Hi friends 👋

A few weeks ago, I was speaking with a founder who had recently shifted his product from a general-purpose AI copilot to a very narrow workflow in healthcare. I asked him what drove the decision.

He paused for a second and said something that stuck with me:

“Horizontal AI is exciting — but vertical AI actually gets used.”

Not “makes more money.”
Not “sounds better in a pitch deck.”
Just gets used.

And the more conversations I had with founders, operators, and investors, the clearer this pattern became:

The biggest wave of real, sustained AI adoption is happening inside industries — not on the surface.

In this week’s deep dive, we’ll explore why vertical AI is gaining momentum, which sectors are shifting the fastest, and how founders can identify the highest-leverage opportunities.

Here’s what you’ll find inside:

Let’s get into it.

— Naseema Perveen

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Most people think of AI in terms of large, general-purpose models like ChatGPT, Claude, and Gemini. These are “horizontal” tools — broad, flexible, and capable of doing a long list of tasks reasonably well.

Vertical AI is something different:

Vertical AI = AI designed for one domain, one workflow, and one set of constraints.

According to IBM, 

A few examples:

Where horizontal AI aims to be the “general assistant,” vertical AI becomes the specialist.

If you think about how work actually happens, this makes sense. Real industries operate with:

General AI can help with early drafts and exploration, but it rarely fits neatly into these constraints.

Vertical AI does.

Many companies are still relying on broad, general-purpose AI tools, and a pattern is starting to appear: these systems often don’t deliver the depth teams expect once they’re integrated into real workflows.

Part of the challenge is that AI evolves quickly — and traditional generative models struggle with the kinds of complex, highly structured tasks that industries like healthcare, finance, and manufacturing deal with every day.

This is where vertical AI agents are starting to make a difference. Instead of trying to do everything, they focus on doing one thing extremely well. They’re built with domain expertise, trained on industry-specific data, and designed to support the exact workflows teams use.

And the market is already moving in this direction. Recent estimates suggest the vertical AI category will grow from $12.9B in 2024 to over $115B by 2034. Around 30% of companies have already adopted vertical AI agents, and another 35% are currently testing them.

The takeaway: as organizations look for more reliable, targeted impact from AI, specialization is becoming the more practical path forward.

Across every conversation I had for this piece — founders, operators, and investors — the same four reasons came up repeatedly. These are worth knowing, whether you're building, buying, or evaluating AI solutions.

Source

When you solve a specific workflow, customers don’t need persuasion.
They already feel the pain.

Healthcare systems, legal firms, banks, and manufacturers quickly fund solutions that remove:

In high-stakes environments, saving hours = real money.

Vertical AI can be trained to meet industry standards, safety protocols, and audit requirements.
Horizontal AI can’t.

Every workflow generates more domain data → better fine-tuning → tighter fit → harder to replicate.

This creates what investors increasingly describe as “compound specificity.”

As I spoke with founders across industries, a consistent pattern emerged. The most promising vertical AI opportunities don’t always look exciting on the surface. In fact, they’re often buried inside the unglamorous parts of a workflow — the places people avoid, postpone, or quietly endure. But once you learn to spot them, they become incredibly obvious.

Across almost every interview and memo, four characteristics stood out.

These are the tasks teams dread: filling forms, reviewing documents, handling compliance checks, writing summaries, routing cases from one team to another. They’re repetitive, rule-driven, and no one feels particularly proud of doing them. That makes them perfect candidates for automation — not because they’re easy, but because people want them gone.

This is where vertical AI becomes valuable. In healthcare, a small mistake can impact patient safety. In finance, an oversight can trigger regulatory action. In manufacturing, an error can halt production. When the cost of getting something wrong is high, the value of getting it right — consistently — becomes enormous.

Third, the environment needs to be data-rich.

Vertical AI thrives where there’s structure. Think of clinical notes, lab results, claim files, sensor logs, audit trails, or multi-year document histories. These datasets give models the context, patterns, and edge cases needed to perform reliably. Without them, accuracy plateaus quickly.

And finally, there has to be an expertise bottleneck.

Every industry has choke points — places where there simply aren’t enough qualified people to handle the volume. These shortages create backlogs, burnout, and delays. They also create an ideal opening for AI to act as a force multiplier rather than a replacement.

When all four of these conditions show up in the same workflow, you have something special. That workflow becomes a strong — and often overlooked — candidate for vertical AI. And more importantly, it becomes the kind of wedge that can unlock a much larger product surface as the solution matures.

One consistent theme across successful vertical AI companies is how they enter a market.

They don’t build “healthcare AI.”
They don’t build “legal AI.”
They don’t build “finance AI.”

They pick a wedge.

Here’s the simplified version that came up repeatedly:

Not “legal operations.”
Instead: “M&A contract clause extraction.”

Not “healthcare automation.”
Instead: “Cardiology prior authorization support.”

Customers adopt quickly when the improvement is clear:

Once embedded, expansion becomes natural:

What begins as a workflow tool slowly becomes the system of record.

This “wedge → expand → platform” pattern is the most reliable path founders shared for this category.

If you’re thinking about building in this space, here’s a practical framework that emerged from conversations with founders and operators.

Healthcare
Finance
Manufacturing
Legal
Logistics

These sectors have measurable ROI from automation and accuracy.

Look for repetitive tasks that:

Ask potential users:

Your product should complete one workflow extremely well.

If someone says “can it do X too?”
That’s usually a sign the wedge is working.

Watch them use it.
See where friction appears.
Fix the rough edges.
Re-test.

This hands-on validation is where every strong case study started.

Once you own a wedge, expand naturally:

The goal isn’t to stay narrow.
It’s to grow from a focused workflow into a category-defining platform.

Below is a simplified map of where vertical AI is accelerating the fastest. These aren’t predictions — these are areas where adoption is already happening.

If you had to pick one industry poised for the largest AI impact over the next 10 years, healthcare would be the clear answer.

Legal work is uniquely structured, making it ideal for vertical AI. The category is already maturing.

One of the most data-rich, compliance-heavy industries — perfect for vertical intelligence.

EvenUp became a unicorn by automating a narrow, legally complex finance workflow (injury claims).

While slower than other sectors, signs are strong that education will undergo a multi-year AI shift.

This is the quiet giant — massive value, less noise.

One pattern became clear as I researched this edition:

Vertical AI is not a trend — it’s a shift in how intelligence enters industries.

The last decade of software automated tasks.
This decade, AI is beginning to automate judgment.

But judgment isn’t generic.
It lives inside workflows, constraints, and domain logic.

That’s why vertical AI matters.

It matches how real organizations operate.
It meets teams where the pain actually is.
And it compounds as the system learns a domain more deeply.

If you’re a founder, operator, or simply someone following the space, here’s the takeaway:

Horizontal AI gets attention.
Vertical AI gets adoption.

And over the next few years, adoption will matter far more than attention.

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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