👋 Hey friends, Happy Monday!!

Let’s start with something that sounds counterintuitive:

AI has made building easier than ever.

Which means it has made winning harder than ever.

In 2026, you can:

Speed is no longer rare.

Execution is no longer scarce.

AI has flattened the technical playing field.

So if everyone can build faster, why are so few AI startups becoming enduring companies?

Because the bottleneck isn’t capability anymore.

It’s placement.

The next generation of breakout companies won’t start as “AI platforms.” They won’t begin with grand visions of owning entire categories.

They will begin as wedges.

Small. Focused. Embedded.

This edition is for founders who:

If that’s you, this framework will clarify what to build next — and what to ignore.

Today, we’re breaking down:

If you’re building in AI right now, this framework could save you years.

— Naseema Perveen

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Let’s ground this in numbers.

McKinsey’s enterprise AI research suggests that AI tools integrated directly into existing workflows are adopted far faster than standalone tools requiring new behaviors.

MIT research on digital platforms indicates that companies expanding from core workflow nodes into adjacent capabilities grow more sustainably than those attempting broad diversification.

The pattern is consistent:

Focus compounds.
Sprawl fractures.

Three years ago, adding AI to your product was a differentiator.

Today, it’s expected.

Open APIs.
Foundation models.
Agent frameworks.
Hosted inference.
Embeddings everywhere.

According to McKinsey’s 2025 State of AI reports, over half of organizations now report regular use of AI in at least one function — nearly double from just two years prior.

Gartner projects that by 2026, over 80% of enterprises will have generative AI APIs integrated into some workflow.

Which means:

You are not competing on “AI-powered.”

You are competing on:

The technology is accessible.

Distribution and workflow control are not.

AI has dramatically lowered production costs.

But it has increased strategic noise.

When you can generate features in hours, the temptation is expansion:

Add another tool.
Add another dashboard.
Add another AI layer.

But here’s what happens when you expand too early:

CB Insights consistently ranks lack of product-market fit as the number one reason startups fail.

Not lack of features.

Not lack of technology.

Misalignment.

And in AI markets, misalignment happens faster because the speed of building outpaces the speed of validation.

You can ship features faster than users can internalize value.

Which is why the smartest founders right now are doing something radical:

They are building less.

And embedding deeper.

A workflow wedge is:

A narrow automation that inserts itself into a high-frequency workflow and removes friction better than anyone else.

It is not a platform.

It is not a category.

It is not a big vision slide.

It is one specific moment inside a larger system.

To qualify as a true wedge, it must satisfy four conditions:

Let’s unpack them.

The workflow must happen daily or weekly.

Monthly tasks do not build habits.

Harvard research on habit formation shows that repeated daily interactions significantly increase stickiness and retention.

If your product isn’t used often, it won’t embed.

Example:

Sales call notes? Daily.
Invoice processing? Daily.
Social media repurposing? Daily.
Quarterly strategy docs? Not a wedge.

The task must be annoying.

Repetitive.
Manual.
Cognitively draining.
Error-prone.
Context-switch heavy.

The pain does not need to be dramatic.

It needs to be persistent.

Example friction points:

These are not glamorous.

They are leverage nodes.

Your wedge must sit at a junction point.

If it sits on the periphery, it won’t expand.

But if it sits where data flows in and out, it becomes essential.

MIT Sloan research on digital platforms shows that companies that anchor themselves in core workflow nodes are more likely to expand sustainably into adjacent products.

Translation:

Solve the center, not the surface.

A wedge works because it creates pull.

Users begin asking:

“Can you also…”

If you automate meeting notes, users will ask for CRM syncing.

If you automate blog repurposing, users will ask for analytics.

If you automate invoice extraction, users will ask for reconciliation.

Expansion must follow behavior.

Not imagination.

Stripe did not begin as “financial infrastructure.”

It began as a payments wedge.

At the time, online payments were:

Stripe inserted itself into a high-frequency workflow:

Accepting payments.

High frequency.
High friction.
Workflow centrality.
Massive expansion gravity.

Once embedded, Stripe expanded:

They didn’t diversify randomly.

They expanded along the flow of money.

The wedge became the platform.

Figma did not start as a “design ecosystem.”

It solved one wedge:

Real-time collaborative UI design.

High frequency.
High centrality.
High expansion gravity.

Once embedded, expansion flowed naturally:

Figma controlled the workflow node.

Everything expanded from there.

Notion began as a flexible document workspace.

When it added AI, it didn’t launch a standalone “AI platform.”

It embedded AI into the core document workflow.

Users were already:

AI became an enhancement inside the existing wedge.

That’s the key difference.

Notion did not force a new behavior.

It amplified an existing one.

Let’s break this down into a clear growth model.

In this stage, discipline matters more than creativity.

Your entire company focuses on:

One task.
One user.
One friction point.

You ignore adjacent ideas.

You decline distractions.

You refine until your solution is dramatically better than manual.

Questions to pressure-test:

Would users notice immediately if this disappeared?
Does it save measurable time?
Does it reduce cognitive load?
Does it sit inside daily work?

This phase builds embedding.

Once embedded, you gain visibility.

You now see:

Upstream friction.
Downstream friction.
Data intersections.

Expansion flows in three directions:

Upstream — What happens before?
Downstream — What happens after?
Adjacent — What else can this data unlock?

But expansion must be reactive.

Driven by user pull.

Not founder ambition.

Bain & Company research shows that companies expanding into adjacent workflows outperform those diversifying across unrelated categories.

You do not declare yourself a platform.

You become one.

By now you have:

Behavioral data.
Trust.
Integration points.
Switching costs.

Your moat shifts from feature to position.

And position is far harder to replicate.

Where do founders go wrong when trying to scale a successful AI workflow into a broader platform?

We’d love to hear your perspective.

Email your thoughts to: [email protected]
Selected responses will be featured in next week’s edition.

Use this exercise before building anything.

What specific daily workflow are you targeting?

Be precise.

Not “marketing.”
Not “finance.”

Example:
“Summarizing and distributing sales call notes.”

Frequency:
How often does this occur?

Friction:
How annoying or costly is it?

Centrality:
Does this sit at a key decision point?

Expansion Gravity:
Does solving this naturally reveal adjacent problems?

If any score is below 4, reconsider.

Draw three columns:

Before the workflow
The workflow itself
After the workflow

Where could automation expand later?

If you can’t see expansion paths, your wedge may be too narrow.

Ask:

If we disappeared tomorrow, what would users revert to?

If the answer is “Google Docs and email,” your wedge may not be strong enough.

Ask five real users:

What part of this workflow feels unnecessary?
What part feels slow?
What part feels repetitive?
What part causes mistakes?
What part causes stress?

Patterns here define your wedge strength.

AI has made building easier.

Distribution is still hard.
Retention is still harder.
Expansion is hardest of all.

The winners in 2026 won’t be the teams that build the most features.

They’ll be the ones who own one workflow so deeply that users can’t imagine working without them.

That starting point is called a workflow wedge.

And wedges compound.

A workflow wedge is not:

A wedge is:

It is small.

It is specific.

It is deeply embedded.

And it earns the right to expand.

A few years ago, novelty won.

If you could say “AI-powered” first, you got attention.

Now?

Everyone has access to:

The moat is no longer model access.

It’s workflow ownership.

AI has shifted the edge from:

Innovation → Integration
Feature → Flow
Velocity → Leverage

The question is no longer:

“What cool thing can we build?”

It’s:

“Which workflow can we own?”

A strong wedge sits at the intersection of five forces:

Does it happen daily or weekly?

Does it cause real cognitive or emotional pain?

Does it sit in the middle of other decisions?

Does solving it give you proprietary insight?

Does it naturally unlock adjacent problems?

If you can’t say yes to at least four of these, it’s not a wedge.

It’s a feature.

Let’s walk through a concrete example.

A startup begins by solving one narrow problem:

“Automatically draft outbound sales emails from CRM notes.”

Why this works:

They don’t launch “AI for sales.”

They launch “AI for writing outbound follow-ups.”

Narrow.

Focused.

Deep.

After months of usage, they now have:

Now they expand into:

The wedge becomes a system.

The system becomes a platform.

Now contrast this with a startup that launches:

“AI Sales Assistant — Everything in One Dashboard.”

It includes:

It sounds impressive.

But users try it and think:

“Why would I switch for this?”

There is no embedded wedge.

No one workflow is owned.

The product becomes:

Wide, shallow, forgettable.

They built too much before earning depth.

That’s diffusion.

Diffusion kills defensibility.

Here’s the part most founders underestimate.

Wedges create habits.

Habits create switching costs.

Switching costs create pricing power.

When your product sits inside a daily workflow:

Leaving means friction.

That’s retention leverage.

Retention doesn’t come from feature breadth.

It comes from workflow depth.

If you’re exploring ideas, answer this:

What is one workflow where:

Now stress test it:

If the answer is unclear, it’s not ready.

Here’s a realistic validation rhythm.

Week 1
Interview 10 users focused only on one workflow.

Ask:

Week 2
Build a narrow prototype that solves 60% of the pain.

Not everything.

Just the most repetitive slice.

Week 3
Observe usage frequency.

Key metrics:

Week 4
Ask one question:

“If this disappeared tomorrow, what would you use instead?”

If the answer is:
“I’d be annoyed.”

You’re close.

If the answer is:
“I’d just go back to my old workflow.”

You haven’t embedded deeply enough.

Every founder now has the same building power.

The real edge is judgment velocity.

Not speed of code.

Speed of clarity.

Try this daily rhythm:

Morning:
“What changed in user behavior yesterday?”

Midday:
“Which assumption are we treating as fact?”

Evening:
“What did we learn that should reshape tomorrow?”

Save those reflections.

After 30 days, summarize recurring blind spots.

That document becomes your strategic mirror.

That’s institutionalized judgment.

Automation can remove friction.

It cannot remove empathy.

The best wedges are not identified by dashboards.

They are identified by:

AI helps you scale pattern recognition.

It does not replace human sensitivity.

Automation without empathy is noise.

Empathy without automation is exhaustion.

The edge is co-building.

Because AI has equalized execution.

Everyone can build fast.

Few can orchestrate well.

The next era of product growth is not about innovation velocity.

It is about workflow orchestration.

When models improve everywhere, performance converges.

What remains?

Judgment.

Which workflow matters?
Which wedge embeds?
Which expansion path aligns?

AI can generate options.

Only founders can choose trade-offs.

AI has made it cheap to build.

It has not made it easier to choose.

The startups that win this decade won’t be the ones shipping the most features. They’ll be the ones solving one workflow so well that expansion becomes inevitable.

A workflow wedge isn’t small thinking. It’s disciplined thinking. You earn the right to grow by becoming indispensable somewhere first.

Focus creates signal.
Signal creates adoption.
Adoption creates leverage.

When you build deeply instead of broadly, you don’t just add features. You create gravity.

And gravity is what turns a tiny automation into a platform.

— Naseema

Writer & Editor, AIJ Newsletter

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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