Hey friends👋

Over the past few months, I’ve noticed a quiet pattern emerging in conversations with early-stage AI founders.

Many start with a long list of ideas — market gaps, industry opportunities, interesting LLM use cases, clever prompts, and ambitious product visions.

But when it’s time to choose what to build first, almost everyone feels stuck.

What comes up again and again is this:

The products that take off rarely begin as full products.

They begin as one very specific workflow someone struggles with every day.

I didn’t fully understand this until a moment that stayed with me.

A founder was demoing a legal AI tool. Clean UI. Several polished features.
But when he walked me through a real contract review, he didn’t use any of them.

Instead, he kept returning to one specific paragraph — comparing, rewriting, checking, and rechecking.

That small loop — the one never shown in pitch decks — was where the real pain lived.

It was also where the real opportunity was hiding.

And that’s what today’s edition is about: Why starting with one workflow is often the most reliable, scalable, and grounded way to build an AI product — and the deeper psychology behind why it works.

What we’ll cover today:

Let’s dive in.

— Naseema Perveen

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It’s tempting to begin with a big idea —
“AI for finance,”
“AI for HR,”
“AI for logistics.”

But those categories hide the daily reality of how work actually happens.

Work happens in loops, not categories.

Inside every workflow, people are:

A workflow isn’t just a task.
It’s a container for dozens of micro-decisions.

And that’s where AI creates the most meaningful lift — not by automating work, but by supporting the thinking inside the work. According to OpenAI, that’s how early adopters identify how to leverage their AI efforts.

This is a subtle but important distinction.

AI doesn’t reduce tasks. It reduces uncertainty.

And uncertainty lives inside workflows, not ideas.

Even the data reinforces this pattern. BCG found that companies leading in AI over the past three years achieved 1.5x faster revenue growth, 1.6x higher shareholder returns, and 1.4x better return on capital than their less advanced peers. But when you look closer, maturity isn’t about having more AI systems — it’s about integrating AI into the right places. 

McKinsey reports that while 92% of organizations plan to increase AI investment, only 1% believe their efforts have reached true maturity. And across more than 300 implementations, 4,000 adoption surveys, and 2 million business users, one insight keeps showing up: the companies that see real returns are the ones that don’t start with big platforms or sweeping automations. They start with one high-value workflow, embed AI where real friction lives, and let value compound from there.

According to a Standford findings, people rarely ask for “automation.”

What they describe instead is a desire for:

These are cognitive needs, not operational ones.

A workflow often involves:

AI becomes most valuable when it lightens that cognitive load.

This is why workflows matter so much: they expose the parts of work where people feel mentally stretched.

Traditional product frameworks start with:

Useful questions, but incomplete.

A persona won’t tell you:

These “moments” inside workflows are where real products originate.

It's rarely the role.
It's rarely the industry.

It’s a single moment where someone feels stuck.

Workflows reveal these moments with surprising clarity.

Before I understood this pattern, I focused on:

But when I began sitting with people as they worked —
just observing, not interviewing —
I noticed something consistent:

Every workflow has a “pause point.”
A moment where the person slows down, doubles back, or rechecks something.

These moments weren’t dramatic.
But they were reliable.

And those pauses — not the tasks themselves — turned out to be the clearest signal of where AI could help.

This was the shift:

Real opportunities don’t show up in discussions. They show up in hesitation.

To ground this, here’s a real walkthrough.

Junior legal associate

Reviewing incoming vendor agreements

Step 3–4.

These involve judgment under uncertainty.
This is where people hesitate.
This is where errors creep in.
This is where anxiety peaks.

An AI tool can:

All of these reduce anxiety more than time.

Once step 3–4 are solved, users naturally ask for:

The roadmap emerges from real work, not hypotheticals.

Here’s a calm, clear way to think about this:

The Pause → The Workflow → The Insight → The First Feature → The Pull-Based Roadmap

This model keeps you grounded and aligned with real behavior.

A practical, step-by-step process you can follow.

This is a clear, simple, and repeatable guide for identifying a strong workflow, validating it, building a focused v1, and expanding only when users pull you forward.

Each step is intentional, calm, and grounded in how real teams actually work.

Pick a single person who performs the workflow you’re exploring.
Ask them to share their screen or walk you through how they normally work.

What you’re looking for is not what they say — but what they do.

Focus on noticing:

These small signals are often more reliable than anything captured in interviews.

Write down each step in simple, clear language.

For example:

You’re not analyzing yet — just capturing what you see.

This workflow map becomes your truth foundation.

Inside every workflow, there are usually 2–3 steps where:

These are your high-value intervention points.

To uncover them, gently ask:

Moments > persona.
These moments reveal where AI brings meaningful lift.

This is the step where the user is not just doing work —
they’re thinking heavily.

Examples:

This is the real opportunity.

AI works exceptionally well here because cognitive load is both:

Make a note:

“This is the step that feels heavy.”

This becomes your v1.

“Does this workflow occur at least weekly?”**

High-frequency + high-friction = strong starting point.

If the workflow is:

…it won’t create enough pull for an early-stage AI product.

But if it’s weekly or daily,
you’re looking at a repeatable, dependable source of value.

Before you write a line of code:

Create a simple visual mock that shows:

Your goal is clarity:

“This helps you with this one step in your workflow.”

Not a platform.
Not a suite.
A shortcut.

Show the user what will happen — not what the tool could someday become.

Not surveys.
Not theoretical conversations.

Actual demos.

Here’s what you’re listening for:

If you hear “interesting,” it’s a no.

You want emotional resonance.
Not politeness.

Choose ONE clear outcome:

A v1 should do ONE of these extremely well.

If you build more than one, the product becomes confusing to users, and the roadmap becomes noise.

Focus brings clarity.
Clarity brings traction.

Once you have early testers, watch:

AI usage patterns reveal everything:

If a user doesn’t use it twice, something is missing.

Usage > feedback.

After using your v1, users will naturally ask for more:

These requests are your roadmap.

Not your ambition.
Not your intuition.
Not your brainstorming notes.

Expansion should be a response, not an idea.

This reduces building risk by 80%.

After a few weeks, patterns emerge.

You’ll see pull in one of three directions:

(more accuracy, more reasoning)

(tasks that naturally follow or precede the v1)

(new teams who share the same pain point)

Choose the one with the strongest signal —
not the one that sounds most exciting.

As users ask for more:

It’s tempting to become a platform.

But platforms are a result of pull-based expansion,
not a starting point.

Make sure every new feature stays aligned with:

This protects you from building a generic, undifferentiated AI tool.

Workflows keep you anchored in reality.

The One-Workflow Strategy isn’t just a framework for focusing your product.
It’s a way of paying closer attention to how people actually work — not in theory, not in process maps, but in the small, quiet moments when real work is done.

Because every workflow, no matter how technical or routine, has a very human core.

Behind every repeated task is a person who is:

These aren’t things you see in dashboards.
They’re not visible in documentation.
They don’t show up in the “official” version of how work happens.

But they are always present — in the pauses, the checking, the small hesitations, the mental load people carry quietly.

When you focus on one workflow, you don’t just remove steps.
You relieve a bit of that invisible burden.
You help someone move through their day with a little more clarity, a little less pressure, and a little more trust in their own judgment.

And that’s what makes an AI product stick.

Not because it is powerful.
Not because it's automated.
But because it quietly supports a human being at the exact moment they feel the most uncertain.

Great AI products don’t transform industries on day one.
They transform moments — and those moments transform how people feel about their work.

If you can make even one of those moments easier,
users won’t just adopt your product.
They’ll rely on it.
They’ll return to it.
They’ll pull you forward into what they need next.

And that’s how something small becomes something meaningful.

Start with one workflow —
and you’ll end up understanding far more than the workflow itself.

See you next time, 

— Naseema 

That’s all for now. And, thanks for staying with us. If you have specific feedback, please let us know by leaving a comment or emailing us. We are here to serve you!

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