👋 Hey friends,

Every so often, a new wave of technology doesn’t just give us new tools — it changes what we even consider a product.

That’s exactly what’s happening with AI right now.

We’re watching a quiet shift unfold:
the smartest founders aren’t asking, “What app should we build?”
They’re asking, “What workflow do we already run that AI could do for us?”

In this week’s edition, we unpack how AI is turning processes into scalable products — and why that shift might be the biggest opportunity of the decade.

🌐 In today’s edition, we’ll explore:

Let’s dive in — because the next unicorn might not come from a new idea, but from a workflow you already run every week.

— Naseema Perveen

Over the next year, Roku predicts that 100% of the streaming audience will see ads. For growth marketers in 2026, CTV will remain an important “safe space” as AI creates widespread disruption in the search and social channels. Plus, easier access to self-serve CTV ad buying tools and targeting options will lead to a surge in locally-targeted streaming campaigns.

Read our guide to find out why growth marketers should make sure CTV is part of their 2026 media mix.

Learn more.

Before we get into the “how,” let’s look at what the numbers reveal.

The data tells a clear story.
AI’s biggest value isn’t coming from new ideas — it’s coming from repackaging existing work into intelligent products.

Software has always been about leverage.
But the kind of leverage AI provides is different.

The last era of SaaS was about organizing human work — CRMs, dashboards, spreadsheets, and project tools. Humans still drove the process.
The new era of AI products is about executing the work itself — summarizing calls, analyzing feedback, generating designs, writing reports.

In other words:

Old software told people what to do.
AI software does it.

Think of it this way:

The focus is shifting from building tools for humans to building systems that replace the human workflow entirely.

This isn’t about “AI features” anymore.
It’s about transforming entire processes into products.

When you zoom out, nearly every fast-growing AI company shares a common pattern: they started with an internal process that was
1️⃣ repetitive,
2️⃣ data-rich, and
3️⃣ expertise-heavy — then turned it into a product.

Let’s break this down with real examples.

Lawyers spend 70% of their time on document review, research, and summarization. Harvey automated that process, creating a product that lawyers can use daily — not as a sidekick, but as a core workflow.

Their magic wasn’t in the tech; it was in deeply understanding the process first.

Every company collects qualitative data — surveys, NPS, support tickets. The workflow used to be: export → tag → theme → summarize → present.
Relevance AI taught AI to do that workflow automatically and wrapped it in a collaborative interface.

Suddenly, customer feedback became an insight engine.

Adept’s core insight: most employees don’t need a new app. They just need something to do what they already do — faster.
So they built an AI agent that learns from browser actions, mimics human clicks, and executes tasks end-to-end.

Every repetitive workflow on a screen — booking, research, data entry — is now a potential product.

Runway didn’t create new demand for video production; they simplified the entire process of editing, rotoscoping, and storytelling into AI-assisted actions.

They productized creative labor — a process historically too fluid to codify.

What ties all of these together?

They didn’t start from a product idea.
They started from a workflow people already do.

And that’s the biggest mindset shift for the next generation of builders.

Here’s a framework many of today’s most successful AI builders are following, whether they realize it or not.

Don’t build. Watch.
Follow how a task gets done — from input to decision to output.
Where are humans adding judgment? Where are they copying and pasting data? Where are they reformatting information for others?

Every friction point is a product opportunity.

AI can’t yet handle chaos — it thrives on structure.
Map the steps:

You’re not automating tasks — you’re modeling reasoning.

The best AI products aren’t the most capable — they’re the most context-aware.
Context gives AI guardrails.
Feed it your playbooks, past decisions, templates, or tone guides.

Example: Harvey’s legal AI doesn’t “summarize text” generically — it knows the structure of legal clauses, the risk flags, and the compliance rules.

The user interface is what turns automation into trust.
Humans need visibility into what AI is doing.

Think of the best AI products today — Notion AI, Jasper, Relevance AI — they all have one thing in common: clear interfaces that help users collaborate with the system, not just watch it work.

Traditional software optimized for accuracy. AI products optimize for impact.
Ask: does this reduce effort by 90%? Does it improve output quality? Does it shorten feedback loops?

That’s how you measure real product-market fit in AI.

Most startups chase novelty.
But the best AI builders chase repeatability.

Turning workflows into products isn’t just clever — it’s a structural advantage that compounds over time.
Here’s why.

In traditional software, defensibility often came from code complexity, network effects, or brand loyalty.
But in the AI era, the new moat is context.

Every internal workflow is full of proprietary nuance — the kind of invisible expertise that never makes it into public datasets.
It’s the reason a legal AI trained inside a single firm can outperform a general-purpose model fine-tuned on billions of documents.

When you productize your internal logic — the way your team makes decisions, flags risk, or interprets exceptions — you’re building an asset no one else can replicate.
Because even if someone copies your product interface, they can’t copy your judgment logic.

Think about it this way:

That’s zero-to-one defensibility. Not through patents — but through proprietary reasoning.

The hardest part of launching software is changing behavior.
The easiest part of launching AI products is not needing to.

When you turn an existing process into a product, users don’t have to learn a new way of working — they just get a faster, more capable version of what they already do.

That’s why adoption rates for AI copilots are 2–3× higher than for entirely new tools.
They slide into existing workflows instead of forcing users to reinvent them.

That’s the magic of natural demand: when your product feels like déjà vu, users trust it faster.

Because you’re not selling change — you’re selling relief.

Here’s the hidden superpower of workflow-based products: they learn as they work.

Every interaction, approval, or correction becomes a data point.
Every time a user overrides a decision, your system gets a little smarter.

That’s how Relevance AI continuously refines its insight engine — by learning from every tagged comment or feedback correction users make.
Or how Harvey improves its accuracy — by absorbing the reasoning behind every lawyer’s edit.

This feedback loop means AI workflow products can evolve weekly, not yearly.

Traditional software teams wait for quarterly updates or A/B tests.
AI teams running workflow-based systems get live reinforcement signals from every user — a built-in R&D engine that compounds.

The result?
Faster improvement, tighter product-market fit, and a feedback loop that rivals any growth hack you could dream up.

When you combine all three —

you get something rare: a product that improves itself faster than competitors can copy it.

That’s not a one-time edge.
That’s a compounding advantage.

It’s why workflow-native companies like Harvey, Jasper, and Runway scale faster than their peers.
They’re not just building tools — they’re encoding expertise.

It’s tempting to think this movement only applies to scrappy founders. But inside every large company, there are hundreds of hidden workflows waiting to be productized.

In 2024, McKinsey reported that 42% of enterprises had started converting internal AI pilots into customer-facing products.

Examples:

These aren’t just efficiency moves. They’re strategy shifts.

Every company is realizing that their internal process — when automated and scaled — can become a revenue stream.

If you checked 3 or more, you’re probably sitting on a product idea.

Imagine a marketing agency running weekly client performance reports.

Today’s workflow:

A single manager spends 4 hours weekly.

Now apply the AI productization framework:

What used to be a cost center becomes an automated product — a “Reporting AI” that can be sold to other agencies.

That’s what this movement looks like in practice.

The real story of AI in 2025 isn’t about chatbots, copilots, or viral demos.
It’s about something quieter — and much more powerful.

For the first time in decades, the boundaries between “process” and “product” are disappearing.
And when that happens, the way companies create value will fundamentally change.

Because what used to be internal know-how — the workflows, playbooks, and human judgment that lived inside teams — is now becoming code.
AI lets us capture expertise, replicate decision logic, and scale judgment in ways that were once impossible.

That means every company, no matter how old or new, suddenly holds the blueprint for a product it doesn’t even know it has yet.
Your compliance workflow. Your client reporting. Your hiring process. Your creative review system.
All of them can now become digital assets that generate revenue.

This is where the next wave of billion-dollar companies will come from — not new categories, but reimagined processes.

Every era of technology gives rise to a new kind of leverage:

That’s a staggering leap — from doing tasks faster to capturing how humans think.

We’re moving from “software eats the world” to “AI reconstructs it.”
The best builders aren’t coding apps — they’re bottling intelligence.

If you’re an early-stage founder, your next product might already exist — it’s just trapped inside your Notion docs and Slack threads.
If you’re a corporate innovator, your next billion-dollar business might already be hiding in your ops department.

All you have to do is:

That’s the new roadmap.

The companies winning the AI era won’t be the ones who “use” AI — they’ll be the ones built like AI: adaptive, context-aware, and always learning.

They won’t just digitize processes.
They’ll turn processes into products.

And in doing so, they’ll remind us of something deeply human:
That progress doesn’t come from replacing people.
It comes from freeing them to focus on the work only humans can do — imagining, deciding, and creating.

✨ The next great startup idea isn’t waiting to be discovered — it’s waiting to be recognized in the work you already do.n’t about what we build
It’s about what can build itself.

— Naseema

Writer & Editor, The AI Journal 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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