Product Management - Tahir Shahzad

How to Know If Your Startup Has a Product Problem or a Marketing Problem

Posted on June 2, 2026 - 4:58 pm by Tahir Shahzad

Most founders in crisis do the same thing: they guess.

Retention is low, so they assume the product needs more features. They ship for three months. Nothing moves. So they pivot to marketing, hire an agency, run ads, rewrite the homepage. Still nothing. Now they’re six months behind, the runway is shorter, and the board is asking questions they can’t answer.

The problem was never a lack of effort. It was a lack of diagnosis. Before you open Jira to restructure the backlog or double your performance marketing spend, you have to answer a high-stakes question: Does your startup have a product problem or a marketing problem?

A product problem and a marketing problem can look identical from the outside, low signups, flat growth, high churn. But they have completely different causes, and treating one with the cure for the other doesn’t just fail to help. It actively makes things worse.

Why This Distinction Matters More Than You Think

According to CB Insights‘ updated 2024 analysis of 431 failed startups, 43% failed due to poor product-market fit. Only 14% cited poor marketing as a primary cause. Running out of cash affected 70% of failures.

Here’s what that means in plain terms: most founders who think they have a marketing problem actually have a product problem. And the ones who think they have a product problem are sometimes spending engineering cycles on the wrong diagnosis, when the real issue is that the right people are simply not finding them.

Misreading the signal costs money. It costs time. At early stage, it can cost the company.

The Core Distinction: What Are You Actually Measuring?

Before running any diagnostic, it helps to clarify what each problem actually means.

Product Problem: The product is not creating enough value for the people who use it. They try it, they don’t experience the promised outcome, and they leave. It doesn’t matter how many people you get through the door: they won’t stay, they won’t pay, and they won’t tell others.

Marketing Problem: The product creates real value for the people who use it, but not enough of the right people are finding it. The pipeline is broken, the message is wrong, or you’re targeting the wrong segment entirely.

To accurately isolate where your delivery engine is breaking down, you must separate user acquisition from user retention.

what happens after the first meaningful interaction with your product?

Diagnostic Tests

Test 1: The “Leaky Bucket” Retention Curve Test

Pull your cohort retention data. Look at what percentage of users who signed up in any given month are still active 30, 60, and 90 days later. Then look at the shape of the curve.

If the curve drops steeply and never flattens, you have a product problem. Users are leaving before they find value, which means the value either isn’t there, isn’t reachable, or isn’t clear.
If the curve flattens at a non-zero baseline, meaning a subset of users stick around indefinitely, you likely have a marketing problem. Your product works for someone. The question is whether you’re reaching enough of those people.
if your paid users churn at similar rates to your free users, the product problem is real. If free users churn and paid users stick, you have a people problem: you’re getting the wrong users in the door.

This is one of the clearest product-market fit signals available. When retention stabilizes and improves without aggressive intervention, it means the underlying problem your product solves is persistent and painful enough that users return on their own. A curve that never flattens is telling you the opposite.

Test 2: The Source-of-Truth Test

You need to know why users left. The problem is that most founders never ask at the right moment.

On one product I worked on, we added a single survey form directly on the deactivation screen. Not an email sent three days later. Not a follow-up call. Right there, before the user clicked the final confirm button. The design was intentional: one question, a short list of options, and a Submit button. No long form.

The result was that users actually answered it. When people are at the exit point, they are already decided and they are often willing to say why, as long as you make it easy. A five-option list takes three seconds to complete. A blank text box gets abandoned.

The options we gave were simple and honest:

It was too hard to use
It was missing a feature I needed
I found something else that works better for me
It was not what I expected when I signed up
It was too expensive for the value I got

Those five options map directly to two buckets.

Product bucket: too hard to use, missing a feature, not enough value for the price. These point to a gap between what the product delivers and what the user needed it to do.

Marketing bucket: found something else, not what I expected. These point to a gap between who the product is for and who is actually being reached.

The marketing bucket answers are about fit between the person and the product at the moment of acquisition: they got there through a wrong message, a wrong channel, or wrong targeting. The product bucket answers are about value delivery after acquisition.

If 70% or more of your responses fall into the product bucket: rebuild before you recruit.
If 70% or more fall into the marketing bucket: your product works, you are just talking to the wrong people in the wrong way.

Test 3: The Word-of-Mouth Test

This is the test most founders forget to run, and it’s one of the most reliable.

Organic word of mouth, meaning referrals that happen through private channels like WhatsApp messages, Slack groups, or direct peer recommendations, is the clearest signal that a product has crossed a value threshold. According to research on organic growth, product retention directly drives new organic user acquisition. The longer and more consistently someone engages with your product, the more they talk about it.

Instead of relying on opinions, assumptions, or internal discussions, look at the data:

Are users referring others without being incentivized to do so?

You can also measure this directly. In one product, we added a simple survey consisting of a single question and a scale. It gave us a lightweight way to track customer satisfaction and sentiment over time. The feedback from real users proved far more valuable than running internal workshops and debating assumptions in meeting rooms. Customers will often tell you exactly how they feel if you make it easy for them.

If you have zero organic referrals after 6+ months and 500+ signups, that is not a marketing problem. People do not stay quiet about products that genuinely help them. They tell someone. The absence of that behavior points to a product that isn’t creating the kind of value that gets talked about.

If you do have organic referrals, even a small trickle, but your overall growth is flat, that’s a marketing problem. You have proof of value. You need distribution.

Test 4: The Sean Ellis Test

Rahul Vohra at Superhuman popularized this for the mainstream. Sean Ellis originally developed it. The question is simple:

“How would you feel if you could no longer use this product?”

very disappointed,
somewhat disappointed,
not disappointed.

If fewer than 40% say “very disappointed,” you have a product problem: specifically, the product is not yet indispensable enough to the people using it. The insight Vohra added to Ellis’s framework: rather than rebuilding features, narrow the segment. Find the users who would say “very disappointed” and understand everything about them. That is your real market.

If 40% or more say “very disappointed” but growth is still stalled: marketing problem. The product has product-market fit, but it’s not reaching the right people at scale.

The Honest Mistake Most Founders Make

In 2025, building became cheaper and faster than ever. This created a new failure mode: founders iterate on the product in response to signals that are actually marketing signals, and they add marketing spend in response to signals that are actually product signals.

The incentive structure makes this worse. Engineers want to build. Marketers want budgets. Both have a professional interest in presenting the problem as solvable through their particular lens.

This is where an independent perspective, someone with no stake in either the product backlog or the media spend, becomes the highest-leverage thing you can invest in. Not to tell you what to build or what to say. But to read the signals you already have and tell you which problem you actually have.

As Sean Ellis’s growth pyramid framework makes clear: sustainable growth only comes after you have unlocked organic. And organic only comes after the product is genuinely worth talking about. You cannot skip this step with ad spend.

A Quick Decision Framework

Use this as a starting point. It’s not a final answer; it’s a map.

Strong signals of a product problem:

☐ Retention curve drops steeply with no flattening, across all user cohorts
☐ Paid users churn at similar rates to free users
☐ Exit interviews cite confusion, lack of value, or unmet expectations
☐ Zero organic referrals after 6+ months
☐ Users engage once and disappear, regardless of channel or campaign
☐ Sean Ellis score below 40% “very disappointed” among active users

Strong signals of a marketing problem:

☐ Retention curve flattens: some users genuinely stick
☐ Paid users stay; free users churn, which points to the wrong audience at the top of funnel
☐ Exit interviews cite “not the right fit,” “signed up thinking it did X,” or “found something else”
☐ You have organic referrals but can’t replicate them at scale
☐ Your best customers came from a narrow, specific channel, and you haven’t doubled down on it
☐ Sean Ellis score above 40%, but only for a small, specific segment

What To Do Next

Once you have identified the bottleneck, use this product management framework to realign your team’s time, budget, and attention on the problem that is actually limiting growth.

If the Signals Point to a Marketing Problem

Stop building features the market has not asked for. Start narrowing your focus.

Find the smallest viable segment where your retention curve is already healthy and users consistently receive value. Then rebuild your growth strategy around those users.

Focus on:

Clarifying the value proposition in plain language
Identifying the channels where your best customers already spend time
Refining your targeting to reach more people who resemble your retained users
Testing messaging based on customer outcomes rather than product features
Investing in distribution before investing in additional functionality

The goal is not to attract more traffic. The goal is to attract more of the right traffic.

If the Signals Point to a Product Problem

Growth will not fix a product that users do not want to keep using.

Before building new functionality, focus on understanding where users struggle and why they leave.

Prioritize:

User interviews with both active and churned customers
Onboarding analysis and drop-off investigation
In-app surveys and contextual feedback collection
UX simplification and friction reduction
Reliability, performance, and technical debt improvements

Many teams respond to weak retention by shipping more features. In reality, the fastest path to growth is often making the existing experience easier, faster, and more reliable.

Run a “Bus Factor” Audit on Your Value Proposition

Product and marketing should be able to describe the primary customer outcome using the same language. A useful exercise is to ask both teams independently:

“What can bring an Aha moment to our customer?”

If the answers differ significantly, users are likely experiencing a gap between what is promised and what is delivered.

Focus on:

A single primary customer outcome
Consistent messaging across marketing and onboarding
A clear path to value during the first user session
Success metrics that both teams share

The closer the promise and experience become, the easier growth becomes.

Establish Automated Guardrails for User Feedback

Stop guessing what is broken. Implement continuous user research methodologies. Do not wait for quarterly reviews or anecdotal feedback. Use micro-feedback triggers to capture customer signals.

Examples include:

Post-onboarding satisfaction surveys
Feedback prompts after key actions
Churn and cancellation surveys
Customer interviews every month
Support ticket trend analysis

A simple one-question survey can often reveal more about customer sentiment than hours of internal debate.

If You Still Cannot Tell

Sometimes the signals are mixed.

Retention may be acceptable but inconsistent. Referrals may exist but not at meaningful scale. Different customer segments may behave in completely different ways.

This is not a failure of observation. It usually means one of three things:

Both product and marketing need improvement
The product solves a real problem, but only for a narrow audience
The wrong customer segment is being targeted

These situations are difficult to diagnose from inside the business because teams naturally become attached to their assumptions.

An experienced external perspective can often identify patterns, blind spots, and opportunities much faster than the team can on its own.

The important thing is to follow evidence rather than opinions. Growth problems become easier to solve when you stop asking who is right and start asking what the data is telling you.

📈 Closing Thoughts

if your product relies on aggressive, non-stop ad spend to survive because your natural retention is zero, you do not have a business—you have an expensive marketing campaign.

The data you need is often easier to access than you think, but only if you are measuring it.

Tools like GA4, Usermaven, Mixpanel, Amplitude, and PostHog can help you track retention, activation, user journeys, referrals, and churn. Combined with customer interviews, support tickets, NPS surveys, and Sean Ellis surveys, they provide enough evidence to determine whether you have a product problem, a marketing problem, or a positioning problem.

Most startups do not suffer from a lack of data. They suffer from a lack of synthesis. The signals are scattered across dashboards and conversations, waiting for someone to connect them into a coherent story.

Read those signals correctly, and you’ll know exactly where to focus your next three months of effort. Misread them, and you’ll spend the next quarter solving the wrong problem, only to wonder why nothing moved.

Discussion of the Week: Mental Health Crisis in Product and Project Management?

Posted on June 1, 2026 - 11:20 am by Tahir Shahzad

Recently, I came across a news about a female manager in the IT industry who left her managerial role and started driving an auto-rickshaw. Her income decreased significantly, but according to her, she is happier and more satisfied than she was in her corporate career.

It made me pause and reflect.

As Product Managers, Project Managers, Product Owners, Scrum Masters, and team leads, stress often becomes a normal part of our jobs. In fact, it becomes so consistent that many of us stop questioning it. We simply accept it as “part of the role.”

A student worries about failing an exam. Similarly, many PMs live with a constant fear of missing deadlines, disappointing stakeholders, or not delivering expected outcomes.

The challenge is that delivery timelines are influenced by countless variables, many of which are outside a PM’s direct control.

At the same time:

Stakeholders expect commitments to be met regardless of changing circumstances.
Teams may not always share the same sense of urgency and can become stuck debating minor details.
Priorities shift constantly.
A feature planned for months suddenly competes with a new idea that appeared in a stakeholder’s mind yesterday.
Expectations increase, but resources, budget, and timelines often remain unchanged.

Individually, these situations seem manageable.

Together, they create an environment of continuous pressure.

The main concern is that we don’t discuss the mental health side of product and project management. We talk about roadmaps, velocity, KPIs, OKRs, Agile frameworks, and delivery dates. We skip the part of anxiety, burnout, self-doubt, workplace stress, or the psychological burden of being accountable for outcomes without having full control over all the variables.

Also Read: The Prism of Priorities: Seeing Beyond Stakeholder Noise

Many organizations promote productivity, but far fewer invest in psychological safety and employee well-being.

Many teams discuss blockers, but very few discuss stress.

Many managers ask for status updates, but not enough ask, “How are you doing?”

Technically the real question is not whether stress exists in product and project management.

The question is:

Have we normalized unhealthy levels of workplace stress and burnout to the point where we no longer recognize them?

I’d love to hear from fellow Product Managers, Project Managers, Product Owners, Scrum Masters, Agile Coaches, and team leads:

What creates the most stress in your role?
Have you ever considered leaving product management or project management because of burnout?
What practices, leadership approaches, or organizational changes have helped reduce stress for you and your team?
How can we create more psychologically safe workplaces for professionals responsible for product delivery and project success?

Looking forward to the discussion.

If this resonates with your experiences or challenges, I’d love to connect and hear your perspective. Feel free to reach out https://tahirshahzad.com/contact/. Let’s continue the conversation and learn from each other.

You might want to understand if your absence breaks delivery, your system is fragile

Previous Edition of Discussion of the week: How AI First Discovery is Changing Attribution

Transitioning from Scrum to Kanban: A 3-Step Execution Checklist for Continuous High-Velocity Deployment

Posted on May 21, 2026 - 3:41 pm by Tahir Shahzad

Modern software teams are rethinking how delivery works.

For years, the Agile Software Development movement shaped the way organizations approach the product development lifecycle. Teams adopted sprint planning, retrospectives, story points, and standups to improve collaboration, structure execution, and create predictable delivery patterns.

On paper, Scrum looks complete.

It provides structure.
It introduces accountability.
It forces alignment between engineering, product, and business.
It creates a shared understanding of delivery.

And honestly, for many teams, Scrum framework is an excellent starting point. But then reality happens.

Sprint after sprint, teams start noticing patterns:

Velocity does not improve consistently
Only 60–70% of sprint work gets completed
QA receives most tickets near the sprint deadline
Developers continue pushing code until the final hours
Urgent requests disrupt sprint commitments
Retrospectives identify the same bottlenecks repeatedly

Eventually, teams enter a strange middle ground.

Sometimes delivery works.
Sometimes it fails.
Sometimes velocity is high.
Sometimes velocity collapses.

And after enough time, instability starts feeling normal. The dangerous part is not failure. The dangerous part is becoming comfortable with unpredictable delivery.

Stakeholders Do Not Care About Scrum

Here is the cold, hard truth: Your stakeholders and customers do not care about your agile purity.

This may sound harsh, but business stakeholders rarely care whether your team uses Scrum, Kanban, SAFe, Waterfall, or a custom hybrid framework.

They care about:

Reliable delivery
Product quality
Faster iteration
Predictable execution
Continuous customer value

Customers care even less. No user has ever renewed a subscription because a team conducted perfect sprint planning. Customers stay because the product continuously improves. That is the real objective of Agile.

Not ceremonies, not sprint rituals, not story points. Continuous value delivery.

The real goal is building a delivery system that supports continuous value creation throughout the product development lifecycle.

Understanding Agile, Scrum, and Kanban

Before discussing transition strategies, it is important to separate these concepts clearly because many teams use them interchangeably.

What Is Agile?

Agile Software Development is a philosophy and mindset focused on iterative delivery, collaboration, adaptability, and continuous improvement.

Agile is not a framework itself. It is a set of principles that encourage teams to respond quickly to change while delivering customer value incrementally.

What Is Scrum?

Scrum is a structured Agile framework.

It introduces:

Fixed-length sprints
Sprint planning
Sprint review
Retrospectives
Defined team roles
Definition of Done
Estimation practices
Velocity tracking

Scrum works particularly well for:

Early-stage Agile adoption
Cross-functional alignment
Teams needing execution discipline
Organizations transitioning from chaos to process

The problem is not Scrum itself. The problem is when Scrum becomes rigid while engineering reality becomes dynamic.

What Is Kanban?

Kanban focuses on continuous flow instead of fixed sprint boundaries.

Instead of asking:

“What can we finish this sprint?”

Kanban asks:

“How can we improve flow efficiency continuously?”

Core principles include:

Visualizing work
Limiting work in progress (WIP)
Continuous delivery
Reducing bottlenecks
Optimizing cycle time
Pull-based execution

Kanban shifts the focus from sprint commitment to delivery flow optimization.

Why Scrum Starts Breaking at Scale

The most common Scrum failure pattern is not bad engineering. It is WIP congestion. Too many parallel tasks create invisible delivery traffic jams.

Example:

Developer A starts Feature X
Developer B starts Feature Y
QA waits for both
Product introduces urgent request Z
Sprint deadline approaches
Half-complete work accumulates
Context switching increases
Nothing reaches production smoothly

The sprint board appears “busy.” But delivery slows down. This creates the illusion of productivity while deployment velocity declines.

The Hidden Cost of Sprint-Based Thinking

Sprint structures unintentionally encourage batch delivery. Teams often delay integration, QA, deployment, and validation until sprint closure.

This creates:

Large deployment risk
QA bottlenecks
Delayed customer feedback
Increased merge conflicts
Artificial deadlines
Burnout cycles

Ironically, teams become “Agile” while shipping less frequently.

The AI Era Changes Delivery Speed Completely

This challenge becomes even more important in the AI-assisted engineering era. Historically, a 1 or 2 week sprint made sense because humans needed time for:

Planning
Development
Refactoring
Documentation
Testing
Deployment preparation

Now AI accelerates major parts of execution. Tasks that previously required days may now take hours.

Boilerplate generation, scaffolding, testing assistance, debugging support, documentation drafting, and even architecture recommendations can happen almost instantly with AI tooling.

The bottleneck is no longer pure coding speed.

The new bottlenecks are:

Validation
Decision making
QA confidence
Production safety
Prioritization
Workflow congestion

This raises a serious question: If implementation takes one hour, what does a two-week sprint even represent?

Would we create:

1-day sprints?
4-hour sprints?
1-hour sprint planning ceremonies?

At some point, sprint boundaries stop matching engineering reality. This is where Kanban becomes significantly more powerful.

Why Kanban Fits High-Velocity AI-Assisted Teams

Kanban adapts naturally to fast-moving environments because it is flow-oriented rather than time-box oriented.

Instead of waiting for sprint completion, teams continuously:

Pull work
Validate work
Ship work
Monitor work
Improve flow

Kanban accepts that delivery speed is variable.

Some tasks may take 30 minutes.
Some may take 3 days.

The system optimizes throughput rather than forcing work into artificial time containers.

The 3-Step Execution Checklist for Transitioning from Scrum to Kanban

Transitioning does not mean abandoning discipline. It means shifting from sprint optimization to flow optimization.

Step 1: Visualize the Entire Delivery Pipeline

Most teams only visualize development status. That is not enough. Your Kanban board should expose the entire product delivery lifecycle.

A healthy engineering Kanban board usually includes stages like:

Backlog
Ready
In Progress
Code Review
QA
Validation
Ready for Deployment
Released
Monitoring

This visibility immediately exposes where work actually gets stuck. Very often, the bottleneck is not development.

It is:

Review delays
QA congestion
Approval dependencies
Deployment hesitation

Kanban makes operational inefficiencies impossible to hide.

Step 2: Aggressively Limit Work in Progress (WIP)

This is the most important transition step. Without WIP limits, Kanban becomes a prettier task board. Set explicit WIP constraints such as:

Maximum 2 tasks per engineer
Maximum 3 tickets in QA
Maximum 5 items in review

When WIP exceeds limits:

Nobody starts new work
Team members help unblock flow
Delivery becomes collaborative instead of isolated

This dramatically reduces:

Context switching
Half-completed features
Sprint spillover
QA overload

Most importantly, it improves deployment consistency.

Step 3: Shift Success Metrics from Velocity to Flow

Scrum teams often become obsessed with velocity. But velocity can be manipulated easily:

Smaller stories
Inflated estimates
Reduced scope
Story splitting

Kanban focuses on healthier metrics:

Cycle Time: How long does work take from start to production?
Lead Time: How long does a request take from idea to customer delivery?
Throughput: How many meaningful items ship consistently?
Flow Efficiency: How much time is spent actively progressing work versus waiting?

These metrics reflect real customer delivery performance.

What Guardrails Are Required for Kanban?

One misconception is that Kanban means “no process.” That is dangerous. High-speed delivery without guardrails creates operational chaos. Especially in AI-assisted environments where development speed increases dramatically. Kanban still requires strong engineering discipline.

Essential Guardrails

Clear Definition of Done

Every task must satisfy:

Code review completed
Tests passed
QA validated
Documentation updated
Monitoring configured

Strong CI/CD Automation

Continuous deployment without automation is unsustainable.

Teams need:

Automated testing
Deployment pipelines
Rollback mechanisms
Feature flags
Observability tooling

Controlled WIP Enforcement

WIP limits should be enforced operationally, not treated as suggestions.

Production Monitoring

Fast deployment requires fast detection.

Monitor:

Errors
Performance degradation
Customer impact
Infrastructure anomalies

Prioritization Discipline

Without sprint boundaries, priorities can shift too frequently. Product leadership must maintain:

Clear prioritization
Stable delivery focus
Reduced interruption frequency

Scrum vs Kanban Is the Wrong Debate

The goal is not choosing a “winning framework.” The real objective is building a delivery system that matches your product, engineering culture, and business speed.

Some teams succeed with Scrum for years. Some teams evolve into Kanban naturally. Some operate with hybrids. What matters is this:

Can your organization continuously deliver value predictably without exhausting the team?

That is the real Agile maturity test.

Final Thoughts

Scrum helped the software industry move away from rigid traditional delivery models. It introduced structure where chaos existed. But modern engineering environments are evolving rapidly.

AI-assisted development is compressing implementation timelines dramatically. The future advantage will not belong to teams that conduct the best sprint ceremonies. It will belong to teams that:

Reduce flow friction
Eliminate bottlenecks
Deploy safely and continuously
Adapt operationally in real time

Kanban is not about removing discipline. It is about optimizing movement.

And in high-velocity software delivery, movement matters more than ritual.

The Prism of Priorities: Seeing Beyond Stakeholder Noise

Posted on May 12, 2026 - 4:07 pm by Tahir Shahzad

Sometimes I wish I had a Prism of Priorities in my hands.

Not a metaphor, but a real tool. Something I could hold up to every incoming request, every feature idea, every “quick win,” and instantly see its true color; its real weight, its actual impact.

In my mind, it works like light passing through glass. A single request enters as a bright, confident beam, full of urgency and conviction. Then it refracts into a spectrum, revealing what it is truly made of; user value, business impact, technical cost, timing, and sometimes, pure bias.

The Illusion of Clarity

we do not live in a products centric storyland and in reality it might not be as helpful as it sounds.

Because the honest picture of every request would likely create more chaos than clarity. Imagine showing every stakeholder that their “high priority” request breaks into faint, scattered colors when measured against real user needs. Or discovering that multiple “critical” initiatives are simply competing opinions with no grounding in evidence.

As Ben Horowitz said:

“The hard thing about hard things is that there is no formula for dealing with them.”

Prioritization is one of those hard things. There is no perfect framework that removes ambiguity. Only better ways to navigate it.

Products Are Built on Perspectives

Products are not built in isolation. They are shaped by people, and people come with perspectives, incentives, and biases. Every wishlist carries a story;

a sales target to hit,
a customer complaint to resolve,
a feature a competitor just launched,
or simply a belief in what “should” work.

This is where many products quietly drift. According to Marty Cagan, the primary responsibility of a product manager on an empowred product tem is to manage two of the four critical products risks: Value and Viability.

The tension lies in that balance. Value is often assumed. Viability is often negotiated. And both can be distorted by internal bias if left unchecked.

The Role of Corporate Diplomacy

This is where corporate diplomacy becomes an essential skill. Not politics for the sake of survival, but structured communication for clarity. It is about guiding conversations in a way that uncovers what truly matters:

What problem are we solving?
For whom?
Why now?

It is about keeping stakeholders engaged while gradually shifting the conversation from opinions to evidence.

You listen carefully.
You translate requests into hypotheses.
You validate them through data, user behavior, and experiments.

Data does not remove bias entirely, but it anchors decisions in something more stable than intuition alone.

You Don’t Need a Prism

We may never get that perfect Prism of Priorities. But perhaps we do not need one.

Because the real craft of product management is not about revealing a single “true color” of every request. It is about navigating the spectrum; understanding where each input fits, filtering what matters, and continuously aligning the product with the problems it is meant to solve.

Or as Steve Jobs famously said:

“Deciding what not to do is as important as deciding what to do.”

Final Thought

A Prism of Priorities would make things easier, but it would also remove the judgment, context, and nuance that define strong product thinking. The real value lies in how we interpret, challenge, and align; not just what we see.

Sitting in the space where clarity is incomplete requires a different kind of discipline; translating urgency into understanding, and opinions into direction. It comes down to choosing what truly moves the product forward, even when the signal is faint and the noise is loud.

If these ideas resonate, or if you see prioritization differently in your own work, there is always value in exchanging perspectives and learning from real experiences across teams and products.

Discussion of the week: The Trade-off Between Simplicity and Discoverability

Posted on May 11, 2026 - 1:19 pm by Tahir Shahzad

Last week, I was working on improving user experience for Usermaven. Usermaven helps you accurately track the full customer journey, from first touch to purchase and retention. By unifying product analytics, CRM data, and ad performance, it provides a single source of truth for growth teams.

On the surface, the goal was simple; make the interface cleaner, reduce clutter, and bring important actions closer to the user. But as we started making changes, an interesting tension emerged. Every improvement in clarity seemed to come with a trade-off in familiarity.

Things looked better. Flows felt more structured. But at the same time, elements users were used to interacting with became less visible. That raised some questions for me.

Are we truly improving the experience, or just shifting the complexity somewhere else? This is where UX stops being just design and starts becoming a product decision.

In many products, simplifying the interface often means hiding complexity. Fewer visible options, cleaner layouts, and more focused screens can make things feel better… at least initially.

But there is a trade-off.

When familiar elements move, or commonly used actions become less visible, users may not feel “simplified”; they may feel lost. What looks like better design for new users can create friction for existing ones.

So where is the balance?

Should discoverability be sacrificed for a cleaner experience?
How do you decide what stays visible vs what gets hidden behind interaction?
At what point does “good design” start working against user habits?

👉 Curious how you approach this tension between clarity and familiarity in your products.

Three Boys, a Barber, and the Biggest Lie About AI Adoption

Posted on May 8, 2026 - 4:14 pm by Tahir Shahzad

We were in a routine discussion when my boss asked a seemingly straightforward question:

How do we encourage cross-functional teams to adopt AI in their workflows; reduce bottlenecks; experiment faster; and improve productivity?

On the surface, this sounds like a tooling problem. Introduce AI tools, train teams, and expect outcomes. But the question took me somewhere else.

The Story I Shared

In a village, a new barber arrived. He was skilled, efficient, and consistent. Word spread quickly. His shop became busy; customers kept increasing day by day.

In the same village, three struggling boys noticed this. They did a rough calculation; number of customers multiplied by price per haircut. To them, it looked like easy money.

They approached the barber and asked:

“What tools do you use?”

He showed them a comb, scissors, and a machine. That was enough for them.

They pooled money, bought the same tools, and opened their own shop.

For a brief moment, things looked promising. Curious customers walked in. There was attention, even excitement. But the boys didn’t have the skill, the discipline, or the understanding of the craft. Haircuts were poorly done. Experiences were ruined. Within days, the village knew. No one returned.

The tools were right. The outcome was not.

The Parallel With AI Adoption

This is exactly what is happening with AI today. Teams see others using tools like ChatGPT, Claude or many others and assume:

“This is the formula.” So they invest on the tools:

Content teams generate copy
Developers use AI for code
Product teams use it for documentation

But without clarity and structure, the results are inconsistent or even damaging.

Just like the boys with the barber tools.

The First Batch Illusion

The boys did get customers initially because new things attract curiosity. Every product, feature, or workflow change gets a first batch:

Early adopters
Curious users
Internal champions

This phase often creates a false sense of success.

Read more about the six stages of product lifecycle

The real signal comes later:

Do users return?
Does quality improve?
Does trust increase?

This is where most teams fail.

Where Teams Actually Fail

From my experience across product and agile environments, failures around AI adoption don’t come from lack of tools. They come from:

No Skill Development: Teams use AI outputs without understanding context, accuracy, or limitations.
No Workflow Integration: AI is added as a layer, not embedded into decision-making or delivery systems.
No Validation Loop: Outputs are not tested with real users or real scenarios.
No Ownership: No one is responsible for quality when AI is involved.

The result is not acceleration; it is amplified inconsistency.

What the Barber Got Right

The barber’s success was not because of tools. It was because of:

Repeated practice, skill built over time
Understanding of customer expectations
Consistency in delivery
Clear ownership of outcomes

Tools were just enablers.

A Better Way to Introduce AI in Teams

Whether it is a barber shop in a village or a modern AI-powered product, the principle remains the same:

Initial attention comes from curiosity. Sustainable growth comes from capability.

If the goal is to reduce bottlenecks and increase productivity, the approach needs to shift:

1. Start with Problems, Not Tools

Identify where teams are actually stuck:

Slow documentation
Repetitive tasks
Decision delays

Then map AI use cases.

2. Build Skill, Not Just Access

Train teams on:

Prompting
Validation
Context awareness

3. Create Feedback Loops

Every AI-assisted output should be reviewed, tested, and improved.

4. Define Ownership

Someone must own the outcome, even if AI assisted in producing it.

Closing Thought

The gap between tool adoption and actual impact is now visible in real numbers.

According to McKinsey’s State of AI 2025 report, AI adoption has broadened significantly, with 88% of organizations reporting that they use AI in at least one business function. Yet only a small fraction report meaningful impact on the bottom line.
Through 2025/2026, roughly 80% of AI projects are expected to fail to deliver on their projected value. Gartner, RAND Corporation.
The 70% Rule: In successful AI implementations, only 10% of the value comes from the algorithm, 20% from technology/data, and 70% from redesigning how work gets done. Boston Consulting Group (BCG)

What this means in practical terms:

You may see a 20–40% speed improvement in isolated tasks using AI
But you may also introduce quality drops, rework, and decision noise if systems are weak
Over time, this can reduce user trust and retention, which directly impacts growth

Teams that pair AI with structured thinking, validation loops, and accountability can see significant gains in speed, cost efficiency, and experimentation.

Teams that don’t will simply move faster in the wrong direction. The tools are not the differentiator. The system behind them is.

If Your Absence Breaks Delivery, Your System is Fragile

Posted on May 5, 2026 - 8:15 pm by Tahir Shahzad

I hated it. I was in a difficult conversation with leadership, explaining why we couldn’t ship. The reason? One key person was absent. As a Technical Product Manager, I was new to the setup and hadn’t built this team myself, but I refuse to abandon ownership just because I’m the new person. I was learning from the dynamics of the team and organizational culture.

I realized our delivery was dependent, not designed. If your software delivery engine stops because someone took a sick day, you aren’t running an Agile team; you’re running a bottleneck.

Fixing the Delivery Engine Piece by Piece

I knew I couldn’t fix everything overnight, so I treated the system like a product and started iterating. I focused on shifting from individual dependency to cross-functional ownership.

Created Backups & Shared Context: We stopped letting critical workflows live only in people’s heads.
Rotated Responsibilities: I ensured knowledge wasn’t locked with one person by rotating tasks during Scrum sprints.
Introduced DevOps & Automation: We implemented CI/CD and uptime monitoring to remove manual deployment risks and “surprises”.
Distributed Ownership: I gave the team ownership of upcoming deliveries, empowering them to make strategic decisions rather than just executing tasks.

Within a year, the transformation was clear: the products I led no longer depended on a single point of failure.

Useful Findings for Product Leaders

Through this process of Digital Transformation, I discovered three hard truths about modern product delivery:

The “Bus Factor”:

The bus factor is a risk management metric representing the minimum number of team members who, if suddenly unavailable (e.g., hit by a bus), would cause a project to fail due to lack of critical knowledge. A low bus factor (e.g., 1) indicates high risk, while a higher number indicates a resilient team with shared knowledge.

Agile is About Resilience

Agile is often misunderstood as a set of ceremonies or frameworks like Scrum or Kanban.
In reality, Agile is a resilience system. It is the ability of a team to adapt when priorities shift, people change, or uncertainty increases. A truly Agile team does not depend on perfect plans; it is designed to absorb change and continue delivering value.

Documentation is Delivery

When knowledge lives only in people’s heads, it creates hidden dependencies. This leads to delays, confusion, and risk when key individuals are unavailable. This is a form of technical debt that is harder to detect than bad code. Documentation is not overhead.
It is what makes delivery repeatable and reliable.

The “Resilient Delivery” Framework

To reduce these issues in your own organization, I suggest moving away from “hero culture” and toward a system-based framework:

Pillar 1: Knowledge Liquidity: Use tools like Kanban to visualize not just tasks, but who knows how to do them. If only one name appears on a certain type of ticket, you have a knowledge silo.

Pillar 2: Automated Guardrails: Shift from manual processes to AI-driven automation and CI/CD. Let the machine handle the “how” so the humans can focus on the “why”.

Pillar 3: Strategic Redundancy: Startups don’t have abundance of resources. That requires adaptability essential. Cross-train your team, share context, and enable people to step into adjacent roles when needed.

Closing Thoughts

Look at your setup today: where does everything slow down when one person steps away?. If you are an aspiring Product Manager in or a founder navigating Digital Transformation, don’t wait for a crisis to fix your system. Build for resilience, not just for speed.

By prioritizing shared context and automated guardrails, you ensure that your “product” continues to deliver value even when life happens.

How are you currently handling single points of failure in your delivery process?

Discussion of the Week: Are dashboards becoming outdated now that AI can explain your product data for you?

Posted on May 4, 2026 - 12:40 pm by Tahir Shahzad

For years, product teams relied on dashboards, filters, and exports just to answer basic questions like:

What changed in user behavior this week?
Which feature actually drove retention?
Where are users dropping off?

But the workflow has always been heavy:
Dashboards → Export data → Sheets → Integration tools & Custom Formulas → Back to decisions.

Slow, fragmented, and heavily dependent on human effort to connect the dots.

Even with advanced analytics tools, the reality hasn’t changed much. Teams still spend more time finding insights than actually acting on them.

Now a different model is emerging.

With Agentic AI in product analytics, tools like Usermaven can directly interpret your product data and give contextual summaries without needing extra steps or integrations. Instead of navigating dashboards, you can simply ask and get insights in natural language.

Instead of building workflows around data, insights are becoming part of the workflow itself.

This shifts the conversation from:
“Where do I find the data?”
to
“What is the data trying to tell me?”

This changes the role of analytics from reporting → to reasoning.

A few questions worth discussing:

What tools do you use to understand user behavior?
Do you export data to LLMs or other tools for analysis?
Do you trust AI-generated insights for product decisions?
If your analytics tool gives you simple summaries automatically, would you still need other tools?

Previous Edition: Is Cross-Channel Tracking Dead or Just Evolving?

Invisible Transitions; The Real Work of an AI Product Manager

Posted on April 29, 2026 - 10:40 am by Tahir Shahzad

A Practical Checklist for Managing Product Transitions

There are moments in every product lifecycle when something underneath needs to change.

Sometimes it is small; upgrading a CRM API from V2 to V3 without touching endpoints or user experience.
Sometimes it is messy; a core library gets deprecated, and suddenly your pipelines, integrations, and assumptions need to be rebuilt.
Sometimes it is big; rebranding, domain changes, or platform migrations that can impact trust and discoverability.

Most users should never notice any of this. That is the job.

In today’s AI-driven ecosystem, where models, tools, and libraries evolve almost daily, these transitions are no longer occasional. They are continuous. AI Product Managers are not just building features; they are constantly managing change under the surface.

I have been through all of these transitions, and one principle stays consistent:

If users notice the transition, something was not handled well; unless you intentionally made it visible as an upgrade.

A Practical Checklist for Managing Product Transitions

With AI systems:

Dependencies change faster
Models become obsolete quickly
Vendor lock-in risks increase
Experimentation becomes continuous

This means transition management is no longer a side task. It is a core competency.

1. Transition Classification

Start by understanding what kind of change you are dealing with:

Silent Upgrade; API versioning, infra improvements
Structural Change; library replacement, pipeline redesign
Experience Impacting; UI changes, workflows
Market-Level Change; rebranding, domain shift

Each category demands a different level of visibility and risk control.

2. Impact Mapping

Before touching anything, map the blast radius:

Systems and dependencies affected
Data flow and integrity risks
User journeys that might break
External integrations and partners

This is where most hidden risks surface.

3. Risk Analysis

Break risks into clear buckets:

Technical Risk; failures, downtime, performance drops
Data Risk; loss, inconsistency, migration errors
User Risk; confusion, trust erosion
Business Risk; revenue disruption, SEO loss (in case of domain change)

For each risk, define:

Likelihood
Impact
Mitigation strategy

No transition should move forward without this clarity.

4. Stakeholder Buy-In

Transitions fail more due to misalignment than technology.

Internal stakeholders:

Engineering; feasibility and timelines
Design; experience consistency
Marketing; messaging if visible
Support; handling edge cases

External stakeholders:

Clients and enterprise users
Integration partners
Third-party vendors

Communicate early. Align expectations. Remove surprises.

5. Execution Strategy

Choose the right rollout model:

Phased rollout
Feature flags
Parallel systems (old + new)
Backward compatibility layers

Always have a rollback plan. If you cannot roll back, you are taking unnecessary risk.

6. Observability and Monitoring

Transitions do not end at deployment.

Track:

System performance
Error rates
User behavior changes
Drop-offs in key funnels

Set clear success metrics before release.

7. Communication Strategy

Decide what to hide and what to highlight:

Keep infrastructure changes invisible
Announce improvements that add user value
Frame transitions as benefits, not disruptions

Silence is a strategy; so is storytelling.

Final Thoughts

Every product evolves; APIs change, dependencies break, platforms shift. In the AI era, this pace is no longer manageable with reactive thinking.

Users do not care what you upgraded, replaced, or migrated. They care if something breaks, slows down, or feels different without reason.

That is the standard.

Strong Product Managers treat transitions as first-class work; not background tasks. They plan them, de-risk them, align people around them, and execute with precision. Because every unnoticed transition builds trust. And every visible failure breaks it.

In a world of constant change, stability becomes your real product.

Why Your Product Team Isn’t Shipping (Hint: It’s Not Capacity)

Posted on April 23, 2026 - 11:01 am by Tahir Shahzad

From 60 “must-haves” to actually shipping: a practical lesson in prioritization.

I’ve been in situations where stakeholders handed over a list of 60+ things they wanted to achieve—some of them sitting there for years. The frustration was always the same: “Why hasn’t anything been delivered?”

At the same time, the development team was constantly busy. Just not on these things.

That disconnect is more common than we admit.

“Everything is a priority” is the fastest way to achieve nothing

When everything matters, nothing moves

The real issue wasn’t capacity. It was clarity.

When every item is labeled as “important,” teams lose the ability to make meaningful progress. Work gets fragmented, priorities keep shifting, and outcomes stay stuck in limbo.

So instead of trying to push delivery harder, I stepped back and focused on prioritization.

Running a prioritization workshop

I set up a workshop with stakeholders to bring everything into one place and force a conversation around trade-offs.

We used the MoSCoW method:

Must have
Should have
Could have
Won’t have (for now)

It’s simple, intuitive, and works well when you need quick alignment without over complicating things.

The hardest part: “Won’t Do”

As expected, the biggest resistance came with the “Won’t Do” column.

“These are all important. We can’t skip any of them.”

Fair point—on the surface.

So I reframed it:

“This isn’t ‘won’t ever do.’ This is ‘won’t do in the next two quarters.’ If priorities change, we can revisit.”

That small shift made a big difference. It turned a hard rejection into a time-bound decision. Eventually, they agreed.

Breaking the list into reality

Once we started sorting, patterns emerged. Finally wt had two clear groups each further sorted with MoSCoW:

Small improvements: quick wins, incremental value
Big initiatives: larger efforts that needed new thinking or full transformation (epics)

This separation helped a lot. It gave us a practical way to balance short-term delivery with long-term impact.

Forcing focus at the sprint level

For the upcoming sprints, I asked stakeholders to commit to:

1 big initiative
2 small improvements

Alongside that, the team continued working on ongoing initiatives and technical debt.

This wasn’t about limiting ambition. It was about creating focus.

What changed

We didn’t magically deliver all 60 items. But we started shipping.

Small improvements went out faster. Bigger initiatives got proper attention instead of being half-started and abandoned. From the top 10 priorities, we were able to deliver meaningful value within a couple of quarters.

That shift—from activity to outcomes—made all the difference.

Final Thoughts

Prioritization isn’t just stacking features by deadline. It’s an exercise in making trade-offs—often uncomfortable ones.

If you can’t clearly explain the cost of delay for a feature, you’re not really prioritizing. You’re reacting to whoever is the loudest in the room.

Product management requires the discipline—and sometimes the courage—to say “no” to good ideas, so you can say “yes” to the ones that truly move the needle.

How do you handle pushback when you have to deprioritize a stakeholder’s “must-have”?

The T-Shaped Technical Product Manager

Posted on April 10, 2026 - 3:52 pm by Tahir Shahzad

It has been a long journey in the IT industry—moving from development into leadership roles. One thing remained constant: never standing still. Continuous learning, experimenting, and staying aligned with evolving technology shaped the way I approach products and teams today.

This journey led me to be recognized as a Technical Product Manager, sometimes referred to as a T-Shaped Product Manager. That said, this title does not imply knowing everything. It reflects a balance—depth in one area, with awareness across many.

What T-Shaped Really Means

A T-shaped profile combines broad understanding across disciplines (the horizontal bar) with deep expertise in one area (the vertical bar).

For a Technical Product Manager:

The depth lies in technology
The breadth spans business, user experience, delivery, and stakeholder alignment

This balance enables better decision-making, clearer communication, and more realistic product outcomes.

The Reality of Going Beyond Roles

Throughout my career, I have often gone beyond what was expected. Not to take over someone else’s role, but to ensure the product succeeds.

At the same time, I have been careful not to step into ownership that belongs to others.

This is where things get complicated.

Even when roles are clearly defined, boundaries are not always obvious in practice. Questions naturally arise:

Where does a Technical Product Manager stop and a Solution Architect begin?
How does a Product Manager differ from a Marketing or Growth Manager?

These overlaps are not just theoretical—they create real tension within teams.

When Ambiguity Turns Into Conflict

Role ambiguity often leads to friction in cross-functional teams.

Not because people are wrong—but because:

It challenges ownership
It disrupts comfort zones
It creates a sense of being questioned

In some cases, this tension becomes visible through reactions like:

“Do you want to join the development team?”

Statements like this are rarely about the actual discussion. They reflect discomfort when boundaries feel unclear.

If not handled carefully, such situations can lead to unhealthy team dynamics—where instead of focusing on building great products, individuals begin protecting their space.

And a team with internal conflict will rarely deliver its best work.

Defining the Roles (From Experience)

Product Manager

Owns the vision, problem space, and outcomes
Focuses on users, business goals, and prioritization

Technical Product Manager

Bridges business and technology
Understands systems, constraints, and trade-offs
Challenges decisions constructively while staying outcome-focused

Development Lead / Solution Architect

Owns the technical design and implementation approach
Ensures scalability, performance, and maintainability

How I Approach the T-Shaped Role

Being a T-shaped Technical Product Manager is not about control—it is about clarity and alignment.

From my experience, a few principles help maintain that balance:

1. Go Deep, But Not Too Far

Understanding technical details is important. Owning them is not.

The goal is to:

Ask better questions
Understand trade-offs
Avoid unrealistic expectations

Not to replace engineering decisions.

2. Stay Outcome-Focused

Discussions should always connect back to:

User impact
Business value
Long-term product direction

This keeps conversations grounded and reduces personal friction.

3. Respect Ownership

Every role exists for a reason.

Crossing boundaries occasionally is natural. Staying there is where problems begin.

4. Handle Challenges Carefully

Challenging ideas is necessary. Challenging people is not.

The difference lies in:

How questions are framed
The intent behind them
The respect shown in conversations

Where to Draw the Line

From experience, the line becomes clearer with intent.

Step in when:

Product outcomes are at risk
Trade-offs are unclear
Technical decisions impact user experience

Step back when:

The discussion is purely implementation-focused
The team is aligned on direction
Input starts becoming prescriptive

Final Thought

The T-shaped Technical Product Manager operates in a space that naturally overlaps with others. That overlap is not a problem—it is a necessity in modern product teams.

The real challenge is managing it with awareness.

It is not about knowing everything.
It is not about controlling decisions.

It is about connecting perspectives without creating conflict.

And when done right, it turns a group of individuals into a team that builds with clarity, not competition.

The Tech Alchemy of Product Management

Posted on September 18, 2024 - 6:06 pm by Tahir Shahzad

In today’s fast-paced world of business and technology, it’s easy to fall into the trap of believing that certifications alone define your capabilities as a project or product manager. Over the years, I’ve completed certifications like the Google Project Management via Coursera and earned my CSPO (Certified Scrum Product Owner) via Scrum Alliance. I’ve also taken few micro-certifications. While these courses taught me valuable knowledge about tools, frameworks, and methodologies, the truth is, project and product management goes far beyond certifications.

These programs helped me understand the core elements and gave me exposure to important practices in the industry. However, I truly mastered these skills only by getting my hands dirty, leading teams, and solving real-world challenges. I’ve realized that project and product management can’t be entirely taught in a classroom—it’s about leadership, handling tough situations, and making decisions when everything’s on the line.

Leadership Over Certifications

Being a project or product manager is more about leadership than technical expertise. Leadership is a skill that isn’t granted by a certificate; it’s developed through experience. Some people excel in technical skills but struggle with leadership, while others naturally have the ability to inspire, organize, and lead without formal training.

Simon Sinek, a leadership expert, illustrates this well with his example from the Marine Corps. He explains that potential officers undergo six weeks of intense training where they can quit at any time. This training is designed to weed out those who don’t want to be leaders. As Sinek puts it, “The first criterion to being a leader is you have to want to be one.” Leadership is tough—it can be thankless, lonely, and incredibly challenging. But in project and product management, it’s what makes the difference between success and failure.

One thing I’ve learned from my experiences is that no certification can prepare you for the real-world challenges you face in the field. For instance, I once led a project demonstration where everything seemed perfect—until the solution failed due to an unexpected angle of sunlight affecting the camera. This was something we hadn’t encountered before, and no textbook or course could have prepared me for the heat of that moment, both literally and figuratively. I had to accept the failure gracefully and quickly think on my feet, acknowledging the problem while managing the client’s expectations, all while battling my own internal guilt.

Balancing Personal and Professional Commitments

Being a project manager often requires balancing personal and professional responsibilities—another aspect no certification can fully prepare you for. I remember a time when my employer used me as an example of dedication to others because I traveled across the country for a work commitment the day after my wedding. This might seem extreme to many, but I had made a commitment well before my marriage date was set. It was my responsibility to manage both my personal and professional commitments, and I did so by discussing it with my family and gaining their support rather than walking away from the commitment.

Leadership in project and product management often means making these tough calls—balancing your time, keeping your promises, and staying accountable, no matter how challenging the circumstances.

Practical Experience vs. Theoretical Knowledge

It’s easy to say things like, “know your customer” or “interview your users,” but putting these principles into practice is much more difficult. During one project, I found myself standing next to an attendance device at a hospital, observing how random person interact with the device and how staff used it to clock in and out. On the surface, this might sound simple, but the discomfort of standing there as a stranger, taking mental notes on behavior, is not something a certification teaches you. It’s the hands-on learning and the real-time observations that help you understand your customer in ways a classroom cannot. A funny aspect, a guest used this device to many times on different days to comb hair.

Another time, I launched a solution for a client whose business handled thousands of dollars in weekly transactions. The pressure of ensuring nothing went wrong was immense. I couldn’t sleep or eat, not because it was part of my job description, but because I knew the stakes were incredibly high. I stayed awake, monitoring every detail, even though I wasn’t being paid for those extra hours. Certifications can teach you how to plan for a launch, but only experience can prepare you for the reality of it.

Leadership in High-Stakes Situations

Leadership is about how you handle pressure, especially when things go wrong. Simon Sinek often speaks about leadership as the responsibility to help those around you rise, even when it’s hard. Leadership isn’t about delegating tasks or following a checklist—it’s about stepping up when the stakes are high and taking responsibility for the outcome.

No certification prepared me for the time when I was in front of a room of clients, sweating both from the physical heat and the internal pressure, when a solution failed due to a reason no one could have foreseen. Leadership, not certification, is what allowed me to remain calm, accept the failure, and offer a plan for improvement.

Handling Unique Challenges

Every project brings its own set of unique challenges, and learning by doing is the only way to normalize those experiences. Certifications are useful for providing a foundation, but it’s the real-world situations that truly shape you as a manager. Whether it’s troubleshooting technical failures on the fly, balancing personal and professional commitments, or standing by a solution you launched while facing the risks, the true essence of project and product management lies in how you navigate uncertainty and lead in moments of crisis.

Conclusion: Experience Over Certification

In the end, while certifications like the Google Project Management Course or CSPO have given me valuable tools, the reality is that project and product management is about leadership, not certificates. It’s about having the ability to make tough decisions, take responsibility, and guide a team through unexpected challenges. Certifications provide a strong foundation, but leadership is something you develop through practice, persistence, and navigating real-world challenges. Leadership is a skill anyone can learn, but it’s only mastered through experience. As a project or product manager, your real growth happens when you’re in the field, handling situations no certification could have ever predicted.