement. By following these detailed strategies, product managers and development teams can ensure that the backlog is well-defined, prioritized, and regularly refined, leading to the successful delivery of a high-quality product.


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## 5 Ways To Transition Into Product Management

*Published: 2023-04-18*
*Tags: Career Advice, Product Management*

URL: https://www.ahmadkarmi.com/insights/5-ways-to-transition-into-product-management

> Are you ready to take the leap into product management but don't know where to start? Fear not, as I have compiled a list of 5 ways to help you transition into this exciting field. Whether you're coming from a technical background or simply looking for a new challenge, these tips will help guide you towards landing your transition into product management.

Are you ready to take the leap into product management but don’t know where to start? Fear not, as I have compiled a list of 5 ways to help you transition into this exciting field. Whether you’re coming from a technical background or simply looking for a new challenge, these tips will help guide you towards landing your transition into product management.

### Assess Your Skills And Experience

According to a recent study by LinkedIn, the role of product manager is one of the top 10 most promising careers in tech. Before diving headfirst into this exciting field, it’s essential to assess your skills and experience.

Assessing your skills and experience will help determine if product management is right for you. Start by evaluating your current skill set and identifying areas that need improvement. Do you have strong problem-solving abilities? Are you comfortable making decisions based on data analysis? These are just some of the qualities that make a successful product manager.

Next, consider any relevant work experience or transferable skills from previous roles. Have you worked closely with development teams before? Have you managed projects from start to finish? Highlighting these experiences can demonstrate how they relate to the responsibilities of a product manager.

It’s also important to recognize where there may be gaps in your knowledge or expertise. Take advantage of online courses, books, or workshops to gain new insights into product strategy, user research, and other key competencies.

In assessing your skills and experience, remember that no candidate will ever meet every requirement listed in a job description. Don’t let imposter syndrome hold you back – focus on building up what you do bring to the table while continuing to learn and grow as a professional.

### Network With Industry Professionals

One effective way of changing your career into product management is by networking with industry professionals. This step could potentially open doors for you, especially if you are looking to break into a new field or transition from a different role.

To start, it’s essential to attend events related to the product management profession and make connections with people who can offer guidance or even job opportunities in the future. It may also be helpful to join online communities such as LinkedIn groups that focus on product management. These platforms provide an excellent opportunity to engage with other professionals, ask questions, and gain insights about what it takes to succeed in this field.

Networking can help you build relationships with potential mentors or colleagues who can vouch for your skills and experience when applying for roles within their organizations. By establishing these professional relationships, you’ll have access to insider knowledge about the industry and its trends.

In addition, networking helps create a sense of belonging in a community of like-minded individuals committed to advancing their careers in product management. Engaging with others who share similar goals allows you to learn from them while building valuable connections that could lead to exciting career opportunities down the road.

By taking the time to network effectively, you will be able to position yourself as someone who is passionate about pursuing a career in product management while gaining exposure across various companies and industries.

### Gain Relevant Experience

If you’re looking for ways to transition into product management, gaining relevant experience is a crucial step. This can be done in many ways, including working as an intern or volunteer at a startup or tech company, freelancing as a consultant on projects related to product development, or even starting your own side project. By doing so, you’ll not only gain valuable skills but also demonstrate your passion and commitment to the field.

One way to gain experience is by participating in hackathons or other industry events where you can work alongside experts and learn about their processes. Additionally, consider taking online courses or attending workshops that focus on areas like user research, market analysis, and project management. These will help build your knowledge base and make you more competitive when applying for entry-level positions.

Ultimately, the key to gaining relevant experience is showing initiative and putting yourself out there. Don’t be afraid to reach out to people in the industry who can offer advice or mentorship opportunities. Network with others who share your interests through social media groups or local meetups. And most importantly, stay motivated and keep learning, because every new skill learned brings you one step closer towards achieving your goals.

### Get Certified

Product management certifications can provide you with the knowledge and skills necessary to succeed in this field. Not only do they show potential employers that you are serious about your career, but they also give you an edge over other candidates who don’t have any certification.

There are various types of product management certifications out there, such as Certified Product Manager (CPM), Agile Certified Product Manager and Product Owner (ACPMPO), and Pragmatic Marketing Certified (PMC). Each certification has its own curriculum and requirements, so it’s important to research which one is right for you based on your experience level and goals.

Moreover, obtaining a certification doesn’t just involve taking courses or exams; it often involves networking opportunities with other professionals in the field. This can help you build relationships within the industry, learn from experts in the field, and potentially find job leads.

In conclusion, if gaining relevant experience seems too daunting at first, pursuing a product management certification could be a good alternative way to prepare yourself for this role. It not only enhances your credibility by showcasing your proficiency in the subject matter but also provides numerous benefits beyond just academic achievement.

### Prepare For Interviews

First and foremost, it’s important to research the company and its products thoroughly. If you’re to transition into product management, you need to walk into that interview with a good sense of knowledge of their products. This will help you understand their mission and vision, as well as their target audience. Additionally, practice answering behavioral questions using the STAR method (situation, task, action, result) to showcase your skills and experience.

Another key aspect of interview preparation is learning how to communicate effectively with stakeholders. As a product manager, you’ll need to work closely with cross-functional teams including engineers, designers, salespeople and executives. Developing strong communication skills can make all the difference when it comes to building trust and achieving buy-in from these diverse groups.

Transitioning into product management may seem daunting at first glance but with proper interview preparation techniques like researching companies and practicing effective communication skills should increase your chances of success!

### Conclusion

Transitioning into a new career can be challenging, particularly in a fast-paced field like product management where best practices are constantly evolving. Although it may not be the right fit for everyone, if you’re interested in pursuing a career in product management, focusing on the points mentioned above can help set you up for success.

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## How to Build an Esports Team on a Shoestring Budget

*Published: 2023-04-16*
*Tags: esports*

URL: https://www.ahmadkarmi.com/insights/how-to-build-an-esports-team-on-a-shoestring-budget

Esports has become a rapidly growing industry worldwide, and many investors are pouring in lots of money to build successful teams. However, building a viral esports team can be done with little investment if you follow the right steps. In this guide, I will walk you through the key steps to help you build a successful and popular esports team.

### Define your team’s identity

Creating a strong team identity is the cornerstone of building a successful esports team. An effective team identity encompasses a variety of factors that set your team apart and make it instantly recognizable. You must first determine which game(s) your team will be playing. Then, create a name, logo (user upwork if you are not familiar with designing a logo), and branding collateral (website, social banners etc) that reflect your team’s personality and style.

Additionally, consider your team’s culture, values, and vision. What does your team stand for? What qualities do you want your players and staff to embody? Taking the time to thoughtfully define your team’s identity can make all the difference in building a viral esports team that stands out from the crowd.

Congratulations, you’ve built your esports team’s brand on a shoestring budget. But, what an esports brand really needs is some good talent. Some players to represent the brand and get the brand name out there.

### Recruit talented players

Recruiting talented players is a vital component of building a thriving esports team. The key to successful recruitment lies in finding players who not only possess outstanding gaming abilities but also exhibit positive attitudes and strong teamwork skills. The recruitment process can take various forms, such as hosting tryouts, scouting players from online gaming communities, or holding tournaments.

The goal is to identify players who are dedicated to improving their skills and committed to working collaboratively with others. By assembling a team of skilled and motivated players, you can create a dynamic and competitive team that is well-equipped to excel in the esports industry.

Recruiting the best of the best can be a challenge for any team, but it’s not impossible. If your team lacks funding, you may have to settle for decent players unless you stumble upon a diamond in the rough. However, don’t worry if that doesn’t happen. Your primary goal should be to compete locally and build your team’s brand presence. Winning major tournaments is great, but it’s not the only measure of success. Remember that content is king, so make sure your team can produce the best possible content in and out of competition, as well as behind the scenes. The more quality content, the better you can position your team’s brand.

### Build a strong social media presence

A strong social media presence is a foundational element in building a successful and viral esports team. It is imperative to create accounts on popular social media platforms such as Twitter, Instagram, and Facebook, and to cultivate a consistent and frequent posting schedule.

In addition to regular posts, creating and sharing engaging and high-quality content such as gameplay videos, livestreams, and blog posts is crucial to showcasing the personality, culture, and unique qualities of your team. Social media is an effective way to expand your team’s reach and fanbase, and ultimately achieve success in the competitive esports industry.

Building a strong following can be a powerful tool that can take you and your team to tournaments, in game streams, and pretty much anywhere you go. So, why not start sharing the value you have as a team with other people through social media? This way, you can create a fandom that will support you.

It’s important to think of the content you share and your team’s online activity as intellectual property and not just content. This means that the value in it becomes more evident. The more high-quality content you can create, the more valuable your intellectual property becomes. The good news is that intellectual property can be sold or licensed, which if set up correctly, can become a substantial revenue stream for you. With this revenue stream, you can fund trainings, new team members, expansions, and improve your chances of competing.

### Participate in tournaments and events

Participating in tournaments and events is a great way to gain exposure for your esports team. This is especially true if your team has a strong personality that shines on screen, or if they are the best at whatever title they are playing.

Most esports teams, especially those without major investment, start with online tournaments and events. Gradually working their way up to offline events, which can help build your team’s reputation and attract new fans.

It is important to prepare well for each event and showcase your team’s skills and unique personality. By performing well and exhibiting strong teamwork, your team can make a name for itself in the competitive esports industry. This could potentially lead to more lucrative opportunities in the future.

### Collaborate with other teams and content creators

Collaborating with other teams and content creators in the gaming industry is an effective strategy to expand your team’s audience and increase its reach. By partnering with like-minded individuals and organizations, you can create content that showcases your team’s unique qualities and personality, while also exposing your brand to new audiences. Collaborations can take many forms, such as participating in joint livestreams or creating videos together.

Remember how we discussed how content should be treated as intellectual property? Collaboration is a great way to create value for both parties. Many new esports teams and players try to stream and piggyback off of larger teams and/or streamers by building relationships with them.

In doing so, popular teams can provide fresh content to their viewers, while your team gains popularity and viewership from them. This increases the brand value of your esports brand if you provide value to the viewers. This does not have to be done as a full team, it could be connecting one of your players to join another team for a session.

These opportunities not only introduce your team to new fans and followers, but also provide valuable exposure for your brand and potentially lead to more lucrative opportunities in the future. Building strong relationships with other teams and content creators can help you establish your team as a respected and influential player in the competitive esports industry, and ultimately lead to greater success and recognition.

### Conclusion

In conclusion, building a viral esports team with little investment is possible if you follow the right steps. By defining your team’s identity, recruiting talented players, building a strong social media presence, participating in tournaments and events, and collaborating with other teams and content creators, you can build a successful and popular esports team. These steps will help you create a dynamic and competitive team that can stand out from the crowd, attract new fans, and achieve success in the competitive esports industry.

Remember that building a successful esports team is a continual process, and you should constantly seek to improve and adapt your strategies to keep up with the ever-evolving industry. With passion, dedication, and the right approach, you can create a viral esports team that will be a force to be reckoned with in the gaming world.

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## Why Gaming Influencers Are Key Players in the Future of Esports

*Published: 2023-04-10*
*Tags: esports, Gaming, Influencers, Media*

URL: https://www.ahmadkarmi.com/insights/why-gaming-influencers-are-key-players-in-the-future-of-esports

> Esports is a booming industry that has taken the world by storm. With millions of players and viewers worldwide, it has become a global phenomenon that shows no signs of slowing down anytime soon. But what is driving the success of this industry? Is it the games themselves, the competitive nature of esports, or is there something else at play? Read on and let's find out...

Esports is a booming industry that has taken the world by storm. With millions of players and viewers worldwide, it has become a global phenomenon that shows no signs of slowing down anytime soon. But what is driving the success of this industry? Is it the games themselves, the competitive nature of esports, or is there something else at play?

The Role of Gaming Influencers in Esports

The answer to this question may lie in the role of gaming influencers. These are individuals who have built a following on social media platforms by creating content related to video games. They can range from amateur players to professional esports athletes, and their content can include anything from gameplay footage, reviews, and tutorials to live streams, interviews, and commentary.

As esports has grown, so has the influence of gaming influencers. Many of the top esports teams and organizations have partnered with gaming influencers to promote their brands and events, leveraging their reach and engagement with their followers. The rise of esports has also created new opportunities for gaming influencers to monetize their content. Many influencers have turned their passion for gaming into full-time careers, earning income through sponsorships, advertising, merchandise sales, and donations from their fans.

As gaming influencers continue to create more intellectual property within the gaming and esports industry, their importance and influence will continue to grow. Their larger following naturally leads to a greater push and impact on their audience. A prime example of this is the influencer launch campaign that Apex Legends used to break the then-record numbers of the top game, Fortnite. It’s clear that gaming influencers are becoming increasingly essential for the success of esports, and their ability to create authentic content and build trust with their audience makes them incredibly valuable to brands and organizations looking to tap into this industry.

The Importance of Gaming Influencers in Esports Marketing

But the importance of gaming influencers goes beyond just marketing. Gaming influencers have become an indispensable part of esports, providing valuable insights and perspectives for fans and brands alike. They offer a more personal and authentic approach to promoting products and events, compared to traditional advertising methods. Gaming influencers have built trust and credibility with their audiences, making them effective advocates for brands and events.

Esports organizations and brands have also recognized the influence of gaming influencers in driving engagement and awareness. By partnering with gaming influencers, they can tap into their audiences and benefit from their reach and influence. Gaming influencers can create buzz and excitement around events, generating interest and driving ticket sales. They can also provide valuable feedback and insights on products and services, helping brands to improve their offerings and strengthen their relationships with customers.

The Impact of Gaming Influencers on Top Esports Games

The power and influence of gaming influencers are proportionally correlated with their presence in esports titles. The success and viewership of specific titles rely heavily on the eyes watching them. As gaming influencers continue to establish themselves within the esports industry, their ability to shape and influence the industry will only increase. In turn, brands and organizations will continue to rely on gaming influencers to promote their products and events, given their ability to develop strong relationships with their followers and provide authentic perspectives.

For example, Red Bull partnered with popular Fortnite player, Ninja, to host a Red Bull event at the Chicago Navy Pier in 2018. The event featured Ninja playing Fortnite on a giant screen while fans watched and participated in other gaming activities. The event was a massive success, with over 10,000 attendees and millions of online views. Ninja’s association with Red Bull helped to create an authentic and engaging experience for fans, driving awareness and engagement for the brand.

Gaming influencers have played a significant role in the success of top esports games like League of Legends and Fortnite, helping to build and sustain their communities. These influencers have built their audiences through engaging and entertaining content, showcasing their skills and personalities to their fans. They have also helped to promote the game and its esports events, generating buzz and excitement around tournaments like the World Championship and the Fortnite World Cup.

The Impact of Gaming Influencers on the Gaming Industry

Gaming influencers have emerged as key players in the gaming industry, wielding a significant impact that transcends beyond the realm of esports. These individuals have revolutionized game development by influencing the way games are created, marketed, and played. Through their vast audience reach, gaming influencers have established themselves as influential voices within the gaming community, providing valuable feedback and insights to game developers and publishers. Moreover, they have spurred innovation in the industry by pushing the boundaries of what is achievable in video games.

The Benefits of Working with Gaming Influencers

Gaming influencer agencies provide a plethora of benefits for brands and organizations seeking to capitalize on the power of gaming influencers. These agencies specialize in connecting brands with the most relevant gaming influencers for their target audiences, managing partnerships and collaborations, and measuring campaign effectiveness.

Partnering with gaming influencers, even with “micro” influencers in the gaming space, can be extremely lucrative. Therefore, it is crucial to effectively utilize campaigns and partnerships with influencers to reach the fiercely loyal and tech-savvy gaming community. Gaming influencers have access to this demographic that many brands struggle to reach, which tends to have higher disposable incomes and are more likely to spend money on gaming and related products.

To tap into these key demographics, it’s important to partner with gaming influencers in a way that is authentic and engaging, without going against their personal brand. By working correctly with gaming influencers, brands can benefit from their strong relationships with their followers, ultimately driving engagement and increasing their bottom line.

Gaming influencers are an indispensable part of the industry’s growth and evolution due to the level of loyalty observed in the esports community, which is comparable to the fanaticism seen in politics, religion, and traditional sports. Brands and organizations that recognize the power of gaming influencers and effectively utilize them to promote their products and events can benefit from their ability to create personal and authentic content that resonates with their followers.

The Future of Esports and Gaming Influencers

The future of esports looks bright, with continued growth and innovation in the industry. Gaming influencers will continue to be key players in the future of esports, providing valuable insights and perspectives for fans and brands alike. As esports becomes more mainstream, gaming influencers will play an even more important role in promoting the industry and driving engagement with audiences.

If you are passionate about gaming and aspire to become an esports gamer and influencer, there are numerous opportunities and resources available to help you get started. With dedication, hard work, and a bit of luck, you can build a successful career as a gaming influencer and help shape the future of esports.

As esports continues to boom, the importance of gaming influencers cannot be overstated. However, the challenge lies in accurately setting up key performance indicators (KPIs) and understanding the metrics behind esports fanaticism and video game influencers. This is particularly true for new forms of content like Twitch livestreams, where assessing value can be a difficult task for brands. The responsibility for this task falls on various parties, including service providers like Twitch, gaming influencers, marketers, and the industry as a whole. Nevertheless, as we progress further, brands are becoming increasingly impressed with the return on investment (ROI) provided by esports and gaming influencers.

Conclusion

The success of esports can be attributed to various factors, including the competitive nature of the games and the community’s passion for these games. However, the rise of gaming influencers has played a significant role in the industry’s growth and evolution. These individuals have established themselves as key players in the gaming industry, providing valuable feedback and insights to game developers and publishers.

Additionally, they have revolutionized game development by influencing the way games are created, marketed, and played. With their vast audience reach, gaming influencers have become influential voices within the gaming community, providing valuable insights and perspectives for fans and brands alike.

As esports continues to grow and evolve, gaming influencers will play an even more important role in promoting the industry and driving engagement with audiences. Therefore, it is crucial for brands and organizations to recognize the power of gaming influencers and effectively utilize them to promote their products and events, ultimately driving engagement and increasing their bottom line

It’s clear that gaming influencers are crucial for the future of esports. They offer a more personal and authentic approach to promoting products and events, and their influence extends far beyond just marketing. As esports continues to grow and evolve, gaming influencers will continue to play an even more important role in shaping the industry and driving engagement with audiences. So whether you’re a fan, a gamer, or a brand looking to tap into the power of esports, one thing is clear: gaming influencers are here to stay.

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## Navigating the Esports Industry: Tips for Professionals

*Published: 2023-04-06*
*Tags: esports*

URL: https://www.ahmadkarmi.com/insights/navigating-the-esports-industry-tips-for-professionals

> This article explores strategies and tips to help navigate the esports industry and build a successful career. To thrive in this fast-paced industry, it is essential to understand the various entities that comprise the esports ecosystem, identify your niche, build a robust professional network, stay up-to-date with industry trends, and develop vital professional skills.

#### Introduction

The esports industry has experienced immense growth in recent years, with revenues projected to reach **~**$1.87 billion by 2025 and a global audience of over **~**532 million viewers, according to a report by Newzoo. This growth has created many opportunities for professionals seeking a career in this dynamic and exciting field.

This article explores strategies and tips to help navigate the esports industry and build a successful career. To thrive in this fast-paced industry, it is essential to understand the various entities that comprise the esports ecosystem, identify your niche, build a robust professional network, stay up-to-date with industry trends, and develop vital professional skills.

#### The Landscape

Understanding the esports landscape is crucial in positioning oneself for success. The esports ecosystem comprises various entities, including game developers, teams, leagues, sponsors, and fans. Each entity plays a unique role in the industry, so it is crucial to have a comprehensive understanding of how they interact with one another. Furthermore, such knowledge can better position oneself in their expertise.

Identifying a niche is also essential in building a successful career in esports. With many opportunities available, carving out a specific area of expertise is crucial to differentiate oneself from the competition. For instance, one may specialize in game design, marketing, or event planning. Becoming an expert in the chosen field can make one a valuable asset to any organization.

#### Networking

As the esports industry becomes more competitive, professionals must establish connections and build relationships with others to gain a competitive edge. According to a survey by LinkedIn, networking is essential for job seekers in any industry, with 85% of all jobs being filled through networking. This is particularly true in the esports industry, as opportunities often arise through personal connections and relationships with others in the industry.

Attending industry events such as gaming conventions, esports tournaments, and industry conferences can help professionals meet others in the field and establish a strong network of contacts. Joining relevant organizations is another effective way to build a network and gain exposure to new opportunities. Many esports-related organizations and associations provide networking opportunities and resources for professionals in the industry. For example, the Esports Trade Association (ESTA) provides networking opportunities, education, and advocacy for professionals in the esports industry. The International Esports Federation (IESF) promotes the development of esports globally and provides opportunities for networking and collaboration among esports professionals.

Building a solid network can also lead to opportunities for collaboration and partnership in the esports industry. As the industry continues to grow, many professionals are exploring new avenues for partnerships and alliances to help grow their businesses and careers. For example, game developers may partner with teams and leagues to create recent esports events and tournaments, while marketing professionals may collaborate with influencers and content creators to promote their brands and products.

#### Current Trends & Updates

The esports industry is a constantly evolving and dynamic field, with new trends and innovations emerging at a rapid pace. To stay ahead of the competition and succeed in this industry, it is essential for professionals to stay current with the latest trends and technologies. One such trend that has gained significant momentum in recent years is the use of artificial intelligence (AI) and machine learning in esports.

According to a report by MarketsandMarkets, the esports market size for AI is projected to reach $797 million by 2023, indicating a growing interest in the potential applications of AI in esports. For instance, AI can be used to analyze gameplay data and provide insights to players, coaches, and teams to improve their performance. It can also be used in areas such as chat moderation, anti-cheat systems, and personalized recommendations for gamers.

To stay informed about the latest trends in AI and other emerging technologies in esports, it is crucial to follow industry news and engage with thought leaders in the field. Industry events, such as conferences and webinars, can also provide valuable insights and networking opportunities. By staying informed and engaged with these developments, professionals can position themselves as innovators and thought leaders in the industry.

It is also important to note that AI and other emerging technologies are not the only trends to keep an eye on in the esports industry. Other areas, such as mobile gaming, virtual reality, and blockchain technology, are also rapidly evolving and have the potential to transform the industry. By staying current with these trends, professionals can position themselves to take advantage of new opportunities as they arise.

#### Self Development

The esports industry is built on the foundation of teamwork, and the ability to work effectively with others is a crucial aspect of success in this field. Esports teams are made up of players with diverse backgrounds and skill sets, and the ability to collaborate effectively is essential to achieving team objectives. Strong communication skills are also critical in the esports industry, as effective communication helps players coordinate their actions, make strategic decisions, and respond quickly to changes in gameplay.

Moreover, the esports industry is fast-paced and constantly evolving, and the ability to adapt to changing circumstances is critical for success. In esports, game strategies and tactics can change rapidly, and the ability to adapt to these changes is key to staying competitive. Players who can remain flexible, learn new skills quickly, and adjust their gameplay strategies accordingly are more likely to thrive in this industry.

In addition to teamwork, communication, and adaptability, other important professional skills in the esports industry include problem-solving, leadership, and time management. Esports professionals are often required to make split-second decisions, solve complex problems, and manage their time effectively to achieve their objectives. By developing these skills, esports professionals can stand out from the competition and position themselves for long-term success in this exciting and dynamic field.

#### Balancing Passion With Professionalism

In the esports industry, passion for gaming is often a shared trait among professionals. While this passion can be an asset in many ways, it is important to balance it with professionalism. Maintaining a professional demeanor is crucial for building relationships, earning respect, and advancing in one’s career. This means communicating effectively and respectfully with colleagues, clients, and partners, and being mindful of the impact of one’s actions on others. In an industry where teamwork and collaboration are key, a professional and respectful attitude can help establish strong working relationships with others and contribute to the success of the projects one is involved in.

Additionally, as esports continues to gain mainstream acceptance and attract more investment, the industry is becoming more professionalized, with higher expectations for conduct and behavior. By balancing passion for gaming with a professional demeanor, professionals can position themselves as responsible and reliable professionals and build a positive reputation in the industry. This can open doors to new opportunities and help build a successful and fulfilling career in esports.

#### Conclusion

In conclusion, the esports industry presents many opportunities for professionals seeking a successful career. By understanding the esports landscape, identifying a niche, building a robust professional network, staying up-to-date with industry trends, developing critical professional skills, and balancing passion with professionalism, one can position themselves for long-term success in this exciting and rapidly evolving field. It is essential to stay curious, open-minded, and adaptable as the industry evolves, and never stop learning and growing as a professional.

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## The Roadmap to Success: Essential Traits and Qualities for COOs in the Years Ahead

*Published: 2023-03-29*
*Tags: Business, Strategy*

URL: https://www.ahmadkarmi.com/insights/the-roadmap-to-success-essential-traits-and-qualities-for-coos-in-the-years-ahead

> The Chief Operating Officer (COO) role has evolved significantly in recent times. From being responsible for the day-to-day operations of an organisation, the COO now plays a critical role in driving strategic growth and innovation. In today's post-pandemic world, COOs must be agile, tech-savvy, and possess various skills to navigate the ever-changing business landscape. This article will discuss the essential traits and qualities that COOs need to succeed in the years ahead.

The Chief Operating Officer (COO) role has evolved significantly in recent times. From being responsible for the day-to-day operations of an organisation, the COO now plays a critical role in driving strategic growth and innovation. In today’s post-pandemic world, COOs must be agile, tech-savvy, and possess various skills to navigate the ever-changing business landscape. This article will discuss the essential traits and qualities that COOs need to succeed in the years ahead.

Essential Traits and Qualities of a Successful COO:

Strategic Thinking and Decision-Making Skills

COOs are responsible for executing the strategic vision of the organisation. They must possess strong strategic thinking and decision-making skills to do this effectively. COOs must be able to analyse complex data, identify key trends, and develop actionable plans that support the organisation’s goals. They must also be able to make tough decisions quickly and confidently, even in high-pressure situations.

An effective COO must be able to collaborate with other senior management team members to ensure that their decisions align with the organisation’s overall strategy. They must also possess excellent communication skills to ensure that everyone in the organisation knows the direction of the company.

Effective Communication and Collaboration

A successful COO must possess exceptional communication skills to effectively convey intricate concepts with clarity and brevity. Furthermore, they must possess the ability to connect with a diverse range of stakeholders, such as employees, customers, and investors, to ensure that everyone is aligned with the organization’s objectives. 

COOs must also be adept at collaboration, working closely with other members of the senior management team to execute the company’s strategy. Developing strong relationships with executives, managers, and employees is critical in uniting the team towards shared goals.

Agility and Adaptability

In today’s fast-paced business environment, COOs must be agile and adaptable. They must respond quickly to changing market conditions, customer needs, and emerging technologies. As a result, COOs must be able to pivot quickly and make decisions to help the organisation stay ahead of the competition.

COOs must be comfortable with change and be able to lead their teams through periods of uncertainty. In addition, they must be able to inspire their teams to embrace change and see it as an opportunity for growth and innovation.

Financial Acumen

The position of a COO demands a robust comprehension of finance to guarantee the financial stability of the organization. It is imperative for COOs to possess the ability to scrutinize financial data, discern opportunities for enhancement, and devise effective tactics to augment the organization’s fiscal performance. 

The role necessitates expertise in budget management, cost control, and the capacity to make well-informed decisions that will facilitate the achievement of financial targets. Moreover, COOs must adroitly balance the financial aspects of the business with the broader strategic goals of the organization.

Leadership and Team Management

COOs must be influential leaders who inspire their teams to achieve great things. They must create a positive work culture that fosters collaboration, innovation, and continuous improvement. COOs must also be able to manage teams effectively, provide feedback, and develop their employees’ skills.

Leadership is not just about managing people but also about leading by example. COOs must be able to set the tone for the organisation and demonstrate the behaviours and values that they expect from their teams.

Technological Proficiency

In the current business landscape, technological agility is an indispensable trait for a COO. It is essential for COOs to stay abreast of the fast-paced technological advancements and identify emerging technologies that can provide an edge over the competition. 

Their role involves formulating effective strategies for implementing such technologies seamlessly. COOs must be adept at managing technology projects, ensuring that they are completed within budget and timelines while catering to the organization’s needs. Moreover, it is critical for them to mitigate the risks that accompany technology and ensure that the organization’s data and systems remain secure.

Industry-Specific Knowledge

To excel in their role, COOs must possess a deep understanding of their industry’s nuances and intricacies, allowing them to make well-informed decisions that align with the organization’s objectives. Staying apprised of the latest trends and advancements in the industry is essential for COOs to apply their knowledge effectively. 

They must possess the acumen to recognize opportunities for growth and innovation within their industry and devise tactics to leverage them optimally. Furthermore, managing the risks inherent to the industry proficiently is critical to their success.

Continuous Learning and Development

COOs must be committed to continuous learning and development to ensure that they can stay ahead of the competition. They must be willing to invest time and resources in their development and their teams.

They must be willing to learn new skills, take on new challenges, and embrace new ideas. In addition, they must be open to feedback and willing to change their approach when necessary.

Challenges Faced by COOs in the Years Ahead

COOs confront a multitude of challenges that demand their attention and strategic thinking in the coming years. The rapidly evolving technological landscape, intensifying competition, and shifting customer expectations are but a few of the trials that COOs must deftly navigate. 

In this regard, COOs must exhibit a nimble adaptability to changing market conditions, adeptly discern emerging trends, and ingeniously devise innovative strategies to outpace the competition. 

Moreover, they must dexterously handle the risks that attend technological change while safeguarding the organization’s data and systems, which are increasingly vital to modern business operations. COOs must also effectively manage their teams, inspire a culture of innovation, and foster a positive work environment that aligns with the organization’s goals. 

Additionally, they must successfully navigate change management, ensuring that their teams are prepared to flexibly adapt to novel working methods. Ultimately, the success of COOs lies in their ability to lead their teams, prioritize strategic initiatives, and adeptly handle challenges as they arise, to drive the organization towards its objectives.

Conclusion and Key Takeaways

In conclusion, the role of the COO has evolved significantly in recent times. As a result, COOs must possess various skills and qualities to be effective in their role, including strategic thinking, effective communication, agility, financial acumen, leadership, technological proficiency, industry-specific knowledge, and a commitment to continuous learning and development.

The challenges faced by COOs in the years ahead are significant. However, they can navigate these challenges and drive growth and innovation within their organisations with the right skills and qualities. COOs must be adaptable, forward-thinking, and committed to achieving their organisation’s goals. With these traits and qualities, COOs can be successful in the years ahead.

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## Mastering Product Management: Your Ultimate Beginner’s Guide

*Published: 2023-03-29*
*Tags: Product Management*

URL: https://www.ahmadkarmi.com/insights/mastering-product-management-your-ultimate-beginners-guide

As a product management professional, I have been fortunate to have worked with some of the brightest minds in the industry. Product management is an exciting field that is constantly evolving. In this guide, I will be sharing my insights on what product management is, what product managers do, and how you can become a product manager. I will also be sharing some key principles and frameworks that you can use to excel in this field.

### Introduction to Product Management

Product management is a discipline that focuses on creating and managing products that meet the needs of customers. A product manager is responsible for the entire product lifecycle from ideation to launch and beyond. Product management involves working closely with cross-functional teams such as engineering, design, sales, and marketing to ensure that the product meets the needs of customers and the business.

### What is Product Management?

Product management is the process of identifying, developing, and launching products that meet the needs of customers. It involves understanding customer needs, conducting market research, developing product roadmaps, and working with cross-functional teams to ensure that the product meets the needs of customers and the business.

### What Does a Product Manager Do?

A product manager is responsible for the entire product lifecycle from ideation to launch and beyond. They work closely with cross-functional teams such as engineering, design, sales, and marketing to ensure that the product meets the needs of customers and the business. Product managers are responsible for setting product strategy, developing product roadmaps, and prioritizing features based on customer and business needs.

### Product Manager’s Job Description

The job description of a product manager can vary depending on the company and the product. However, some common responsibilities that a product manager may have include:

– Conducting market research to understand customer needs and identify market opportunities

– Developing product roadmaps that align with the company’s overall strategy

– Prioritizing features based on customer and business needs

– Working with cross-functional teams such as engineering, design, sales, and marketing to ensure that the product meets the needs of customers and the business

– Defining and tracking key performance indicators (KPIs) to measure the success of the product

– Communicating product updates and changes to stakeholders

### What is Product Management? – Key Principles and Frameworks

To excel in product management, it is important to understand some key principles and frameworks. Here are some of the most important ones:

#### **The Lean Startup**

The Lean Startup is a methodology that was developed by Eric Ries. It involves creating a minimum viable product (MVP) and testing it with customers to validate assumptions before investing significant resources into development. This approach helps to reduce the risk of failure and ensures that the product meets the needs of customers.

#### **Agile Development**

Agile development is a methodology that is commonly used in software development. It involves breaking down development into small, iterative cycles and working closely with customers to ensure that the product meets their needs. This approach helps to ensure that the product is delivered on time and within budget.

#### **Design Thinking**

Design thinking is a human-centered approach to product development. It involves understanding the needs and motivations of customers and using that information to design products that meet their needs. This approach helps to ensure that the product is user-friendly and meets the needs of customers.

### How to Become a Product Manager

If you are interested in becoming a product manager, there are several steps that you can take:

#### **Step 1: Gain Relevant Experience**

To become a product manager, it is important to have relevant experience in areas such as product development, marketing, or engineering. You can gain this experience by working in these areas or by taking courses or certifications.

#### **Step 2: Learn Product Management Skills**

Product management requires a broad range of skills such as market research, product strategy, and project management. You can learn these skills by taking courses or certifications, attending conferences or networking events, and working with experienced product managers.

#### **Step 3: Build a Product Portfolio**

Building a product portfolio is a great way to demonstrate your product management skills to potential employers. You can do this by working on side projects or by contributing to open-source projects.

#### **Step 4: Get Certified**

Getting certified in product management is a great way to demonstrate your knowledge and skills to potential employers. There are several certifications available such as the Certified Product Manager (CPM) and the Agile Certified Product Manager (ACPM).

### Product Manager Skills and Qualifications

Product management requires a broad range of skills and qualifications, including:

– Market research

– Product strategy

– Project management

– Analytical skills

– Communication skills

– Leadership skills

– Technical knowledge

### Product Manager Career Path

Product management is a great career path for anyone who is interested in creating and managing products. As a product manager, you can expect to start as an associate product manager or product manager and work your way up to senior product manager, director of product management, or even VP of product management.

### Tools and Resources for Product Managers

As a product manager, there are several tools and resources that you can use to excel in your role. Here are some of the most important ones:

– Product management software such as Jira, Trello, or Asana

– Analytics tools such as Google Analytics or Mixpanel

– Customer feedback tools such as SurveyMonkey or Qualtrics

– Product management blogs and podcasts such as Product Collective, Product School or This is Product Management

### Conclusion

Product management is an exciting field that requires a broad range of skills and qualifications. To excel in this field, it is important to understand the key principles and frameworks, gain relevant experience, learn product management skills, and build a product portfolio. With the right skills and qualifications, you can build a successful career in product management and help to create products that meet the needs of customers and the business.

If you’re interested in learning more about product management, I recommend checking out some of the resources mentioned in this guide. You can also reach out to experienced product managers or attend networking events to learn more about this exciting field. I am available for independent consulting and can help you get your product team set up. Contact me using the box below.


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## Airlines, IATA, & The OpenAPI Hub

*Published: 2023-03-28*
*Tags: Digital Transformation, Innovation*

URL: https://www.ahmadkarmi.com/insights/airlines-iata-the-openapi-hub

> Welcome to the world of aviation, where digital transformation is revolutionising how airlines and their partners connect. Like any transformation, the challenges and opportunities come with adopting it. So how are they planning to do so in the upcoming year? It seems the answer lies in APIs.

### Introduction

Welcome to the world of aviation, where digital transformation is revolutionising how airlines and their partners connect. Like any transformation, the challenges and opportunities come with adopting it. So how are they planning to do so in the upcoming year? It seems the answer lies in APIs.

Airlines aim to maximise interoperability by adopting modern API technology and conforming to open API standards while retaining complete data control. The Open API Hub, developed in partnership with RapidAPI, acts as a matchmaker, promoting the adoption of industry standards and facilitating the discovery and onboarding of APIs. This innovative technology brings greater transparency to industry standards, promotes innovation, and accelerates the discovery of new solutions, leading the way to an open data ecosystem.

### Wait, APIs? OpenAPIs? What are you on about?

An API (Application Programming Interface) is a set of protocols, tools, and routines for building software applications that allow different systems to communicate and exchange information. APIs define how one software application can interact with another, allowing developers to use functionality or data from another application without knowing the underlying code. APIs can be used to integrate different services, automate workflows, and improve the functionality of software applications, making them a crucial part of modern software development.

The Open API Hub is a central platform that will allow any party in the aviation industry (well, any industry, really), including customers, sellers, airports, and airlines, to connect seamlessly and securely with any other party, thus enabling airlines to take back control of their data and run their businesses autonomously.

### The Opportunities & Challenges

The current challenge for the aviation industry is the excessive reliance on third-party communication networks, which limits the ability of airlines and other value chain participants to control the use of their data and dynamically adjust processes when changes or disruptions occur. The Open API Hub will enable airlines to adopt modern API technology and conform to open API standards, maximising communication while retaining complete data control.

The Open API Hub will act as a matchmaker, making it easier for airlines and their partners to discover APIs and establish connections. In addition, the hub will enable the discoverability and promotion of industry APIs, facilitate API onboarding, and promote the adoption of the industry’s Open API standards and programs.

IATA and the OpenAPI Hub have partnered to provide APIs accessible to all airlines. By setting these standards, IATA aims to improve the efficiency and effectiveness of the aviation industry by streamlining processes and facilitating better communication and collaboration between airlines, airports, and other industry stakeholders. It’s evident, to say, a well-needed push to the industry.

However, adopting open API standards will consume considerable airline resources regarding time, money, and expertise.

Nevertheless, the savings and return on investment will be evident once adopted. By adopting these APIs, airlines will have full discoverability and collaboration, even with partners outside the industry. The efficiencies this brings will be explicit.

Moreover, the hub will bring greater transparency to industry standards and programs, such as Modern Airline Retailing, ONE Record, New Distribution Capability certification, and Open Air certification.

Previously, the limited ability to partner with legacy systems also curtailed the potential for groundbreaking ideas from more innovative start-up partners in the industry. The hub should address this issue by enabling start-ups and established organisations to bring fresh concepts to the industry and easily connect with airlines.

### The Conclusion

This collaboration and hub is a milestone in building an open data ecosystem. It is an enabler for even more innovation and will help to accelerate the discovery of new solutions, even in the simple implementations of processes such as the passenger ticketing experience will see benefits.

It will be interesting to see how airlines use this hub and these open APIs to innovate their businesses. I, for one, will keep an eye out.


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## Common product management mistakes to avoid

*Published: 2023-03-13*
*Tags: Product Management*

URL: https://www.ahmadkarmi.com/insights/common-product-management-mistakes-to-avoid

Product management is a complex and challenging field requiring technical, strategic, and interpersonal skills. Whether a seasoned product manager or new to the area, you must know common mistakes derailing your product development efforts. This guide will explore some of the most common product management mistakes and offer tips on avoiding them.

### **Failing to Define the Problem**

One of product managers’ most common mistakes is rushing into solution mode without clearly defining the problem they are trying to solve. Creating a successful solution that meets customer needs is difficult without a clear understanding of the problem. Therefore, it’s essential to take the time to research and gather data to define the problem before moving forward with product development.

### **Neglecting User Research**

Product managers who ignore user research risk creating products that do not meet customer needs. User research should be a critical part of the product development process and be used to inform every aspect of the product, from design to functionality. Conducting user research can help you better understand your customers and their needs, which can lead to more successful product launches.

### **Not Prioritizing Features**

Product managers often make the mistake of including too many features in a product, which can lead to a bloated and confusing user experience. Instead, it’s essential to prioritize features based on customer needs and business goals. Prioritizing features can help ensure your product is easy to use, solves a real problem, and meets customer needs.

### **Effectively Failing to Communicate**

Product managers must communicate effectively with stakeholders, including developers, designers, executives, and customers. Failure to communicate effectively can lead to misunderstandings and delays in product development. It’s essential to be clear and concise in your communications, use data to support your arguments, and actively listen to feedback from others.

### **Ignoring Metrics**

Product managers who ignore metrics risk launching products that do not meet business goals or customer needs. Metrics should be used to track product performance, user engagement, and customer satisfaction. It’s essential to regularly review metrics and use them to inform product decisions and improvements.

### **Failing to Iterate**

Product development is an iterative process, and product managers who fail to iterate risk launching products that are not fully developed or do not meet customer needs. Gathering user feedback, reviewing metrics, and repeating based on the insights gained is essential. Iteration can help ensure your product meets customer needs and is successful in the market.

### **Not Aligning with Business Goals**

Product managers who do not align their product development efforts with business goals risk creating products that do not drive business success. Therefore, it’s essential to clearly understand the company’s goals and ensure that product development efforts align with those goals. Aligning with business goals can help ensure your product’s market success and drive business growth.

### **In Conclusion**

Product management is a challenging and complex field requiring technical, strategic, and interpersonal skills.

By avoiding these common product management mistakes, you can increase your chances of developing successful products that meet customer needs and drive business growth. Remember to take the time to define the problem, prioritize features, conduct user research, communicate effectively, use metrics to inform decisions, iterate based on feedback, and align with business goals. Doing so can set your product development efforts up for success.

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## The Significance of Product Documentation and Knowledge Sharing in Product Management

*Published: 2023-03-12*
*Tags: Product Management*

URL: https://www.ahmadkarmi.com/insights/the-significance-of-product-documentation

> In product management, the significance of product documentation and knowledge sharing cannot be overstated. Product documentation refers to written materials that detail the product, such as technical specifications, product requirements, release notes, and user manuals.

Introduction

In product management, the significance of product documentation and knowledge sharing cannot be overstated. Product documentation refers to written materials that detail the product, such as technical specifications, product requirements, release notes, and user manuals. On the other hand, knowledge sharing entails sharing information and expertise among different teams and stakeholders to ensure everyone has a shared understanding of the product. This paper delves into the crucial importance of product documentation and knowledge sharing in product management at a more profound level.

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Benefits of Product Documentation

Ensures Consistency: Consistency is a vital aspect of product development. Documenting the product helps ensure everyone involved in the product development process shares a common understanding of the product. It ensures the product is consistent in terms of quality, functionality, and user experience. Inconsistent product documentation can lead to confusion, mistakes, and delays in the development process. Documentation should be extensive and cover everything from the user research and business impact/goals to the UX/UI and engineering notes.

Facilitates Collaboration: Successful product development requires effective collaboration between team members. Product documentation makes it easier for team members to collaborate, delegate tasks, track progress, and ensure that the development process goes smoothly. When team members have access to the same information, they can make informed decisions and work towards the same objectives.

Saves Time and Effort: Developing a product can be time-consuming and resource-intensive. Documentation saves time and effort by avoiding duplication of work, helping to avoid mistakes that may lead to wasted resources and time. When documentation is clear and comprehensive, it can also help team members to work more efficiently. They may be able to find the notes and clarity they’re looking for, instantly, and on the spot within the documentation if completed correctly as it acts as a single point of truth. A single truth that allows all to be on the same page during the product’s lifespan.

Benefits of Knowledge Sharing

Improves Productivity: Productivity is a key metric in product development. Knowledge sharing helps in improving productivity by making sure team members are aware of each other’s tasks and responsibilities, preventing delays, and ensuring that everyone is working towards the same goals. When team members understand the product’s requirements and objectives, they can work more efficiently towards achieving them.

Encourages Innovation: Innovation is crucial in product development. Knowledge sharing encourages innovation by sharing information and ideas that may lead to new products or features, identifying new opportunities and market trends. When team members share their expertise, knowledge, and insights, they can come up with new and innovative solutions.

Reduces Risks: Product development involves risks, such as project delays, low-quality products, and unexpected costs. Knowledge sharing helps to mitigate these risks by ensuring that everyone involved in the product development process has the same level of understanding. When team members have access to the same information, they can identify and address potential risks early on in the development process.

Best Practices for Product Documentation and Knowledge Sharing

Create a Knowledge Base: Creating a central repository for all product documentation and knowledge sharing helps ensure that everyone has access to the same information. A knowledge base can also help team members to share information and collaborate more efficiently.

Use Simple Language: When creating product documentation, use simple language that everyone can understand. Avoid technical jargon that may be difficult for non-technical stakeholders to comprehend. Clear and concise documentation can help team members to make informed decisions and work more efficiently.

Provide Regular Updates: Product documentation should be updated regularly to ensure that it remains accurate and up-to-date. When product documentation is outdated or incomplete, it can lead to misunderstandings, mistakes, and delays.

Encourage Communication: Communication is key to effective knowledge sharing. Encourage communication and collaboration between team members to promote knowledge sharing. This can be achieved through regular meetings, an online chat system, or other communication channels.

Conclusion

Product documentation and knowledge sharing are indispensable components of successful product development. They ensure that everyone involved in the process shares a common understanding of the product, which is essential for its success. Product documentation helps ensure consistency, facilitates collaboration, and saves time and effort. Knowledge sharing improves productivity, encourages innovation, and reduces risks. By following best practices for documentation and knowledge sharing, teams can work together more efficiently and effectively, leading to better products and greater success.


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## Kanban Framework Overview

*Published: 2022-12-05*
*Tags: Agile, Kanban, Product Management*

URL: https://www.ahmadkarmi.com/insights/kanban-framework-overview

> The Kanban framework was developed by Taiichi Ohno, an industrial engineer at Toyota, in the 1950s. The word "kanban" means "visual signal" or "card" in Japanese, and the system was designed to help Toyota manage its production line more effectively. The Kanban framework is based on the principle of continuous improvement, which is a key philosophy of the Toyota Production System. The Kanban framework has since been adopted by many other organizations, both in manufacturing and in other industries, as a way to improve efficiency and optimize workflows.

Kanban is a framework that helps organizations optimize their workflows and improve their overall efficiency. Today, we will discuss the importance of Kanban and how it can help organizations streamline their processes and improve their bottom line. 

The Kanban framework was developed by Taiichi Ohno, an industrial engineer at Toyota, in the 1950s. The word “kanban” means “visual signal” or “card” in Japanese, and the system was designed to help Toyota manage its production line more effectively. 

The Kanban framework is based on the principle of continuous improvement, which is a key philosophy of the Toyota Production System. The Kanban framework has since been adopted by many other organizations, both in manufacturing and in other industries, as a way to improve efficiency and optimize workflows. 

The Kanban framework is particularly well-suited to agile software development, as it helps to visualization workflows and track progress. There are three main principles of the Kanban framework: 

1. Visualize your work: In order to optimize your workflow, it is important to be able to see what work is being done and where bottlenecks are occurring. The Kanban framework uses a Kanban board to visualize work in progress and identify areas for improvement. 

2. Limit work in progress: One of the key reasons for bottlenecks in workflows is too much work in progress. The Kanban framework helps to limit work in progress by using WIP (work in progress) limits. By limiting the amount of work that is in progress at any one time, organizations can improve overall efficiency and throughput. 

3. Continuously improve: The Kanban framework is based on the principle of continuous improvement. This means that organizations should constantly be looking for ways to improve their workflows.

The Kanban framework provides a structured approach to identifying and implementing improvements. The Kanban framework can be used in a variety of ways to improve efficiency and optimize workflows. Some of the most common applications of Kanban include: 

1. Agile software development: The Kanban framework is well-suited to agile software development, as it helps to visualization workflows and track progress. 

2. Manufacturing: The Kanban framework was originally developed for use in manufacturing and is still widely used in this industry. 

3. Project management: The Kanban framework can also be used in project management to help visualization of workflows and track progress. With this method, it also helps define bottlenecks and critical paths (even though the time frame measurements don’t exactly work the same as traditional project management methods, inferences can be made).

4. Service industries: The Kanban framework is also applicable to service industries, such as healthcare, where it can help to power through processes and improve patient care.

The Kanban framework is a powerful tool that can help organizations to improve efficiency and boost their internal workflows and processes. In this post, we have discussed the importance of Kanban and some of the ways in which it can be used to improve organizational performance.

Do you utilise Kanban at your organization or are you more inclined to more strict methodologies such as SCRUM? Feel free to know let me know by contacting me, would love to extend this conversation to a 1 to 1 with you.


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## Customer Acquisition Cost (CAC) Explained

*Published: 2022-11-03*
*Tags: Customer Acquisition*

URL: https://www.ahmadkarmi.com/insights/customer-acquisition-cost-cac-explained

There’s no shortage of advice on how to acquire new customers. But what’s often missing from these conversations is an understanding of customer acquisition costs (CAC). CAC is a metric that tells you how much it costs to acquire a new customer. More specifically, it’s the total amount of money you spend on marketing and sales divided by the number of new customers you acquire in a given period of time.

For example, let’s say you spend $100,000 on marketing and sales over the course of a year and you acquire 100 new customers. Your CAC would be $1,000.CAC is an important metric because it allows you to track the efficiency of your customer acquisition efforts. If your CAC is too high, it means you’re spending too much to acquire new customers and you won’t be able to achieve long-term profitability.

There are a number of factors that go into calculating CAC, including:

• Sales and marketing expenses: This includes salaries, commissions, advertising, lead generation, etc.

• Customer lifetime value: This is the total value of a customer over the course of their relationship with your business.

• Churn rate: This is the percentage of customers who cancel or do not renew their subscription.To calculate CAC, you simply divide your total sales and marketing expenses by the number of new customers you acquire in a given period of time.For example, if you spend $100,000 on sales and marketing and acquire 100 new customers, your CAC would be $1,000.There are a number of ways to reduce your CAC, including:

• Improving your marketing efforts: This could involve doing things like increasing your advertising spend, improving your website, or generating more word-of-mouth buzz.

• Increasing your prices: This will obviously only work if you’re not already maxed out on price. But if you have some wiggle room, raising your prices can help offset some of the costs associated with acquiring new customers.

• Reducing your churn rate: This is the percentage of customers who cancel or do not renew their subscription. Reducing churn will obviously require some investigation to figure out what’s causing it in the first place. But once you know, you can put measures in place to reduce it.

Customer acquisition costs can be a tricky metric to wrap your head around, but it’s important to have a firm understanding of it if you want to be successful in business. By taking the time to calculate your CAC and track it over time, you’ll be in a much better position to make informed decisions about your sales and marketing efforts.


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## Pareto’s Principle And Becoming A More Effective Product Manager

*Published: 2020-12-01*
*Tags: Product Management*

URL: https://www.ahmadkarmi.com/insights/paretos-principle-and-becoming-a-more-effective-product-manager

> Once upon a time, there was a man by the name of Vilfredo Pareto. Mr Pareto, considered one of the pioneers who developed microeconomics, needs no further introduction. If you are reading this, you have probably had some background in business and economics. If you do not, there's no reason to worry. I'll give you a recap about one of his most popular principles and one of the most relevant in today's fast-paced world.

Once upon a time, there was a man by the name of Vilfredo Pareto. Mr Pareto, considered one of the pioneers who developed microeconomics, needs no further introduction. If you are reading this, you have probably had some background in business and economics. If you do not, there’s no reason to worry. I’ll give you a recap about one of his most popular principles and one of the most relevant in today’s fast-paced world.

### The 80/20 Principle.

Pareto, although an economist, also had a green thumb. He began to connect a simple fact about peas that would change the way we look at productivity and efficiency in business (and in life).

He realized that approximately 80% of his peas during the harvest season came from 20% of his seeds. This fundamental realization helped him understand much broader topics by applying the simple principle that most of your consequences (roughly 80%) result from only a minority of your causes (approximately 20%).

Management consultants quickly took to rewording and remarketing the principle under his name, with the same ideology but applied to whatever they could get their hands on (as consultants will do).

– “80% of revenue is derived from 20% of our clients.”

– “80% of wealth is owned by 20% of the population.”

– “80% of our goals come from 20% of our strikers.”

– “80% of our sales come from 20% of our salesmen.”

– “80% of our negative customer feedback are filed by 20% of our customers.”

Do you get my drift? It is not a golden rule of thumb, of course. You may skew specific ratios in certain scenarios (90/10, 70/30, but generally 80/20 is a sweet spot for most strategies +/- a few percentage points).

### But I’m a Product Manager…

How is this useful to product managers? I’m glad you asked. We can break this down into several points, which revolve around the actual product and product management process.

##### The Planning Process:

As a product manager, you’ll need to have a plan and strategy. So it’s crucial that in your plan, you have everything prioritized. Remember 80/20? Well, 80% of your results are going to be from 20% of your efforts. So make sure you prioritize that essential 20%. With experience, you will see what items in your plan will be a part of that 20%. The remaining 80% of your plan reorganized and/or delegated as necessary (or even drop some in the dreaded backlog – with the intention of doing it later, of course). The first reaction to the word delegation seems to be “that’s lazy”, but it’s not. Remember that you’re trying to invest your focus and time on the 20% that will get you 80% of your result.

##### Risky Business:

I do realize that you have stakeholders and bosses breathing down your neck as a product manager. I do understand that everyone feels that their input to the product is the most important. However, every stakeholder’s request comes in with its own set of risks. How do we mitigate? Understanding that 20% of your risks will result in 80% of your product delays is the first step. With experience, you’ll begin to understand what stakeholder requests can potentially amount up to that 20% and what delays will result from that 20%. Remember, time is money, and it is crucial to master risk assessment as a product manager.

##### Sales & Marketing:

You will surely know that roughly 80% of your income from 20% of your customer base. The Pareto principle dictates that you should optimize your marketing and advertising efforts to that 20%. With experience, time (and hopefully good analytics), you’ll understand who that 20% customer base is. You should not ignore the 80% of customers, but I would spend most of the set budget towards the 20% as precedence for marketing and advertising efforts.

### Key Takeaways

– The principle is a guideline, a framework, and not a set in stone set of rules.

– Let your hair down a little and let the ratios skew a little; it will never always be precisely 80/20.

– Simplifying the idea further keeps in mind that inputs and outputs have an unequal relationship and are not directly proportionate.

– You will get better at seeing the 20% with more experience and time under your belt.

– Analytics will help speed up that experience and time if you have the data available.

– User research and conversations will also significantly improve your ability to see the 20% as a product manager.

– As time goes on, you’ll be able to use fractal nature to determine more precise and smaller percentages of inputs that yield disproportionately higher results (64/4 and 51/1)

– 80/20 does not only apply to your product but everything else around you, such as marketing, your teams, customers, and so forth.

‍


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## Empowering your Digital Transformation Teams

*Published: 2020-09-01*
*Tags: Digital, Innovation, Transformation*

URL: https://www.ahmadkarmi.com/insights/empowering-your-digital-transformation-teams

> Ever heard of the saying that no one is perfect? The same applies to transformation, be it the strategy, roadmap or the development. The key here is to make progress and then iteratively improve on that progress. If you chase perfection and an "ideal" end result, you'll never make it to that finish line. The function is more important than form.

“The new normal” they say. As much as I hate to say it, it seems to be the overture to every published article since the new year. It’s a bunch of marketing fluff, to avoid saying “our current situation”. But, with the introduction of this pandemic, long lockdowns and work from home policies, it’s hard to imagine that our current situation has become anything other than a new norm after nine or so months.

This new situation pushed companies to become more digital than ever, in a dire need to survive a time when downtime and economic strains reigned supreme.

So how do companies, who have not taken advantage of digital transformation help nurture and encourage good habits across their teams to conceptualize, strategize and implement these types of projects successfully?

Although this is not an extensive list, these are some traits or attitudes to promote throughout teams to help make better and more sound decisions.

#### Agile is not just a buzzword

Agile, it’s one of those buzzwords that if you define it and study it, it will make sense. You can jump into SCRUM as well and about 50 other sub-frameworks. Sure, each one has it’s benefits but they all share the ideology of incorporating incremental development. The best thing you can do as a transformational actor is to support the idea of being Agile. Transformation should be progressive, where all the sum of their parts, creates a whole. What you’ll find is that if you break every part of a transformation’s development; that the sprints of a feature-driven mentality help build a better customer experience than the previous waterfall methods. This helps with quality control and time to market and leads us to our next point.

#### Advancement is greater than ideal

Ever heard of the saying that no one is perfect? The same applies to transformation, be it the strategy, roadmap or the development. The key here is to make progress and then iteratively improve on that progress. If you chase perfection and an “ideal” end result, you’ll never make it to that finish line. The function is more important than form.

#### Data, data, data

With digital transformation, one of the most important aspects is data. Be sure to understand two things when it comes to data. What data are we basing our transformation strategy on and how will we structure it? Then secondly, you’ll need to know what data is being captured and how it will be useful to derive insights from it. If you are not a data-driven company in 2020, I am afraid you’re less likely to see the end of the decade, and that’s if you’re lucky. Data is the most powerful tool in today’s world, and if you don’t believe me; ask Cambridge Analytica.

#### Transparency

When it comes to transformation projects, the team behind it can make or break the project. Especially when building the strategy and concept of the transformation project. Transparency and communication are key. All stakeholders involved will have key experience and knowledge on processes, policies, and roadblocks of the operations that the transformation project will tackle. When all of these parties communicate clearly and transparently, it is more likely they will conceptualize and strategize something that is more useful to the end-user. It is also important that during the process of a transformation project, especially development; all stakeholders can communicate clearly and without fear of consequence. You don’t want to be halfway through a project and suddenly find out that desired roadmaps and features cannot be met on time because the business strain of things took precedence on the development of things. I’ve seen way too many projects where developers have been pushed into extremely unrealistic timelines and requests, only to be faced with angry emails and finger-pointing.

#### Do not “people please”

This also relates to the previous point, but I’d like to extend this trait a little further. We live in a society of what I like to call “people-pleasers”. It is not “lazy” or “rude” to say no if you see something you disagree with, as long as you can openly discuss and extend your thoughts on why “no” is a fair answer.

#### Security & Governance

With more data coming into play, we need to make sure this data is secure. With a more digital footprint, we need to know that these systems are safe and secure. It is always smart to have someone on any transformation project with a security background. It is also important that we have data-governance. Bad data, is even more harmful than no data. So let’s make sure we have accurate business data in both our decision-making process for the strategy and roadmap of our transformation projects and that we are collecting the right data for our project needs. All transformation projects need to ensure that it’s methods and leadership responsibilities with regards to data and security are aligned.

#### There is no “I” in transform

It is important as a team member to have a team mentality. There should not be one single department who fully manages or full employs a team for digital transformation. If this is a case, then diversity should be requested from management. Transformation projects require diversity in both thinking and expertise. No one stakeholder is more important than the other. Each should bring in with them the key expertise to represent their stakeholders and communicate this clearly with all team members.

#### The customer is always right

There is a truth behind the old adage has never been more accurate. The customer is always right, and that customer is the end-user. All end-users should be represented in teams and listened to very carefully. We need to start looking at the transformation from the eyes of the end-user and not what the company values as ideal. Incorporate your end-users into every step of the way, and make sure that the transformation caters to making their lives easier, more efficient and better. Look at the KPIs of the end-user and see how you can help improve them through the transformation. An important KPI I always love to look at is time to completion. Another old adage that is true is “time is money” and if I can make the end-users’ journey faster and more efficient, then I’ve saved money and given these end-users more time in their day where they can be useful elsewhere. That is not the only KPI to look at, but it surely tends to be one of my favourites.

These are not the only ways to empower your digital transformation teams, but they are what I believe to be some of the most important. If you have any more to share, I’d love to hear them.

‍


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# Portfolio

## Ekko Health: A Health App That Listens Before It Speaks
*Client: Ekko Health*
URL: https://www.ahmadkarmi.com/portfolio/ekko-health-a-health-app-that-listens-before-it-speaks

TL;DR

Ekko Health is an iOS app that reads your Apple Watch signals on-device, learns your personal baseline over fourteen nights, then speaks up only when something has shifted. The whole intelligence layer runs locally by default. Plus subscribers ($5.99/mo) can opt into Claude for richer answers, off until they explicitly turn it on, with sanitisation, a zero-retention proxy and an on-device audit log.

This case study walks through six product problems Ekko was built to solve, the constraints and alternatives I weighed for each, what the chosen solution looks like in the product and what outcomes I expect or have measured. The technical decisions are in service of the problems, not the other way round.

Why this exists

If you wear a watch to bed for a month, you generate enough physiological signal to fill a small clinical record. Resting heart rate. HRV. Respiratory rate. Sleep stages. Wrist temperature deviation. Apple Watch, Oura, WHOOP and Garmin all collect it. The question is what you do with it.

The category fails in two predictable ways.

The first failure is the dashboard. Charts and rings and percentiles, accurate as a clinical chart and meaningless to a non-clinician. New users open the app on day one, look at a screen full of numbers they cannot interpret, decide they will read about it later and never come back. The retention curve in this category is unforgiving. Most health apps lose seventy percent of installs in the first seven days, and the dashboards are a primary cause.

The second failure is the notification. A ping every morning that says “Your sleep score was 72!” with no context for what 72 means or what to do about it. Users learn within a week that the notification carries no information, develop a Pavlovian swipe-away response and stop reading the very signal they wanted help with. The product trained them not to listen.

Ekko was designed for the gap between those two postures. Not a coach. A witness. The app says nothing for fourteen nights while it learns your baseline. After that, it speaks only when something has shifted against your own history, and it speaks editorially rather than analytically. “Quiet by default” is the brand axiom. Every product decision either supports it or breaks it.

What follows is the structure of those decisions, rebuilt around the problems they solve.

 
 
 
 

The product, surface by surface

A short orientation, since the problems that follow reference specific surfaces.

Onboarding is five steps: a welcome screen, a brief explanation of how Ekko works, age confirmation, HealthKit permissions and a baseline-period splash that promises the app will stay quiet for two weeks. Each step sits on a slowly intensifying aurora background. The welcome screen features a ListeningWaveform graphic, a slow signature sine with sparse Gaussian blips, suggesting that the watch already noticed something.

Today is the hero surface. A BodyStateHero masthead occupies the top of the screen: one word naming today’s body state, one sentence of editorial context and an ambient aurora tinted by that state, breathing at the user’s actual resting heart rate. Below the hero, a metrics ledger lists each signal Ekko tracks with an inline seven-day sparkline per row.

History is a calendar where each day is tinted with a sliver of its tone-aurora. Tap a day and the app shows you what that day was, written as a short story.

Trends rolls everything up. Weekly summaries, pattern detection and per-metric charts that draw themselves with a DrawingSparkline animation when the tab loads.

You is the settings surface. Privacy posture sits at the top. The Plus subscription card, intelligence preferences and journal export all live below it.The paywall is its own surface and gets its own section further down, because every lever on it is a PM decision.

 
 
 
 

Six problems the product was built to solve

Each section below opens on the user or business problem, walks through the constraints and the alternatives, names the choice and closes on the outcome that ships, the mechanism by which it should work or the measurement plan that closes the loop.

Problem 1: Thirty percent of iPhones cannot run the AI feature your privacy thesis depends on

Apple Intelligence requires an iPhone 15 Pro or newer. In late 2025, that excludes roughly thirty percent of the active iPhone install base. For a product whose distinctive promise is on-device natural-language interpretation of your health data, that exclusion is not a footnote. It is a category-level problem. A user on an iPhone 13 downloads Ekko, completes onboarding, hits Day 14 and finds the most expressive part of the product greyed out behind a hardware requirement they cannot resolve without buying a new phone.

The business problem inside the user problem: privacy-first health is a small enough TAM that turning away thirty percent of devices at the door makes the unit economics fragile. Plus needs to be available on every supported iPhone if Ekko is going to compound subscribers.

I considered three paths.

The first was on-device only. Cleanest privacy story, simplest architecture, and a permanent thirty percent ceiling on the addressable market. The privacy purists would have loved it. The business would have died of it.

The second was cloud by default. Universal device support, one consistent experience to design and QA, and a privacy thesis that collapses on contact with the App Store description. If every user’s health data flows to a third-party API by default, the brand promise is marketing copy, not architecture.

The third was a hybrid, with on-device as the default and a documented opt-in path to Claude for users whose devices cannot run Apple Intelligence or who want richer answers regardless. More architecture, more screens, more code, and the privacy thesis stays intact for the default user while older devices still get a path to the full feature surface.

I picked the hybrid. The decision logic: the privacy thesis is the brand, the brand is the wedge into a crowded category, and the brand cannot be partially abandoned to ship a more consistent experience. Offering Claude as a documented opt-in respects user autonomy without forcing a privacy compromise on every user. The consent screen is the architecture, not a checkbox at the bottom of a settings menu.

The honest tradeoff is in maintenance cost. Two intelligence backends. Two failure modes to design for. A recurring internal debate about whether Plus is “really” the privacy product or “really” the AI product. The answer is both, by design, and the cost of that answer is paid in code review hours that I think are worth it.

Outcome. Hardware exclusion drops from thirty percent to zero across the supported install base. Plus subscribers on incapable devices can opt into Claude after reading the consent screen. The Apple Intelligence ineligibility card uses a “There’s another way” reframe, which leads with the opportunity instead of the limitation and is the single highest-converting moment in the Plus funnel based on early read. The measurement that would close the loop is Plus conversion rate on Apple-Intelligence-ineligible devices versus eligible ones. My expectation is that ineligible devices convert at a higher rate, because the value proposition is sharper for them.

Problem 2: Every health app on the App Store looks like every other health app

Open the top twenty health apps in the App Store and look only at the screenshots. Stock illustrations of geometric people meditating. Pastel hearts. Soft gradient charts. Cheerful sans-serif type. If you strip the logos, you cannot tell most of them apart. The visual layer is interchangeable, which means it is uncopyable as a differentiator and free to copy as a competitor.

The product problem is brand recognition in a crowded category. The business problem is defensibility. Anything that can be reproduced in a sprint by a competitor with three designers is not a moat.

The conventional response is to invest in illustration. Hire a brand illustrator, commission a custom set, ship a “distinctive” visual layer. The output is usually beautiful and entirely portable. Six months later a competitor has a similar set, the differentiation evaporates and you have a recurring illustration retainer on your books.

I wanted every graphic in Ekko to be a function of the user’s own physiology, so that the design system would be inseparable from the data and uncopyable without copying the entire architecture.

Four examples carry the idea.

The aurora background draws its hue from body-state tone, its intensity from HRV deviation against baseline and its drift cycle from the time of day. It is the same visual primitive on every screen and never the same image twice. The First Prediction Reveal animation takes the user’s actual thirteen-night resting heart rate sparkline, collapses it into a luminous dot and lets that dot ignite the aurora behind the prediction card. The day-progress rod under the masthead is a tone-tinted capsule that fills from 5 AM through midnight, telling you where you are in your day without naming the hour. The metrics ledger renders the last seven days of each signal inline as a sparkline, no tap required.

The defensibility argument is direct. A competitor can copy the copy. They cannot copy the choice to make a user’s body the design system, because to copy it they would have to give up their entire illustration library and rebuild their data layer around aesthetic output. Few teams will do that for a feature they cannot screenshot for marketing.

The cost lives in production. The graphics are harder to design, harder to QA and impossible to fake for a marketing asset, because every screenshot has to come from real user data. I had not solved the marketing-asset content pipeline at ship, which is a real gap I am paying for now in App Store optimisation cycles.

Outcome. Every surface in Ekko is recognisable as Ekko within one screenshot, with no logo visible. Brand recall in a five-second test against three category competitors lands meaningfully higher, based on the small qualitative round I ran with eight users. The measurement that would scale this signal is unaided brand recall at thirty days post-install in a paid survey, which I have not yet funded.

Problem 3: One voice across every surface produces paywalls that sound like haiku and settings screens that sound like marketing

Every brand voice guide I have read insists on one voice. The instinct makes sense in a marketing-led product where every surface is doing the same job: persuasion. It falls apart in a product where the surfaces have genuinely different jobs.

Reflection and onboarding ask the user to feel something. The hero asks them to read one sentence and look up from their phone. The paywall asks them to make a financial decision. The settings screen asks them to find a toggle and not be confused. The error toast asks them to understand what went wrong. Forcing one register across that range produces predictable failure modes: a paywall that reads like a poem and fails to convert, or a settings screen that reads like a marketing email and feels manipulative.

The user problem is comprehension and tone fit. The business problem is conversion on functional surfaces and trust on emotional ones, two outcomes that pull a single-voice system in opposite directions.

I picked a split. Editorial-literary register on the emotional surfaces. Restrained-precise register on the functional surfaces.

Editorial register lives in onboarding, the hero subhead, reflection, anniversary moments and the rare animation captions. It is descriptive, lyrical, never imperative, never numeric. The 19-cell BodyState by TimeWindow matrix in BodyState.swift:subhead(for:) is its home. A cell looks like “the morning is unhurried, your body is steady”. Three variants per cell, rotated by day of year so users do not see the same line twice in a row.

Restrained register lives in metrics, settings, paywall copy, toasts and errors. It is plain, precise and numeric where numbers help. “Hearing 38% today.” “Annual saves you 30%.” “Sync failed, we’ll retry in the background.” It earns trust by being literal.

The split is enforced by tests, not by editorial judgment. Five invariants are asserted across the matrix and the functional copy: no duplicates inside a cell, no register leakage across cells, character limits per surface, no second-person imperatives on functional surfaces and no metric numerals on emotional ones. A pull request that adds a copy string in the wrong register fails CI.

The tradeoff is maintenance overhead. Two registers means two style policies, two sets of tests and two onboarding ramps for any future contributor to the copy. The cost of one voice would have been lower. The cost of two voices, paid in test infrastructure rather than ongoing editorial judgment, is the cost I chose.

Outcome. The paywall reads like a paywall. The hero reads like a sentence the user remembers. The settings screen reads like a settings screen. The split is testable and therefore enforceable in code, which means it survives the next designer and the one after that. The measurement that closes this loop is paywall conversion against a single-voice control, which I designed for but did not ship because the trial-length revision absorbed the testing slot for that sprint.

Problem 4: Animation everywhere is the same as animation nowhere

The temptation in a craft-driven product is to give every screen its hero moment. The result is a hyperactive surface that exhausts the user by the third tab, drains battery, makes the app feel busy and erases the hierarchy that signature moments depend on. If every transition pulses and shimmers, none of them mean anything.

The user problem is attention fatigue. The business problem is battery and complexity, both of which feed back into reviews and uninstalls. The brand problem is the hardest of the three: in a category where the pitch is “this product is calm”, every gratuitous animation is a small betrayal of the promise.

The conventional response is a motion guideline document that no one reads, followed by a slow accretion of animations until the app feels like a slot machine. I have seen this happen at three companies. The motion guideline is necessary and insufficient.

What I picked was a rationing pattern with three signature moments at three different cadences. The pattern is the decision, more than any individual animation.

The Body-State Aurora is the ambient one. It breathes behind Today every time the user opens the app, at the user’s own resting heart rate. It is permanent and almost subliminal, which makes the Today screen feel alive without ever drawing attention to itself.

The First Prediction Reveal is the once-in-a-lifetime one. It fires exactly on Day 14, when Ekko has earned the right to speak for the first time, and it never fires again for that user. The user’s actual thirteen-night sparkline collapses into a luminous dot, the dot ignites the aurora behind the prediction card and the card resolves with the prediction. The whole thing takes about four seconds.

The Day Closes Ritual is the recurring one. It is triggered nightly when the user taps Goodnight in Reflection. It is shorter than the Day 14 reveal, slower than the aurora and uses motion the user can come to expect.

Every other surface in the app gets craft, just not spectacle. Press-scale on buttons. A drift-gradient under the section headlines. The day-progress rod filling slowly across the day. These read as quality without competing for attention.

The hierarchy is the thing that makes the spectacle land. One ambient, one once-in-a-lifetime, one recurring. Nothing else.

The tradeoff that keeps me up: the once-in-a-lifetime moment is exactly that. If Reduce Motion is on, if the user has thin data, if a HealthKit sync delay throws the timing off, there is no replay. I did not spec a fallback. I did not spec the success metric for the moment before shipping it, which is a craft-on-instinct decision I would not repeat.

Outcome. Three named moments at three named cadences. App-wide motion budget visible in code review. No competing animation candidates added during the build, because the rationing pattern made it easy to decline new requests. The measurement that would close the loop is Day-14 reveal completion rate, time on screen for the reveal and a follow-up retention curve at Day 30 conditional on reveal completion. I should have specified all three before ship.

Problem 5: A privacy claim the architecture cannot defend is marketing, not privacy

Every privacy-first app in the App Store says the same things. “Your data stays on your device.” “We never sell your data.” “End-to-end encrypted.” Most of those claims are unverifiable from the binary, contradicted by the network log or true only in the sense that the company has not yet sold the data they continuously collect.

The user problem is trust collapse. Users who care about privacy have heard every claim and learned to discount all of them. Repeating the claims louder does not work. The business problem is that “privacy-first” is the brand wedge, and a wedge that the user does not believe is not a wedge.

The conventional response is a privacy policy page, a marketing landing block and a wordmark with a padlock on it. None of that changes user belief. The architecture is what changes belief, when the architecture is inspectable.

I picked privacy as architecture, not posture, and tried to make each load-bearing claim enforceable in code rather than promised in copy.

Apple Intelligence runs on-device. The default experience never sends health data off the iPhone. This is verifiable in the network log on a jailbroken device and in the Apple Intelligence framework documentation.

Plus subscribers opt into Claude only after reading an explicit consent screen. The opt-in is one-way: turning it on is two taps, turning it off is one tap, and the off state is the default for every Plus user including those who upgraded specifically to use Claude. Planned for Phase 3.

All data sent to Claude is sanitised on-device first via AISanitizer.swift. Journal notes are redacted with a permissive regex set. UUIDs are stripped. Absolute dates are relativised to “yesterday” or “12 days ago”. Seven unit tests assert round-trip integrity against a canonical fixture set, which means a PR that breaks the sanitiser fails CI.

The Cloudflare Worker proxy in front of Claude is zero-retention by design. The Worker logs operational metrics only: latency, error code and request count. Request bodies are never persisted. Planned.

Every Claude call is logged in an on-device transparency view the user can audit, clear or export. This makes the privacy posture inspectable rather than asserted. The user can see what was sent, when and for what purpose. Planned.

The pattern is that every claim Ekko makes is paired with a piece of architecture that enforces it. The architecture is the marketing. When a reviewer or a journalist asks “how is this actually different”, the answer is six artefacts they can look at.

Outcome. Privacy posture moves from claim to defence. The audit log is the most expensive piece of the system to build and the highest-leverage one for trust, because it lets a sceptical user verify the claims themselves. The measurement that would close the loop is Plus opt-in rate to Claude after the consent screen, which I expect to be lower than industry consent flow rates and which I think is the correct direction.

Problem 6: The paywall is the part of the product PMs are not proud of, which is exactly why it underperforms

Most PM portfolios skip the paywall. It feels like the part of the product you are not supposed to be proud of, the commercial moment in an otherwise crafted experience. I think that posture is wrong, and the conversion data agrees. Most of the levers on a subscription paywall are decisions the PM owns directly, and most of them are left on the table because PMs treat the paywall as marketing’s problem.

The user problem is decision overload at the paywall. Two plans, an offer, a trial, a feature list, a comparison table and a CTA, all rendered on one screen. The user defaults to the cognitive shortcut: pick the cheaper option or close the app. The business problem is LTV. A user who picks monthly churns at four to six times the rate of a user who picks annual, and a user who closes the paywall costs the same as the first two combined.

The conventional response is to ship a paywall that defaults to monthly, applies an urgency banner (“offer ends in 23:59”), stacks an intro discount on top of a free trial and runs a discount cycle every quarter to chase numbers. The conventional response works and corrodes the brand. For a product whose pitch is “this app respects you”, urgency banners are an active brand liability.

What I picked, lever by lever.

A seven-day free trial on annual only. Annual-only captures higher LTV. Seven days converts better than fourteen days in current industry benchmarks, because fourteen days gives users enough runway to forget the app before the charge fires. The RevenueCat 2024 dataset puts the conversion delta between seven and fourteen day trials at roughly twelve to fifteen percent on annual.

Default-to-annual selection on the plan picker. This is the single largest conversion lever in subscription paywall design, worth a thirty to fifty percent lift over default-to-monthly in published case studies. It is also the most under-pulled lever, because PMs default to giving the user a choice and frame any default as manipulative. The defence is that the default reflects the recommendation, and the recommendation is the higher-LTV plan.

A “30% OFF” badge on annual, calculated from the effective monthly price ($4.17 vs $5.99) and surfaced in the chip rather than the body copy. Body copy is for explanation, chips are for signal.

CTA copy that adapts based on intro-offer presence. “Start 7-day free trial” when the offer is live, “Subscribe” when it is not. The logic reads StoreKit 2 product state rather than picking from two static strings, which means the copy stays correct if the offer is paused or revoked in App Store Connect.

No intro discount stacked on top of the trial. Stacking adds decision complexity, lowers first-session conversion in the tests I have seen and dilutes the brand voice toward “everything-must-go”.

No urgency banners. No countdown timers. No “23 people are looking at this offer right now”. The brand cannot survive those tactics, and the conversion lift from them is small enough that the brand cost is the wrong trade.

The Apple Intelligence ineligibility card uses a “There’s another way” reframe. The default copy would have read as a limitation: your device cannot run on-device AI. The Plus reframe leads with the opportunity instead: Plus unlocks an alternative path through Claude, gated by explicit opt-in. This converts because it inverts the user’s emotional posture from disappointment to discovery.Outcome. Net margin of around sixty-one percent per annual subscriber, after Apple’s fifteen percent Small Business Program take, cached inference cost and corporate tax. Annual price of $49.99 versus Calm and Headspace at $69.99 and Gentler Streak at $44.99 puts Ekko at a defensible “premium but not punitive” position. The measurement that would close the loop is a paywall A/B test against a default-to-monthly control. The hypothesis sits on benchmark data and brand fit. Being able to name those sources is the starting point for shipping the test, not a substitute for it.

 
 
 
 

Problems I refused to solve

Restraint is a PM virtue the industry undervalues. The omissions are where the product judgment lives, because every feature is a decision and every refused feature is also a decision. Five things I left out on purpose.

No bespoke illustrations. The data is the design system. Adding illustration would compete with the aurora rather than complement it and would weaken the defensibility argument from Problem 2.

No urgency banners on the paywall. “Offer ends in 23:59:11” works in mobile games and category-leading consumer apps. For a product whose pitch is “this app respects you”, the same banner is brand damage measured in lifetime value, not conversion lift.

No mascot, no coachmark overlays, no pulsing “tap here” hints. The user is an adult and is opening a health app voluntarily. Tutorialising the experience signals that the product does not trust the user, and a product that does not trust the user is not going to be trusted back.

No silent fallback to rule-based text when the user explicitly invokes AI. If the user taps “Why am I Ready?” and Apple Intelligence is unavailable, Ekko shows a card titled “Apple Intelligence is sleeping” with a path to Plus. The alternative would be a quietly worse answer that pretends nothing is wrong, which trains the user to distrust the AI features the moment they notice the seams. The honest failure is better than the silent one.

No third-party SDKs in the MVP. StoreKit 2, FoundationModels and URLSession are hand-rolled per a project rule documented in CLAUDE.md. The rule exists because privacy claims are easier to defend when the boundary of the binary is small and known. Every SDK is a piece of code we did not write and a piece of trust we are asking the user to extend on our behalf.

The unit economics

PlanPriceEffective monthlyTrial

Monthly$5.99/mo$5.99none

Annual$49.99/yr$4.177-day free

Net annual subscriber math, before any retention or churn modelling.

Gross revenue per annual subscriber, $49.99. After Apple’s fifteen percent Small Business Program take, $42.49. After cached AI inference plus infrastructure, estimated at $1.80 per subscriber per year, $40.69. After roughly twenty-five percent effective corporate tax, the net is approximately $30.50 per subscriber per year. Net margin lands around sixty-one percent.

Category positioning is friendly. Calm and Headspace sit at $69.99/yr. Strava at $79.99. Gentler Streak, the closest direct comparable, at $44.99/yr. Plus is five dollars above Gentler Streak’s annual and one dollar above their monthly, which puts Ekko at a defensible “premium but not punitive” price for a privacy-first product.

Plus pays for itself at roughly $2.30 per subscriber per month against running cost. The unit economics survive long before the top of the funnel grows.

 
 
 
 

What is shipped vs what is planned

Shipped: onboarding, Today, History, Trends and You. Body-State Aurora, First Prediction Reveal and Day Closes Ritual. Apple Intelligence provider with AISanitizer and AppleIntelligenceRequiredCard. SubscriptionService and PaywallView with a conversion-optimised .storekit config. AICacheService with a four-layer caching strategy.

Planned for Phase 3 and beyond: Claude opt-in consent screen. EkkoClaudeProvider and the Cloudflare Worker proxy. AIActivityView transparency log. A “New iPhone with Apple Intelligence” one-time card for users upgrading mid-subscription.

What I would do differently

Five follow-ups I would commit research budget to before a real launch. Each had directional signal at spec time. The gap is validation, not judgment.

The seven-day trial on annual sits on two inputs. RevenueCat’s 2024 subscription benchmark data shows a consistent conversion lift for seven-day trials over fourteen-day on annual plans, and my own experience shipping subscription products before Ekko has matched the pattern: fourteen days gives users enough runway to forget the app before the charge fires. Both inputs pointed the same direction. What I would sequence differently is the design pass. Trial length got finalised after the paywall was already in flight, which meant a revision cycle once I settled on seven days. The correct move is to lock trial length at the top of the paywall brief, since every piece of copy and conversion logic downstream depends on it. The validation that closes the loop is a paywall A/B test against a default-to-monthly control, once the install base supports clean reads at around five thousand paywall views per arm.

Fourteen nights is a physiology decision before it is a UX one. Healthy adult heart rate variability, resting heart rate and sleep architecture show enough night-to-night noise that a seven-night baseline can be skewed by a single outlier: a glass of wine, a stressful day or a one-off sleep hygiene break. Two weeks of data damps the noise without asking for so much patience that users uninstall before the reveal lands. I tested the baseline on myself across four weeks of watch data before committing to the number, and my own signals settled into a stable pattern between nights ten and twelve. That internal testing is a useful starting signal and is not the same as a study. The next pass is a moderated study with eight to twelve participants comparing seven, fourteen and twenty-one night baselines against both stability curves and qualitative interviews. The research budget for that study is the first line item I would fund post-launch.

The First Prediction Reveal was designed around a conversion thesis I held tightly and never committed to a spec. The hypothesis: if a user reaches the Day 14 reveal and then completes the next sixteen nights to hit Day 30, the product has crossed the line from habit into lifestyle. The reveal is the inflection point that earns the user’s continued attention. Day 30 is the validation that the moment did the work it was designed to do. The gap is that I kept the hypothesis in my head rather than writing it into the animation spec. The right PM behaviour is to define the success metric in the same document that specifies the moment, and to instrument it in the same sprint as the build. The metric I would commit to is Day 30 retention conditional on completing the Day 14 reveal, with a target of sixty percent or better for cohorts that clear the first week.

The onboarding personalisation gap is the one I am glad to have caught, even late. The First Prediction Reveal copy already supports the personalised form (“Ahmad, you’re ready”) but onboarding never collected the data the copy needed. I spotted the disconnect well into development, after the reveal animation was already in flight, which meant the personalised line never fired in the build I shipped. The lesson generalises past this product. A name field in onboarding looks insignificant at the data layer and is load-bearing at the experience layer, because every piece of user data in a craft-driven product is connected to a moment somewhere else. The cost of missing one of those connections is always paid at the moment of peak emotional payoff, which is the worst place to under-deliver. If Ekko goes beyond the portfolio piece, the first onboarding revision is a name field with the right framing, and the second is an audit pass on every other input I might be quietly under-using for a moment elsewhere in the product.

Marketing-asset production is the process gap I would close by changing the order of the roadmap rather than by funding more research. Because every graphic in Ekko is a function of real user data, every App Store screenshot has to come from a real cohort. I had not solved that pipeline at ship, which means ASO cycles now compete with the next feature sprint for the same resource. The fix is structural: marketing-asset production goes on the roadmap as a launch dependency in the next product brief, not as a follow-up sprint after launch.

 
 
 
 

Closing

The strongest product judgments in Ekko are not the features. They are the choices about which problems to solve and which to refuse.

Keep AI on-device by default, even though it costs thirty percent of devices unless you build a second backend. Make privacy enforceable in code rather than promised in marketing, even though it triples the architecture cost. Make every graphic a function of the user’s physiology, even though it kills the marketing-asset pipeline. Pick one tone per surface and write a system for the next designer to extend, even though the test infrastructure is more expensive than a style guide. Ration three hero moments rather than animate everywhere, even though every reviewer asks why the rest of the app is “quieter”. Charge $5.99 and design the paywall around long-term LTV rather than short-term tactics, even though the tactics would convert better in the first week.

Ekko learns who you are in fourteen nights. The app learns who you are by showing you yourself.

The graphics, the words and the motion are all the same data, translated into different senses.


---

## ZAIN eSports
*Client: Zain Group / Zain eSport*
URL: https://www.ahmadkarmi.com/portfolio/zain-esport

### Brief
In 2017, I spearheaded an innovative initiative at our company, which came to be known as the Innovation Program. This program was designed to empower our employees to pursue novel projects or explore potential business avenues while taking complete ownership of their endeavors. The fundamental objective of this program was to provide a platform for employees to conceptualize, strategize, and execute their projects with the support of our organization.

Upon identifying a promising project in 2018, I conceived the idea of creating a regional esports powerhouse that would foster and organize gaming tournaments and leagues for enthusiasts across the region. Our vision was to establish an infrastructure that would enable amateur and grassroots players to develop and flourish into professional esports players. Recognizing the potential of this project, we actively sought a prominent telco to partner with us, and after extensive negotiations, we found the ideal collaborator in Zain.

(We invite you to download the public pitch deck we used during the business development process with various telcos in the region by [downloading the following PDF](https://cdn.prod.website-files.com/66c8ba6cd0ad61e4b10e4cbe/66d07dcf89a6539504fb2541__eSports_ProjectBrief_ReducedFileSizeVersion.pdf).)

### Scope
In order to bring the client's vision to life and create an esports organization that met their specifications, we embarked on several key activities. As the driving force behind this project, I worked closely with the client to ensure its successful execution. The scope of our work was as follows:

- Conceptualize the project
- Identify a suitable local telco partner
- Conduct extensive market research and analysis
- Develop a comprehensive strategy and plan
- Engage international esports consultants with established relationships with developers
- Provide ongoing support to the telco with branding, marketing, and communications
- Create sponsorship opportunities
- Establish intellectual property and media plans
- Implement the esports organization

By executing these critical tasks with precision and dedication, we were able to deliver an esports organization that not only met but exceeded the expectations of our client.

### Details
Undoubtedly, this project was not without its challenges. As we began to prepare for collaboration and launch, we were suddenly hit by the global Coronavirus pandemic. The unprecedented circumstances made it exceedingly difficult to devise a strategy and bring all stakeholders together remotely. However, despite the unexpected hurdles, we were able to achieve remarkable success.

In fact, one could argue that the pandemic, while making operations more difficult and sluggish, ultimately worked in our favor as it coincided with the gaming industry's exponential growth. The surge in demand for online gaming during the pandemic allowed our business to flourish even further. By navigating the difficulties with resilience and adaptability, we were able to capitalize on the opportunities presented by the challenging circumstances and achieve our goals.

‍

### The Video

Undoubtedly, this project was not without its challenges. As we began to prepare for collaboration and launch, we were suddenly hit by the global Coronavirus pandemic. The unprecedented circumstances made it exceedingly difficult to devise a strategy and bring all stakeholders together remotely. However, despite the unexpected hurdles, we were able to achieve remarkable success.

In fact, one could argue that the pandemic, while making operations more difficult and sluggish, ultimately worked in our favor as it coincided with the gaming industry’s exponential growth. The surge in demand for online gaming during the pandemic allowed our business to flourish even further. By navigating the difficulties with resilience and adaptability, we were able to capitalize on the opportunities presented by the challenging circumstances and achieve our goals.

‍

### The Video


---

## Wilson & Bass
*Client: Wilson & Bass*
URL: https://www.ahmadkarmi.com/portfolio/wilson-bass

### Brief
The task at hand was to fully brand a new Human Capital Solutions company. The desired brand was to be very neutral, with blacks, whites and greys. The client needed the brand to speak to large enterprises as they were their target market. Yet, they wanted a modern edge to their brand that spoke to C-Levels as well as entry level employees. The task at hand was difficult, but the client was very satisfied.

### Scope
1. Build a simple logo using negative space that works on all collateral
2. Using a specific color palette (black, white and shades of grey)
3. Complete a corporate website with dynamic contact forms and application forms
4. Provide start-up collateral with creative geometric branding
5. Provide back end panel for sorting applications
6. Client requested wordpress to power the website for familiarity
7. Used custom fields and gravity forms to power the application process and customizations

### Details
Wilson & Bass is a human capital solutions company that focuses on many aspects of HR Consulting & Advisory. One of their key strengths is an AI platform that is fed with high quality data that the firm constantly updates throughout their business activities. This results in a system that is always learning and outputting the best automated insights possible. To develop a brand that echos such a feat, the client wanted to go for a neutral yet modern look. I recommended that we work with blacks, whites & grays and throughout the logo development we felt that using negative space would provide a very edgy yet professional look. The colour scheme created for branding this company has been detailed below:

![](https://admin.ahmadkarmi.com/wp-content/uploads/2026/01/66cb074a0b23652cea7ce5c0_6305b99b96183ea13c1eacbb_WB_Logo_Colors_f07ddbf7e4.jpeg)

#### Moving Forward

Once this logo has been created and our colour scheme was set, the task at hand was very difficult to create something that would satisfy the client's needs. At first the client wanted straight lines and very rigid approach. We began with this business card that can be seen in the following image:

![](https://admin.ahmadkarmi.com/wp-content/uploads/2026/01/66cb074a0b23652cea7ce5c3_6305b9c2ab6b977717979966_BusinessCards_WB.jpeg)

#### Branding the Collateral

However, both myself and the client found this to be too boring of a style. We decided it was time to change it up. With this new vision, the client wanted to avoid straight lines in their branding collateral and use curves inspired from the "&" part of the logo seen in the negative space. My idea was that the best way to go around this is to use a circle to signify the curve, but in order not to brand it as a single geometric shape; we thought to think outside of the box. Thinking outside of the box put us on a journey to literally change the concept to "think outside of the circle". You can see the overall branding approach in the image.

![](https://admin.ahmadkarmi.com/wp-content/uploads/2026/01/66cb074b0b23652cea7ce5fd_6305b9f9db6178fb970e915b_K_V2_stationary_branding_mockup_items_sideview_V1_3.jpeg)

The client was extremely happy with the work provided. This concept followed them everywhere; from print, to web properties and even into their social media.

The branding for this company was scored independently and ranked 14th place out of 121 SMEs, for brand value. Obviously the designs are not the only factor in this measure but it had definitely helped the company score high and achieve their goals.

**Please view the gallery below to see some more implementation of the branding collateral:**

Wilson & Bass is a human capital solutions company that focuses on many aspects of HR Consulting & Advisory. One of their key strengths is an AI platform that is fed with high quality data that the firm constantly updates throughout their business activities. This results in a system that is always learning and outputting the best automated insights possible. To develop a brand that echos such a feat, the client wanted to go for a neutral yet modern look. I recommended that we work with blacks, whites & grays and throughout the logo development we felt that using negative space would provide a very edgy yet professional look. The colour scheme created for branding this company has been detailed below:

![](https://admin.ahmadkarmi.com/wp-content/uploads/2026/01/66cb074a0b23652cea7ce5c0_6305b99b96183ea13c1eacbb_WB_Logo_Colors_f07ddbf7e4.jpeg)

#### Moving Forward

Once this logo has been created and our colour scheme was set, the task at hand was very difficult to create something that would satisfy the client’s needs. At first the client wanted straight lines and very rigid approach. We began with this business card that can be seen in the following image:

![](https://admin.ahmadkarmi.com/wp-content/uploads/2026/01/66cb074a0b23652cea7ce5c3_6305b9c2ab6b977717979966_BusinessCards_WB.jpeg)

#### Branding the Collateral

However, both myself and the client found this to be too boring of a style. We decided it was time to change it up. With this new vision, the client wanted to avoid straight lines in their branding collateral and use curves inspired from the “&” part of the logo seen in the negative space. My idea was that the best way to go around this is to use a circle to signify the curve, but in order not to brand it as a single geometric shape; we thought to think outside of the box. Thinking outside of the box put us on a journey to literally change the concept to “think outside of the circle”. You can see the overall branding approach in the image.

![](https://admin.ahmadkarmi.com/wp-content/uploads/2026/01/66cb074b0b23652cea7ce5fd_6305b9f9db6178fb970e915b_K_V2_stationary_branding_mockup_items_sideview_V1_3.jpeg)

The client was extremely happy with the work provided. This concept followed them everywhere; from print, to web properties and even into their social media.

The branding for this company was scored independently and ranked 14th place out of 121 SMEs, for brand value. Obviously the designs are not the only factor in this measure but it had definitely helped the company score high and achieve their goals.

**Please view the gallery below to see some more implementation of the branding collateral:**


---

## The Story Point Calculator
*Client: Personal Project*
URL: https://www.ahmadkarmi.com/portfolio/story-point-calculator

### Brief
I took it upon myself to vibe-code my way to a tool that I could use on a daily basis. The Story Points Calculator is a Progressive Web App (PWA) designed to assist Agile development teams in estimating story points during planning poker sessions. This tool provides a structured approach to breaking down and evaluating user stories based on multiple complexity factors. I also use this tool as a product manager to pre-plan my roadmap for the coming cycles. I apply the same general questions to product features when asking developers from a high level about the features I want to build and it significantly helps me plan the cycle ahead. ‍ [Visit Now](https://www.storypointcalculator.com) ‍

### Scope
1. The build a tool that could accurately estimate story points for planning purposes
2. The tool must be easy to use
3. The tool must use a weighted scoring system, and dynamically change based on variables and combinations of variables to match our estimation process
4. To learn how to vibe code a basic calculation model with output in graphic format (points, tshirt size, and contribution breakdown).

### Details
#### **1. Intelligent Story Point Calculation**

- **Hybrid Fibonacci Algorithm**: Implements a sophisticated calculation method that considers multiple weighted factors
- **Rule-Based Overrides**: Includes specific rules for edge cases (e.g., when unknowns or dependencies are maxed out)
- **Nonlinear Normalization**: Uses mathematical functions to map input values to the Fibonacci sequence

#### 2. User Experience

- **Responsive Design**: Works seamlessly across all devices (desktop, tablet, mobile)
- **Interactive Sliders**: Intuitive interface for adjusting complexity factors
- **Visual Feedback**: Real-time updates and visualizations of the estimation

#### 3. Technical Implementation

- **Progressive Web App**: Works offline and can be installed on devices (with toast prompting user to download to homescreen)
- **Service Worker Caching**: Enables offline functionality

#### 4. Analytics Integration

- **Google Analytics**: Tracks usage patterns and feature adoption
- **Event Tracking**: Monitors user interactions for continuous improvement

### How It Works

1. **Input Collection**:
 - Users adjust sliders for various complexity factors including:
 - Business Logic Complexity
 - Technical Implementation Difficulty
 - Dependencies
 - Unknowns
 - And more...
2. **Calculation Process**:
 - The app applies a weighted algorithm to the input values
 - Special rules are triggered for specific scenarios
 - A nonlinear transformation maps the inputs to the Fibonacci sequence
3. **Output**:
 - Story points are displayed in the Fibonacci sequence (1, 2, 3, 5, 8, 13, 21, 34)
 - T-shirt size equivalents (XS, S, M, L, XL) are provided for quick reference
 - A visual chart shows the contribution of each factor

### The Calculation Logic

The scoring system in the Story Points Calculator uses a sophisticated hybrid approach that combines rule-based overrides with a mathematical model to calculate story points. Here's a breakdown of how it works:

#### 1. Input Processing

- The calculator takes 8 different input factors (6 regular and 2 inverse)
- Regular factors (0-5): Higher values increase the score
- Inverse factors (0-5): Higher values decrease the score

#### 2. Rule-Based Overrides

The system first checks for specific conditions that override the normal calculation:

1. **Maximum Uncertainty (21+ points)**
 - If both "Unknowns" and "Dependencies" are maxed out (5), the minimum score is 21
2. **High Complexity (13+ points)**
 - If either "Business Logic Complexity" or "Technical Implementation Difficulty" is maxed out (5), the minimum score is 13
3. **Neutral Case (5 points)**
 - If all sliders are at their midpoint (3), the score is exactly 5
4. **Minimum Effort (1 point)**
 - If all regular factors are at minimum (1) and all inverse factors are at maximum (5), the score is 1
5. **Maximum Effort (34 points)**
 - If all regular factors are at maximum (5) and all inverse factors are at minimum (1), the score is 34
6. **High Uncertainty (8+ points)**
 - If either "Unknowns" or "Dependencies" are maxed out (5), the minimum score is 8

#### 3. Hybrid Fibonacci Calculation

If no overrides apply, the system uses a nonlinear calculation:

1. **Weighted Sum with Exponential Scaling**

`javascript`

**`let`** `sum = 0;for (`**`let`** `i = 0; i 0.93  → 34`

### Key Design Principles

1. **Nonlinear Scaling**
 - Uses **`Math.pow(v, 1.7)`** to ensure that higher complexity ratings have proportionally more impact
 - This reflects the reality that complex features often have exponential, not linear, impact on effort
2. **Fibonacci Sequence**
 - Uses the standard Agile Fibonacci sequence (1, 2, 3, 5, 8, 13, 21, 34)
 - The sequence accounts for increasing uncertainty with larger numbers
3. **Inverse Factors**
 - Some factors (like "Team Familiarity") are inversely related to effort
 - These are handled by inverting their scale (6 - value) before calculation
4. **Defensive Programming**
 - Includes extensive input validation
 - Handles edge cases explicitly through the rule-based overrides

This hybrid approach provides a good balance between:

- Mathematical rigor (through the normalized scoring)
- Practical experience (through the rule-based overrides)
- Agile principles (through the Fibonacci mapping)

### Technical Stack

- **Frontend**: HTML5, CSS3, JavaScript (Vanilla)
- **Charts**: Chart.js for data visualization
- **Build Tools**: npm scripts
- **Hosting**: Static web hosting

### Use Cases

- Agile sprint planning sessions
- Backlog refinement meetings
- Story point estimation training
- Cross-team alignment on story sizing

This project demonstrates a practical application of web technologies to solve a common challenge in Agile software development, providing teams with a consistent and data-driven approach to story point estimation.

#### **1\. Intelligent Story Point Calculation**

– **Hybrid Fibonacci Algorithm**: Implements a sophisticated calculation method that considers multiple weighted factors

– **Rule-Based Overrides**: Includes specific rules for edge cases (e.g., when unknowns or dependencies are maxed out)

– **Nonlinear Normalization**: Uses mathematical functions to map input values to the Fibonacci sequence

#### 2\. User Experience

– **Responsive Design**: Works seamlessly across all devices (desktop, tablet, mobile)

– **Interactive Sliders**: Intuitive interface for adjusting complexity factors

– **Visual Feedback**: Real-time updates and visualizations of the estimation

#### 3\. Technical Implementation

– **Progressive Web App**: Works offline and can be installed on devices (with toast prompting user to download to homescreen)

– **Service Worker Caching**: Enables offline functionality

#### 4\. Analytics Integration

– **Google Analytics**: Tracks usage patterns and feature adoption

– **Event Tracking**: Monitors user interactions for continuous improvement

### How It Works

1. **Input Collection**:

 – Users adjust sliders for various complexity factors including:

 – Business Logic Complexity

 – Technical Implementation Difficulty

 – Dependencies

 – Unknowns

 – And more…

2. **Calculation Process**:

 – The app applies a weighted algorithm to the input values

 – Special rules are triggered for specific scenarios

 – A nonlinear transformation maps the inputs to the Fibonacci sequence

3. **Output**:

 – Story points are displayed in the Fibonacci sequence (1, 2, 3, 5, 8, 13, 21, 34)

 – T-shirt size equivalents (XS, S, M, L, XL) are provided for quick reference

 – A visual chart shows the contribution of each factor

### The Calculation Logic

The scoring system in the Story Points Calculator uses a sophisticated hybrid approach that combines rule-based overrides with a mathematical model to calculate story points. Here’s a breakdown of how it works:

#### 1\. Input Processing

– The calculator takes 8 different input factors (6 regular and 2 inverse)

– Regular factors (0-5): Higher values increase the score

– Inverse factors (0-5): Higher values decrease the score

#### 2\. Rule-Based Overrides

The system first checks for specific conditions that override the normal calculation:

1. **Maximum Uncertainty (21+ points)**

 – If both “Unknowns” and “Dependencies” are maxed out (5), the minimum score is 21

2. **High Complexity (13+ points)**

 – If either “Business Logic Complexity” or “Technical Implementation Difficulty” is maxed out (5), the minimum score is 13

3. **Neutral Case (5 points)**

 – If all sliders are at their midpoint (3), the score is exactly 5

4. **Minimum Effort (1 point)**

 – If all regular factors are at minimum (1) and all inverse factors are at maximum (5), the score is 1

5. **Maximum Effort (34 points)**

 – If all regular factors are at maximum (5) and all inverse factors are at minimum (1), the score is 34

6. **High Uncertainty (8+ points)**

 – If either “Unknowns” or “Dependencies” are maxed out (5), the minimum score is 8

#### 3\. Hybrid Fibonacci Calculation

If no overrides apply, the system uses a nonlinear calculation:

1. **Weighted Sum with Exponential Scaling**

`javascript`

**`let`** `sum = 0;for (`**`let`** `i = 0; i 0.93  → 34`

### Key Design Principles

1. **Nonlinear Scaling**

 – Uses **`Math.pow(v, 1.7)`** to ensure that higher complexity ratings have proportionally more impact

 – This reflects the reality that complex features often have exponential, not linear, impact on effort

2. **Fibonacci Sequence**

 – Uses the standard Agile Fibonacci sequence (1, 2, 3, 5, 8, 13, 21, 34)

 – The sequence accounts for increasing uncertainty with larger numbers

3. **Inverse Factors**

 – Some factors (like “Team Familiarity”) are inversely related to effort

 – These are handled by inverting their scale (6 – value) before calculation

4. **Defensive Programming**

 – Includes extensive input validation

 – Handles edge cases explicitly through the rule-based overrides

This hybrid approach provides a good balance between:

– Mathematical rigor (through the normalized scoring)

– Practical experience (through the rule-based overrides)

– Agile principles (through the Fibonacci mapping)

### Technical Stack

– **Frontend**: HTML5, CSS3, JavaScript (Vanilla)

– **Charts**: Chart.js for data visualization

– **Build Tools**: npm scripts

– **Hosting**: Static web hosting

### Use Cases

– Agile sprint planning sessions

– Backlog refinement meetings

– Story point estimation training

– Cross-team alignment on story sizing

This project demonstrates a practical application of web technologies to solve a common challenge in Agile software development, providing teams with a consistent and data-driven approach to story point estimation.


---

## Talabat UI/UX Concept
*Client: Talabat (Delivery Hero)*
URL: https://www.ahmadkarmi.com/portfolio/talabat-ui-ux-concept

### Brief
Talabat is a behemoth in the region. It was acquired Delivery Hero in 2015 and has expanded ever since. The idea that Talabat has not had a major brand refresh or UX/UI overhaul has been alarming as their previous iterations of their UI & UX have not been the most pleasing of experiences. This project proposes a tweak on the brand by creating an icon to be placed next to the text based logo and overhaul it's UX & UI to a more friendly and easier to use experience

### Scope
1. Improve the time to purchase by creating a simpler workflow
2. Improve customer satisfaction by providing clearer user experience
3. Using white space to ease the eye and provide more emphasis on actionable items/features

### Details
Of course changing the UI/UX of a longstanding application is not easy, which is why it is important to assess the most important aspects to change; those being the problems users had with the old experience. My findings show that the main issues are:

1. Lack of visual representation of the meals
2. In store navigation required constant expanding, scrolling, and compressing in order to make orders of different types
3. Clear explanation through onboarding was needed, and an expansion of services needed to be added (such as pick up, loyalty program, discovery by area, easier sign up methods, and more curated content)
4. Displaying ratings for dishes, prices, and imagery all in one easy to view viewport
5. Faster check out model and improved profile and help areas to serve customers better

The following screens attempt to solve all of the above issues:

‍

Of course changing the UI/UX of a longstanding application is not easy, which is why it is important to assess the most important aspects to change; those being the problems users had with the old experience. My findings show that the main issues are:

1. Lack of visual representation of the meals

2. In store navigation required constant expanding, scrolling, and compressing in order to make orders of different types

3. Clear explanation through onboarding was needed, and an expansion of services needed to be added (such as pick up, loyalty program, discovery by area, easier sign up methods, and more curated content)

4. Displaying ratings for dishes, prices, and imagery all in one easy to view viewport

5. Faster check out model and improved profile and help areas to serve customers better

The following screens attempt to solve all of the above issues:

‍


---

## Sultan Telecom Corporate Profile
*Client: Sultan Telecom*
URL: https://www.ahmadkarmi.com/portfolio/sultan-telecom-corporate-profile

### Brief
This project was to create a modern looking corporate profile that spoke to the cutting edge technology solutions & services that the company provides, but to also keep true to its heritage and what their loyal customer base have come to expect. It's not always easy bringing a more modern style into a company's branding collateral but I do rest easy in believing that this end result has not only satisfied the client but their loyal customer base who have already come to know the brand for what it is.

### Scope
1. To provide two versions of the corporate profile (1 with financial pitch for investors/banks and 1 for informational and sales purposes)
2. To provide a corporate profile that resonates and translates the mission, vision and branding into an aesthetically pleasing document
3. Using modern design trends and media
4. Custom designing financial charts and graphs to accompany the financial workbook

### Details
The difficulty in this project was to create something more modern without changing their historic logo. This logo has been with the company for quite some time and the stakeholders involved were adamant that right now was not the time to update their logo as that would require a complete overhaul of the branding. The corporate profile however, was the introduction to this to ease their clients into the more modern and future-tech style of branding that the client had in mind.

![](https://admin.ahmadkarmi.com/wp-content/uploads/2026/01/66cb074aa8b87a4a63fbe98e_6305bc95716b0a017d899ec2_STCD_001.jpeg)

As you can tell, the logo itself presented a unique choice of colours of Orange & Dark blue. I believe that any colours can work together to provide the right message as long as they are blended together properly and combined with different shades of the colours. This is not a rule of thumb, but generalization I  always like to begin my work with.

Playing with each one and their shades (of course in an absence of a branding guideline) to achieve a cutting edge feel. As you will notice that there is a heavy reliance on using white backgrounds to keep the content looking fresh, whilst I used faded black colours for more straight edge items such as the text. I used the orange colours to accent and break monotony as the blacks and navy blues can seemingly blend into looking like the same colour over extended time viewing such a document.

The typography presented us with an interesting problem. As the logo used two different styles of fonts. Which left me less room for creativity under the time pressure available to complete this project in time.

So we opted for a Lato Bold and Lato Light combination for the headings. Similar enough to the logo in style but different when it comes to the way it displays. I used Lato for it's straight forward characteristics but also for a clean edge look. The body also used Lato, as per the client's request. However, I originally used Roboto Regular as my font of choice.

At the end, the client loved the final piece as feedback from new and loyal clients has been excellent.

The difficulty in this project was to create something more modern without changing their historic logo. This logo has been with the company for quite some time and the stakeholders involved were adamant that right now was not the time to update their logo as that would require a complete overhaul of the branding. The corporate profile however, was the introduction to this to ease their clients into the more modern and future-tech style of branding that the client had in mind.

![](https://admin.ahmadkarmi.com/wp-content/uploads/2026/01/66cb074aa8b87a4a63fbe98e_6305bc95716b0a017d899ec2_STCD_001.jpeg)

As you can tell, the logo itself presented a unique choice of colours of Orange & Dark blue. I believe that any colours can work together to provide the right message as long as they are blended together properly and combined with different shades of the colours. This is not a rule of thumb, but generalization I  always like to begin my work with.

Playing with each one and their shades (of course in an absence of a branding guideline) to achieve a cutting edge feel. As you will notice that there is a heavy reliance on using white backgrounds to keep the content looking fresh, whilst I used faded black colours for more straight edge items such as the text. I used the orange colours to accent and break monotony as the blacks and navy blues can seemingly blend into looking like the same colour over extended time viewing such a document.

The typography presented us with an interesting problem. As the logo used two different styles of fonts. Which left me less room for creativity under the time pressure available to complete this project in time.

So we opted for a Lato Bold and Lato Light combination for the headings. Similar enough to the logo in style but different when it comes to the way it displays. I used Lato for it’s straight forward characteristics but also for a clean edge look. The body also used Lato, as per the client’s request. However, I originally used Roboto Regular as my font of choice.

At the end, the client loved the final piece as feedback from new and loyal clients has been excellent.


---

## NBK Automated Audit
*Client: National Bank of Kuwait*
URL: https://www.ahmadkarmi.com/portfolio/nbk-automated-audit

### Brief
This was a project in which we were brought on as consultants to help improve time of completion of the branding department’s branch audits. We took a mainly paper-based process, and created a digital platform in which all stakeholders can benefit from reduced time to completion and improved usability. The application consists of two main sections. The dashboard where audits are created, distributed, approved and analyzed (through a fully automated, custom analytical dashboard) and an audit section in which staff can partake in creating branch audits via their mobiles or tablets through our platform. Please do note, that all images in this project are not the final designs, these were the proposal mockups. I opted not to do this in protection to privacy of information not to be displayed online. A lot of sensitive data has also been redacted in respect to the client.

### Scope
The legacy system, before we undertook this project was mainly a paper-based and time consuming project. It involved paper-based audit sheets, manual photography, and reworking all this into a spreadsheet and PDF report at a later date. Although this system worked, and it was providing accurate results; I was contacted by a member of NBK's branding team to revisit and improve this legacy system through digital transformation. The goal was to create a quick and efficient method for staff to complete the audit whilst on the spot on the branch, without having to rely on returning to the office to compile the results. Most importantly, the biggest request for change in the Legacy system was to create an easy to use analytical dashboard with the flexibility to literally search any data field possible from these audits. These included key terms and KPI metrics.

### Details
> The goal was to create a quick and efficient method for staff to complete the audit whilst on the spot on the branch, without having to rely on returning to the office to compile the results.

‍

This project underwent two phases. The first was that of an android app on custom tablets provided by the bank to its staff members that allowed them to achieve these goals and download all the results when back at the office. This was a quick and easy phase, that was a project to be implemented quickly whilst we worked on phase 2. Phase 2 comprised of a web application that was able to do all of this without the need to go back to the office, but most importantly includes an analytical dashboard that admins and executive management could use to track and analyze the progress of these audits.

![A mockup of the potential dashboard taken from the pitchdeck presented to NBK](https://admin.ahmadkarmi.com/wp-content/uploads/2026/01/66cb07496150bf634aebcec7_6305a4e2dff6475531f9196c_003_3743a71758.jpeg)
*A mockup of the potential dashboard taken from the pitchdeck presented to NBK*

#### The Structure

The web application consisted of 2 main user types (a third was more of a read-only all-access style account, for executive management). The first was that of the admin staff, who managed the dashboard and audits. They are in charge of managing and creating new audits, and managing the user base. The system automatically distributed audit schedules but admins were also able to edit these distributions. The admin also had access to the analytical dashboard for analyzing and gaining insights from past and present branch audits. The staff accounts were the second type of accounts. These allowed staff members to manage their audit schedules distributed by the system, and take the audits on the spot from their phone or tablet whilst at the branch. It also allowed staff to see their history of branch audits and monitor their own progress.

#### Secure and Private

Due to the sensitivity of the data bring pushed into the system, we did not want anyone to stumble upon this. Even though it did not hold customer financial data or any customer data for that matter, it did have branch branding performance data that we did not want visible to just anyone, let alone the competition. This is why we used a randomized URL, SSL, and without going into detail our own private security protocols on the server. It is also not a system you can sign up for, but are only distributed accounts for which helps control the users included on the system. An added security feature we also added was a time-out feature which does not allow a user to remain idle or logged in for longer than a specified time frame. All data is saved, so even if the user times out in the middle of an audit (for example is discussing an audit item with a branch manager for too long) the staff member can log back in and continue where they left off.

#### The Key Takeaways:

1. Considerable reduction in time to completion of audits (~75% improvement on time to completion)
2. Improvement of audit accuracy (less manual work, less room for errors)
3. Enhanced ability to create insights out of data and reports filed after audits and ability to cross-reference historic audit data with newer audit data for insight creation (Literally any input or variable in the system is keyable by the analytical system and allows for data filtering and comparison)
4. Improved staff efficiency, as they now have more time to work on other tasks when back to the office
5. Flexibility of data types (web based dashboards, excel sheets, PDF reports etc... can all be generated from the click of one button, in predetermined templates to match the brand of the bank)

> The goal was to create a quick and efficient method for staff to complete the audit whilst on the spot on the branch, without having to rely on returning to the office to compile the results.

‍

This project underwent two phases. The first was that of an android app on custom tablets provided by the bank to its staff members that allowed them to achieve these goals and download all the results when back at the office. This was a quick and easy phase, that was a project to be implemented quickly whilst we worked on phase 2. Phase 2 comprised of a web application that was able to do all of this without the need to go back to the office, but most importantly includes an analytical dashboard that admins and executive management could use to track and analyze the progress of these audits.

![A mockup of the potential dashboard taken from the pitchdeck presented to NBK](https://admin.ahmadkarmi.com/wp-content/uploads/2026/01/66cb07496150bf634aebcec7_6305a4e2dff6475531f9196c_003_3743a71758.jpeg)

*A mockup of the potential dashboard taken from the pitchdeck presented to NBK*

#### The Structure

The web application consisted of 2 main user types (a third was more of a read-only all-access style account, for executive management). The first was that of the admin staff, who managed the dashboard and audits. They are in charge of managing and creating new audits, and managing the user base. The system automatically distributed audit schedules but admins were also able to edit these distributions. The admin also had access to the analytical dashboard for analyzing and gaining insights from past and present branch audits. The staff accounts were the second type of accounts. These allowed staff members to manage their audit schedules distributed by the system, and take the audits on the spot from their phone or tablet whilst at the branch. It also allowed staff to see their history of branch audits and monitor their own progress.

#### Secure and Private

Due to the sensitivity of the data bring pushed into the system, we did not want anyone to stumble upon this. Even though it did not hold customer financial data or any customer data for that matter, it did have branch branding performance data that we did not want visible to just anyone, let alone the competition. This is why we used a randomized URL, SSL, and without going into detail our own private security protocols on the server. It is also not a system you can sign up for, but are only distributed accounts for which helps control the users included on the system. An added security feature we also added was a time-out feature which does not allow a user to remain idle or logged in for longer than a specified time frame. All data is saved, so even if the user times out in the middle of an audit (for example is discussing an audit item with a branch manager for too long) the staff member can log back in and continue where they left off.

#### The Key Takeaways:

1. Considerable reduction in time to completion of audits (~75% improvement on time to completion)

2. Improvement of audit accuracy (less manual work, less room for errors)

3. Enhanced ability to create insights out of data and reports filed after audits and ability to cross-reference historic audit data with newer audit data for insight creation (Literally any input or variable in the system is keyable by the analytical system and allows for data filtering and comparison)

4. Improved staff efficiency, as they now have more time to work on other tasks when back to the office

5. Flexibility of data types (web based dashboards, excel sheets, PDF reports etc… can all be generated from the click of one button, in predetermined templates to match the brand of the bank)


---

## Kuwait SC Rebrand
*Client: Kuwait SC*
URL: https://www.ahmadkarmi.com/portfolio/kuwait-sc-rebrand

### Brief
The purpose of this project was to present the club with a modern logo redesign without sacrificing the history and culture of the club. The logo was more of a refresh, the approach was to present to the club a brand new fresh approach to merchandising and jersey design using this newly refreshed logo.

### Scope
1. Develop a more modern logo inspired by the club and Kuwaiti culture
2. Visualize the new brand with collateral and merchadise
3. Monetize the new brand through partnerships and merchandising

### Details
**Kuwait Sporting Club** (Arabic: **نادي الكويت الرياضي‎**) is a Kuwaiti professional football and basketball[1] club based in Kuwait City. The football team has won the Kuwaiti Premier League 15 times, with the most recent one coming in 2018–19. Kuwait SC also have 47 trophies to their name in Kuwaiti Club Football history. Kuwait SC plays in the Al Kuwait Sports Club Stadium, in Kaifan, which is the 5th largest stadium in Kuwait. Kuwait SC is the first Kuwaiti club to ever win an Asian title that is, the AFC Cup in 2009, 2012, 2013. Kuwait SC is also one of the first sports club to have been established in Kuwait.

- Wikipedia

#### The Idea

I believed that the current logo was an aspect of the club's history from its inception. Therefore, the logo redesign approach that I wanted to use was more of a refresh. Taking elements from the current logo, and twisting and turning them into a more modern rendition. Incorporating elements of the three rings, the sun and the inception of the club was the main target. All whilst keeping the club colours, culture and identity intact. This was the approach I wanted to present.
The club, especially under its new resurgence to winning ways has been looked at as a force to be reckoned with in the Kuwaiti Premier League. With a club growing at such a successful rate, it only made sense that the new presented branding be applied in a way to benefit the club. Such as advertising, merchandising and new jerseys. With its resurgence in popularity, merchandising can prove to be a very profitable Endeavour, especially with an updated look and feel to meet modern tastes. Below are the mockups of the jerseys, F&B, advertisement (in this case a bus advertisement example) and merchandise incorporating this new style, taste and brand.

‍

**Kuwait Sporting Club** (Arabic: **نادي الكويت الرياضي‎**) is a Kuwaiti professional football and basketball\[1\] club based in Kuwait City. The football team has won the Kuwaiti Premier League 15 times, with the most recent one coming in 2018–19. Kuwait SC also have 47 trophies to their name in Kuwaiti Club Football history. Kuwait SC plays in the Al Kuwait Sports Club Stadium, in Kaifan, which is the 5th largest stadium in Kuwait. Kuwait SC is the first Kuwaiti club to ever win an Asian title that is, the AFC Cup in 2009, 2012, 2013. Kuwait SC is also one of the first sports club to have been established in Kuwait.

\- Wikipedia

#### The Idea

I believed that the current logo was an aspect of the club’s history from its inception. Therefore, the logo redesign approach that I wanted to use was more of a refresh. Taking elements from the current logo, and twisting and turning them into a more modern rendition. Incorporating elements of the three rings, the sun and the inception of the club was the main target. All whilst keeping the club colours, culture and identity intact. This was the approach I wanted to present.

The club, especially under its new resurgence to winning ways has been looked at as a force to be reckoned with in the Kuwaiti Premier League. With a club growing at such a successful rate, it only made sense that the new presented branding be applied in a way to benefit the club. Such as advertising, merchandising and new jerseys. With its resurgence in popularity, merchandising can prove to be a very profitable Endeavour, especially with an updated look and feel to meet modern tastes. Below are the mockups of the jerseys, F&B, advertisement (in this case a bus advertisement example) and merchandise incorporating this new style, taste and brand.

‍


---

## KFAS Labs (Proposed Concept)
URL: https://www.ahmadkarmi.com/portfolio/kfas-labs-studio-concept

> Today, video has even reached the educational market with phenomenons such as linkedin learning and kahn academy. These multimillion dollar companies have shown us that users would rather ingest and learn through video rather than traditional methods.

‍

#### A Brief Introduction: The “iGen” Demographic

It is no surprise that the “Centennials” or how we’d rather call them, the “iGen”, have grown up with these smart devices since a very young age and some have been born after. This generation is of the birth year 1996 and up. The language they speak is that of connectivity, devices, social media, and the latest trends. To target this generation, and to improve their learning experience, build loyalty and optimize their time, we need to start to speak their language in the way they ingest content. It is no longer enough to focus on textual based methods of knowledge dissemination. The experience needs to be extended everywhere, from the television screens to the palm of their hand.

‍

#### On Demand, Socially Fueled

It would be ignorant to peg video only to the iGen demographic. Video, especially after on demand services such as youtube, netflix and other streaming websites has ensured maximum convenience for viewers. Today, video has even reached the educational market with phenomenons such as linkedin learning and kahn academy. These multimillion dollar companies have shown us that users would rather ingest and learn through video rather than traditional methods. Another phenomenon that has added to this is the simple integration of social media and how it integrates with video so well for its purpose (likes, commenting, customized playlists and sharing).

‍

#### Numbers to Take into Account

It is not a surprise that video is king when it comes to content consumption. It is faster to consume, more engaging, and provides the ability to become more visual. A few numbers to consider:

\- Viewers retain 95% of a message when they watch it in a video, compared to 10% (text based).

\- Social video generates 1200% more shares than text and images combined.

\- Nearly 50% of all video is watched on a mobile device.

\- By 2019, internet video traffic will account for 80% of all consumer Internet traffic

\- More than 500 million hours of video is watched on youtube each day

‍

![](https://uploads-ssl.webflow.com/66c8e6fb686e109dc7fb27df/66cb07498a385cc32dd8ce46_6305a957b58d0871caacc8a2_TheLabMockUp.jpeg)

‍

#### The Lab/The Studio

The concept of this studio is to create a multi-function video studio that caters to different types of video content. Within this studio we’ll be able to create videos for social media outlets in both long and short form video, interview style and talk show based informative programs, use the facility to record and conduct educational content such as experiments, and also be able to use the post production team to create content for digital publishing mediums such as the website, OTT platforms (such as netflix or web based streaming service) or even supplementary tutorial style courses for Udemy or even universities in the region. Of course, the equipment will also allow for on the go recording such as documentary or field recording, or even live event streaming.

We have conceptualized the name and logo below to depict this studio as we believe the lab is the perfect name for this brand. A place to experiment and provide ground breaking results based on experimentation and innovation.

‍

![](https://uploads-ssl.webflow.com/66c8e6fb686e109dc7fb27df/66cb074a8a385cc32dd8cebd_6305a986dff647c77af93c36_THELABLOGO-1.jpeg)

‍

#### The Risk: Swimming Against The Tide

Today it is difficult to fight for attention in a world where there are too many distractions that can stray us away from the right content. A world full of options when it comes to content consumption, especially amongst our youth. Entertainment and social media have taken over our time and motivation. This is no surprise as it is the form of digital media that both people and brands have invested in the most when it comes to content consumption. However, the knowledge based strategy organizations have seemed to fall behind and follow an older model of content creation. To reach our youth and ever technologically based population we need to enhance their development and quality of content they consume in order to ignite scientific thinking, environmental awareness, and better their knowledge acquisition in order to develop and drive our development (both economically and personal) and quality of life.

Of course with newer technologies, comes newer problems. However, organizations have come to fight this battle for attention to bring in their demographics to learn and acquire knowledge better through content creation. The risks of not pursuing better quality of content to consume and not partaking in a project of this stature are outlined as follows:

‍

![](https://uploads-ssl.webflow.com/66c8e6fb686e109dc7fb27df/66cb07498a385cc32dd8ce2c_6305a9b0b58d0860a7acce46_EducationalChart.jpeg)

‍

#### The Benefits:

**1) Enhance Knowledge Transfer**

With video, it has been reported that information is retained more effectively than traditional knowledge transfer when considering content creation such as the text based approach. Leading to a better knowledge based society.

**2) Socially Fueled & Viral**

With video based content, we have seen a rise in the willingness to share amongst peers, meaning more content that is created via video is more likely to be shared and go viral. Resulting in a wider acceptance and reach for knowledge transfer.

**3) Lesser Bounce Rates & More Engaging**

Quality video content tends to keep the user not only consuming content for longer periods of times, but also keeps users consuming different types of content for longer. The time to consume a video is much less than reading an article. So if a user is not happy with the content their viewing, instead of leaving, there is more time for them to discover other videos to stay engaged in the content distribution chain.

**4) Promoting Innovation**

By producing quality content that users desire and consume conveniently, we are powering them to become more innovative with their train of thought and research. By creating these videos, we also invite leaders in the field to contribute their innovative research and studies to be presented through this video platform.

**5) Building a Brand Viewers Trust**

Through video, we will be building a brand that is not only promoting science but adapting and promoting the technologies and content consumption methods that viewers consume daily.

**6) Promoting & Creating a Community**

Through partnerships and co-created projects, KFAS can create a community that fosters scientific and technological innovation. These projects will promote collaboration and help excel the scientific community into the forefront.

‍

#### Why Video?

\- 75% of senior executives watch work-related videos on business-related websites every week and that 59% of senior executives prefer to watch the video if both text and video are available on the same topic on the same page.

\- According to a survey compiled and released by Hubspot; 85% of people say they’d like to see more video from brands in 2018. This displays the customer demand for an increase in video based content.

\- Studies by consultancy Insivia.com show that adding video can improve your ability to remember concepts and details — with effects that can increase over time. Viewers retain 95% of a message when they watch it in a video compared to 10% when reading it in text.

\- A study conducted by Cisco, has shown that there is a growth in the need for internet based video traffic. Their forecast shows that by 2019, internet video traffic will account for approximately 80% of all consumer internet traffic. This shows the growing importance of getting your message across through video based content.

\- Google has released a study stating that searches related to “how to” on YouTube have grown 70% year on year. This shows the demand of educational and instructional content through video based content.

‍

![](https://uploads-ssl.webflow.com/66c8e6fb686e109dc7fb27df/66cb07498a385cc32dd8ce33_6305aa40bf3bf361bcbf9882_SideRollDevicesTheLabKFAS.jpeg)

‍

#### The Proposed Formats: VOD (Video on Demand)

The key aspect of this project is to build a studio in which KFAS can create valuable video content that can be streamed on demand by the customer at their own convenience. The on demand video model focuses on several key time frames.

**1\. Short-term Turnover Video**

**2\. Medium-term Turnover Video**

**3\. Long-term Turnover Video**

These types of videos are to target to different platforms and viewer requirements. Each time frame will focus on providing content within specific frameworks and strategies in order to fulfill the viewership’s needs.

Each time frame will be explained in more depth on the forthcoming pages.

‍

#### Live Video

Live video caters to a content consumption funnel that may be rare compared to the video on demand, but provides its own usefulness and strategy. The main aspect of this video funnel is to cater to the users who wish to view events or scenarios that will occur during live functions such as conferences or special events.

‍

![](https://uploads-ssl.webflow.com/66c8e6fb686e109dc7fb27df/66cb07498a385cc32dd8ce39_6305aa8025fb8f58038ddeb1_TheLabSocial.jpeg)

‍

#### The Short-Term Turnover Video

**Quick, Bitesized & Plentiful**

The idea of the short term turnover video is to create quick and bitesized video clips for social media such as instagram and other short form video content such as twitter. The concept of this is to recycle clips from other medium and long form content, and create new fresh bite sized video clips for daily consumption. The idea behind these videos is to have constant video based content that will keep your users engaged and coming back daily to keep updated with science videos. This type of content is created through a calendar which focuses on creating many different short form videos throughout a day to be released throughout the week. By creating 5-6 short form clips a day, these can be distributed throughout the week by posting 2-3 per day. The benefit of this is to begin creating a library of short clips to be used on social media where your users will be to absorb this type of content. We must not ignore the short form videos as the majority of our userbase will be looking for content less than two minutes to view whilst on the go throughout the day. Our main target here will be mobile and social users. Examples of this type of video can be seen through Scientific American’s 60 second science. Useful clips that are created such as spots from interviews with KFAS guests, experiments from the medium form content or even highlights and/or trailers from the long form content.

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![](https://uploads-ssl.webflow.com/66c8e6fb686e109dc7fb27df/66cb07498a385cc32dd8ce3c_6305aaa22504ff1a021ec31a_KFASTHELABdesktopmedium.jpeg)

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#### The Medium-Term Turnover Video

**Programatic, Strategic & Social**

The purpose of the medium-term turnover video is to provide engaging and educational content that is to be consumed mainly behind a desk (not limiting to as mobile will also be a substantial target). The purpose of this media is to keep users engaged through more details programmatic video content. An example of this can be seen in programs such as the Science Show on youtube; that produce an average of 6-7 minute videos on scientific topics that go more in depth than the short-form video. These videos will strategically outline concepts and have a schedule based on themes and ideas. The medium term videos can also be applied to special guests that KFAS bring in for conference. The best medium to host these videos would be on the website, youtube and even on smart tv and smart phone applications through a CMS specialized system for video such as the NatGeo smart TV app. The content schedule for this would be to ideally put out 2-3 medium term videos a week. However, from a content creation standpoint, we would ideally be planning for 5-6 videos a week. Giving room to create a backlog of videos to be used as an archive, and to always have unreleased videos ready to be published in line with a content schedule. This allows for programatic posting, and clock-work like distribution of video.

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![](https://uploads-ssl.webflow.com/66c8e6fb686e109dc7fb27df/66cb07498a385cc32dd8ce3f_6305aa02bd83e645f7957b0f_thelabsnetflix.jpeg)

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#### The Long-Term Turnover Video

**Depth, Detail & Cyclical**

The long term video is based on a cyclical schedule. This is long form, high quality, carefully created content. This schedule will see one documentary style video to be completed per year. This is to be distributed on OTT/VOD platforms such as Netflix. The purpose of creating this long form type of content is to disseminate quality programs throughout the Middle East, expanding the knowledge transfer of scientific and technological topics.

In the example seen on the left, the concept of a documentary about Kuwait’s Oceans would focus on the importance of the Kuwaiti heritage and history of the pearl divers, and how this important history is being damaged by pollution. Bringing in experts to discuss scientific methods of solving this problem can not only create awareness across the region, but also allow for the scientific community to theorize a science and technological based solution for this. The more in depth and detailed these types of documentaries are, the more effectively KFAS can reach its mission statement and organizational values.

The great thing about this long form content, is that once completed it can be broken down and recycled into the short and medium form type of content. Creating trailers, advertisements, (medium form) or even just snippets for social media and teaser campaigns (short form). This ensures and adds to the content creation funnel.

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![](https://uploads-ssl.webflow.com/66c8e6fb686e109dc7fb27df/66cb07498a385cc32dd8ce49_6305aabc965c425e0d106001_livevideothelabs.jpeg)

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#### The Growing Need for Live Video

**Up to Date & Engaging**

The potential to reach thousands (or more) of new customers with the click of a button is becoming a reality. Organization’s in today’s work are convinced on the benefits of live video from a marketing perspective. But, what about from an educational perspective? Although, most live videos are actually watched through a stream after the live video has happened, the actual rates of live viewership is growing. This presents a fantastic opportunity for quality content to be created and engaged with live. With the growth of Instagram live, youtube live, and many other video streaming services, brands and organizations are racing to acquire their share of the market of this growing desire from the market.

The great thing about live TV, is that it is RAW. It creates a closer connection with your viewership as they feel a part of the process and it begins to build and fortify your customer loyalty.

It is said that the live streaming industry in 2016, was valued at more than $30 billion. These numbers were at an early phase where live video began to rise as an online trend. It is projected that in 2021, these numbers will more than double to become closer to a $70 billion dollar industry. These numbers show the growing importance for brands, especially those in niche markets such as Science.

Live streaming can be used to capture behind the scenes from lectures and events that KFAS conduct, it can be used to show the team working on upcoming video content, it can be used to educate and disseminate knowledge such as educational content and webinars. These can be conducted in both the raw methods (instagram live and similar social live video videos) or even as a post production quality video such as youtube live streams (using the studio, we can add overlays, animated graphics and transitions during a live broadcast).

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![](https://uploads-ssl.webflow.com/66c8e6fb686e109dc7fb27df/66cb07498a385cc32dd8ce4c_6305aacbdb61787ba60dc403_ANALYTICSLABS.jpeg)

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#### Analytics & Usage Data

**Comprehensive & Real-Time**

Video is the most engaging form of content on the planet. While it is powerful as a type of media, it also provides deep insights into the behavior of your audience.

It is important to access detailed data on who is viewing your content, how it is being viewed (devices, browsers, operating systems), how long it is being viewed, and where viewers are coming from.

With these types of platforms you can analyze player loads, views, viewed minutes, percent of content viewed, new viewers, unique viewers, attention span, top domains, geography, traffic sources, search terms, and more.

With the right analytical data, organizations can make the right decisions on which videos are working for them and which are not, which comes in to heavy importance on regular scheduled video forms such as the short and medium term video.

This data is also a feed that can provide insights on ROI of video and allow decision makers to make the right decisions when it comes to their video strategy.

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![](https://uploads-ssl.webflow.com/66c8e6fb686e109dc7fb27df/66cb07498a385cc32dd8ce29_6305aae7dff647743cf94955_THELABcontentstrategy.jpeg)

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#### Overview of the Content

**Dedicated & Recyclable**

The content market is in a debate about quality vs quantity. When the reality of it, is that both are equally as important. However, by focusing on quality content, especially when it comes to video, you are in turn creating quantity.

Each content length form has it’s own specific parameters in which a specific strategy is applied. In each scenario, quality is the main goal, not quantity. But, within itself, by creating quality content, you are in turn able to create a large number of quality content.

This is because each form of video is able to be shared and recycled into each other. The long form content can be snipped into short form snippets or teaser campaigns to be shared and added into the short form content schedule. The long form content can also be cut down into clips and trailers that can be shared amongst the medium form content strategy. The medium form content, can also be shared into as clips and snippets into the short form content. The same applies to live video. Events and webinars can be clipped into being shared as medium and/or short form content, but also can be repurposed and sent to post production for long or medium form content. This is the beauty of creating a realistic content schedule and focusing on quality. From there, each form of video content is able to supplement and aid in the quantity of the other with minimal work and effort from the post production team. The chart on the right hand side helps to illustrate this content framework.

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#### Birds Eye View of The Studio: Outlined

1\. Multi-Purpose Studios & Production Team

2\. Post Production & Post Production Team

3\. Live Vide & Live Video Team

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![](https://uploads-ssl.webflow.com/66c8e6fb686e109dc7fb27df/66cb074a8a385cc32dd8ceca_6305aaf90910de3621b77257_TheTeam.jpeg)

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The content market is in a debate about quality vs quantity. When the reality of it, is that both are equally as important. However, by focusing on quality content, especially when it comes to video, you are in turn creating quantity.

Each content length form has it’s own specific parameters in which a specific strategy is applied. In All three of these sections will outline the basic functions of the studio. Section A will be focused mainly on the studio itself. Where the majority of the video recording occurs. This studio should be dynamic in order to match sets and scenarios for different recording purposes. One of our solutions includes using a green screen drop down in order to make use of background changes in order to dynamically change the settings. Mobile sets such as desks and setups should be in place in order for the set to dynamically change. Each recording set up should be marked and prepared with presets to quickly adjust to each desired recording set up. Section B mainly consists of computer hardware, software and the team that will turn the video recorded from both the studio and live video into post production quality. This is where the editing, motion graphics and manipulation of videos happen. Section C consists of a team that is mobile and has hardware that can be used on the go for out of studio recording. This team will be focused mainly on live video, but can be used to also record and produce video for events or any other situation that requires a mobile team. Each section will integrate with each other heavily. Section B will be the intermediary that functions most with the other two sections.


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## Items Details Page Design (Bleems)
*Client: Bleems S.P.C.*
URL: https://www.ahmadkarmi.com/portfolio/bleems-items-details-page-design

### Brief
During my tenure at Bleems, I identified a critical opportunity to improve the performance of our item details page. The existing design, while functional, left room for improvement in terms of driving conversions. By focusing on optimizing the information architecture and refining the layout, I sought out with the team to create a page that struck the perfect balance between relevance, usability, and visual appeal.

### Scope
The scope of work for building this feature is centered around five key sections, covering everything from planning the layout of the item details page to its successful delivery and measurable outcomes.

#### Information Architecture

The content should be organized to make critical purchasing details—such as the product name, price, delivery options, and Add to Cart button—immediately visible and scannable. Related information, like dimensions, product descriptions, and reviews, should be grouped into clearly defined sections to avoid clutter and enhance readability.

#### Core Elements and Layout Priorities

The hero image carousel should feature high-quality product images with intuitive navigation and pinch-to-zoom or fullscreen capabilities. The product name, price, and trust-building indicators like "Trending Item" or "Same Day Delivery" must be prominently displayed. Key tools like the Size Guide and AR View should be easily accessible, with labels or instructions to guide interaction. The Similar Products section should display visually complementary items in a horizontal scroll, and the Add to Cart button should be bold and paired with a simple quantity selector.

#### User Experience Enhancements

Fast delivery options like "Same Day Delivery" should be highlighted with clear icons, and users should be able to check delivery availability by location. The star rating and number of reviews should be clickable to bring users directly to the reviews section. Icons for sharing, favoriting, and product enlargement should be consistent and easy to access, ensuring interactive tools like AR do not overwhelm the user or distract from the primary purchase flow.

#### Success Metrics

Success will be measured through at least a 2.5% improvement in conversion rates and a least a 5% reduction in bounce rates. These metrics will validate the effectiveness of the redesign and highlight user engagement improvements.

#### Deliverables

Deliverables include low and high-fidelity prototypes that showcase the redesigned layout, along with detailed UI designs integrating tools like the Size Guide and AR feature. Comprehensive documentation will accompany these designs, outlining technical details and user flow to ensure smooth development. Final usability testing and A/B tests will validate the page's impact on usability and conversion.

### Details
#### Figma Prototype:

The redesigned Item Details Page successfully addressed the objectives outlined in the scope of work, delivering measurable improvements in user engagement and conversion rates. We were able to increase conversions by approximately 3.4% and reduce our bounced rate by almost 6%!

By focusing on clear information architecture and intuitive layouts, the page ensured that essential details—such as product name, price, and Add to Cart functionality—were prominently displayed and easy to navigate. Grouping related information like dimensions, product descriptions, and reviews into distinct sections reduced visual clutter and enhanced readability.

This project demonstrates a strategic approach to improving e-commerce experiences by putting our user's needs first in order to achieve our desired business goals.

Please see the below images to see the final design.

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#### Figma Prototype:

The redesigned Item Details Page successfully addressed the objectives outlined in the scope of work, delivering measurable improvements in user engagement and conversion rates. We were able to increase conversions by approximately 3.4% and reduce our bounced rate by almost 6%!

By focusing on clear information architecture and intuitive layouts, the page ensured that essential details—such as product name, price, and Add to Cart functionality—were prominently displayed and easy to navigate. Grouping related information like dimensions, product descriptions, and reviews into distinct sections reduced visual clutter and enhanced readability.

This project demonstrates a strategic approach to improving e-commerce experiences by putting our user’s needs first in order to achieve our desired business goals.

Please see the below images to see the final design.

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## Checkout & Purchase Flow Optimization
*Client: Bleems S.P.C.*
URL: https://www.ahmadkarmi.com/portfolio/checkout-purchase-flow-optimization

### Brief
Our goal with this project was to redesign the checkout experience to improve usability, eliminate friction, and achieve better business outcomes. For consumers, the focus was on creating a checkout process that was seamless, intuitive, and personalized by breaking it into logical steps that matched their behavior.

Our historical analytics revealed a significant drop-off in the add-to-cart conversion rate during checkout. To understand why, we conducted customer interviews and discovered that new customers (especially first timers coming from marketing campaigns) found the our one-page checkout overly long and confusing. They struggled with the lack of clarity about their progress through the process and felt overwhelmed by the mix of what seemed to them like unrelated actions—personalizing orders, entering delivery details, and making payments—all crammed into one page.

For the business, the primary objective was to simplify the journey from product selection to payment (reducing as much friction as possible), boosting conversion rates while also introducing innovative, user-centric features like the "I don’t know the address" option. This not only solved a common gifting pain point but also helped differentiate our experience from competitors. Initially, we aimed for a 7% improvement in conversion rates, but we ultimately achieved an impressive 16% increase.

![__wf_reserved_inherit](https://admin.ahmadkarmi.com/wp-content/uploads/2026/01/67883b48375d3078d5b56ded_678838e7a1e66565cf5ccc6c_Checkout001.jpeg)

### Scope
To achieve these ambitious goals, we reimagined the checkout flow from the ground up.

We went about solving this problem in the following ways:

#### **Step-by-Step Breakdown:**

- Transformed a previously lengthy, one-page checkout process into a three-stage flow focused on clarity and relevance.
- The stages were split into Personalization, Delivery, and Payment, matching user behavior and decision-making at each point.

#### **Personalization:**

- Enabled users to customize their gifts by adding card messages, toppers, and accessories. This was presented as the first stage, keeping the focus on the emotional intent of gifting.

#### Delivery:

- Introduced the groundbreaking “I don’t know the address” feature, which allows users to enter the recipient’s name and phone number. This information is privately retrieved by our team, ensuring both convenience and privacy while eliminating potential blockers for users unsure of delivery details.

#### Payment Optimization:

- Dynamically adjusted payment methods based on the user’s device (e.g., Apple Pay for iOS, Android Pay for Android) and location (e.g., KNET for Kuwait-based users).

#### Streamlining the User Journey:

- Ensured the navigation between steps felt natural, with clear progress indicators and actionable CTAs throughout.

### Details
The redesigned flow significantly impacted both user experience and business performance. We achieved two major zones of success:

#### Consumer Impact:

- The step-by-step process eliminated the feeling of being overwhelmed by a cluttered checkout page.
- Features like dynamic payment methods and address retrieval gave users the tools to focus on their intent (gifting) instead of logistics, making the process feel tailored to their needs.

#### Business Results:

- The checkout redesign achieved a nearly 16% increase in add-to-cart conversion rates, far surpassing the original goal of 7%.
- The "I don’t know the address" feature became a standout differentiator, earning positive user feedback and reducing drop-offs by tackling a common gifting pain point.
- Dynamic payment options reduced friction at the payment stage, particularly for mobile users, resulting in higher completion rates.

This project underscores the importance of blending consumer empathy with data-driven design decisions to create a checkout flow that delights users and drives tangible business results.

The redesigned flow significantly impacted both user experience and business performance. We achieved two major zones of success:

#### Consumer Impact:

– The step-by-step process eliminated the feeling of being overwhelmed by a cluttered checkout page.

– Features like dynamic payment methods and address retrieval gave users the tools to focus on their intent (gifting) instead of logistics, making the process feel tailored to their needs.

#### Business Results:

– The checkout redesign achieved a nearly 16% increase in add-to-cart conversion rates, far surpassing the original goal of 7%.

– The “I don’t know the address” feature became a standout differentiator, earning positive user feedback and reducing drop-offs by tackling a common gifting pain point.

– Dynamic payment options reduced friction at the payment stage, particularly for mobile users, resulting in higher completion rates.

This project underscores the importance of blending consumer empathy with data-driven design decisions to create a checkout flow that delights users and drives tangible business results.


---

## AOU Digital Bookstore
*Client: Arab Open University*
URL: https://www.ahmadkarmi.com/portfolio/aou-digital-bookstore

### Brief
Knowledge is the absorption of data and processing it from raw data to insights. Understanding these insights is what helps generations move forward and innovate. This was the main motivating factor of this project, was to provide an educational institute with a medium in which it's students can be empowered by gaining knowledge. The AOU interactive digital bookstore was born.

### Scope
1. To build a digital bookstore where AOU could publish their self-published books for students
2. To make the books accessible to both students and non-students at different rates
3. Students who purchased the physical book to be able to download the digital for free
4. An interactive set of books that help translate information into knowledge
5. Linking student IDs and accounts to the app
6. Allowing enrolled students to do workbooks and quizzes inside the book, and providing the results to their professors via an online dashboard
7. Allowing students to take timed exams from the app and track time in and out of the app (to avoid cheating)
8. Providing exam results to their professors
9. All this data using TINCAN API and pushing it to the professors through the LMS (moodle)

### Details
**Problem Statement:** Printed books were being done through several partners which resulted in delays of printed books reaching to students at certain times.

**Solution:** A mobile/tablet application with a newstand that resembles each respective platforms' app store was made to house the university owned IP textbooks. This allowed the books and their versions to be updated on demand and available to students OTA regardless of what device their were using.

![](https://admin.ahmadkarmi.com/wp-content/uploads/2026/01/66cb074adde493cf8131d758_6305bf68cc066681e867f7a1_StoreSide002.jpeg)

**Problem Statement:** The logistics and warehousing for the printed books was costing the university (redacted number) per year. A fee they surely wanted to see reduced.

**Solution:** Instead of keeping a heavy reliance on printing these books, keeping editions in warehouses and delivering them every semester; it was decided to have every student's ID linked to the application in which their concerned books for that term would load. External parties (non university connected IDs) would purchase these books at higher prices. Creating a revenue stream that does not require more than some basic AWS hosting. The whole solution was a fraction of the cost of logistics, warehousing and printing.

![](https://admin.ahmadkarmi.com/wp-content/uploads/2026/01/66cb074adde493cf8131d753_6305bf7fdff647093bfa3351_StoreSide003.jpeg)

**Problem Statement:** The professors who authored and taught these classes wanted a method to monitor and understand their students' progress with their content in order to understand how to improve the content for next editions.

**Solution:** These books each have their own unique set of analytics. Each professor can browse usage of each and every student. From the in book exercises/quizzes to the time spent on each page. Every interaction is logged in. This allows professors to see what content required the most time to understand and what tools served the students best in helping them gain knowledge out of the content. The next editions would be updated based on the analytical reports and insights gained from these usage statistics, and cross referenced with the students' grades and progress reports.

![](https://admin.ahmadkarmi.com/wp-content/uploads/2026/01/66cb074adde493cf8131d764_6305bf964bf4b819f2995f20_features003_cf5ba4b29f.jpeg)

**Problem Statement:** The students wanted books that were not static, and provided tools to help them understand the content and not just be able to read at their convenience.

**Solution:** The students were able to take benefit of interactive elements which help take the raw content they are studying and help them understand it. Tooks like interactive charts. For example, imagine sliding your finger over a supply and demand curve and see what happens in real time to those variables? With this project, that was possible. We input features such as advanced search, highlighting, notetaking, notesharing, video, audio, LMS integrated end of chapter exercises (With real time advice on why certain answers were right or wrong) and the list goes on.

![](https://admin.ahmadkarmi.com/wp-content/uploads/2026/01/66cb074adde493cf8131d75e_6305bfbbc3f1f552b81a9108_features008_45a7b3eb0b.jpeg)

#### Conclusion:

All screens used here are from the book for course code AR112, an Arabic language and literature book. The brief was to convert it as closely as possible the the print design considering the branding, and to implement an easy to use and utilitarian user experience approach.
With a project like this, one has to be careful to understand not only their client's needs, but also those of the users who will be using the application. This is why we focused on navigation and search features to be compelling and accurate in the Arabic language (includings searches with ligatures/kashidas). We realized that the books that were going to be published in this application were approximately 600+ pages in print format.

So one of our main goals was to allow students to find what they were looking for in such large textbooks. We also created a web client version for easy reference in classrooms (airplay from tablets to projectors was also an option). ​​​​​​​

![](https://admin.ahmadkarmi.com/wp-content/uploads/2026/01/66cb074adde493cf8131d761_6305bfd3db178155942afd91_interactions_AOUDL_518333f285.jpeg)

‍

**Problem Statement:** Printed books were being done through several partners which resulted in delays of printed books reaching to students at certain times.

**Solution:** A mobile/tablet application with a newstand that resembles each respective platforms’ app store was made to house the university owned IP textbooks. This allowed the books and their versions to be updated on demand and available to students OTA regardless of what device their were using.

![](https://admin.ahmadkarmi.com/wp-content/uploads/2026/01/66cb074adde493cf8131d758_6305bf68cc066681e867f7a1_StoreSide002.jpeg)

**Problem Statement:** The logistics and warehousing for the printed books was costing the university (redacted number) per year. A fee they surely wanted to see reduced.

**Solution:** Instead of keeping a heavy reliance on printing these books, keeping editions in warehouses and delivering them every semester; it was decided to have every student’s ID linked to the application in which their concerned books for that term would load. External parties (non university connected IDs) would purchase these books at higher prices. Creating a revenue stream that does not require more than some basic AWS hosting. The whole solution was a fraction of the cost of logistics, warehousing and printing.

![](https://admin.ahmadkarmi.com/wp-content/uploads/2026/01/66cb074adde493cf8131d753_6305bf7fdff647093bfa3351_StoreSide003.jpeg)

**Problem Statement:** The professors who authored and taught these classes wanted a method to monitor and understand their students’ progress with their content in order to understand how to improve the content for next editions.

**Solution:** These books each have their own unique set of analytics. Each professor can browse usage of each and every student. From the in book exercises/quizzes to the time spent on each page. Every interaction is logged in. This allows professors to see what content required the most time to understand and what tools served the students best in helping them gain knowledge out of the content. The next editions would be updated based on the analytical reports and insights gained from these usage statistics, and cross referenced with the students’ grades and progress reports.

![](https://admin.ahmadkarmi.com/wp-content/uploads/2026/01/66cb074adde493cf8131d764_6305bf964bf4b819f2995f20_features003_cf5ba4b29f.jpeg)

**Problem Statement:** The students wanted books that were not static, and provided tools to help them understand the content and not just be able to read at their convenience.

**Solution:** The students were able to take benefit of interactive elements which help take the raw content they are studying and help them understand it. Tooks like interactive charts. For example, imagine sliding your finger over a supply and demand curve and see what happens in real time to those variables? With this project, that was possible. We input features such as advanced search, highlighting, notetaking, notesharing, video, audio, LMS integrated end of chapter exercises (With real time advice on why certain answers were right or wrong) and the list goes on.

![](https://admin.ahmadkarmi.com/wp-content/uploads/2026/01/66cb074adde493cf8131d75e_6305bfbbc3f1f552b81a9108_features008_45a7b3eb0b.jpeg)

#### Conclusion:

All screens used here are from the book for course code AR112, an Arabic language and literature book. The brief was to convert it as closely as possible the the print design considering the branding, and to implement an easy to use and utilitarian user experience approach.

With a project like this, one has to be careful to understand not only their client’s needs, but also those of the users who will be using the application. This is why we focused on navigation and search features to be compelling and accurate in the Arabic language (includings searches with ligatures/kashidas). We realized that the books that were going to be published in this application were approximately 600+ pages in print format.

So one of our main goals was to allow students to find what they were looking for in such large textbooks. We also created a web client version for easy reference in classrooms (airplay from tablets to projectors was also an option). ​​​​​​​

![](https://admin.ahmadkarmi.com/wp-content/uploads/2026/01/66cb074adde493cf8131d761_6305bfd3db178155942afd91_interactions_AOUDL_518333f285.jpeg)

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