👋 Hey friends,

Let’s be honest — in this AI gold rush, the hardest part isn’t writing code or even launching fast.
It’s choosing wisely.

We’ve entered a phase where the barrier to building has all but disappeared.
You can create an MVP in a weekend, launch a demo page in hours, and get a flood of “Nice work!” comments on LinkedIn within minutes.

But what happens next?
Most of those AI products quietly die within months.
Not because the founders weren’t talented — but because they mistook what was possible for what was valuable.

This edition is for those who want to break that pattern.
Whether you’re a founder, product lead, or creative builder, today we’ll unpack:

By the end, you’ll walk away with a repeatable system for identifying AI products that are not only buildable, but inevitable.

— Naseema Perveen

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Most AI initiatives don’t deliver measurable business value — yet.
According to research from MIT’s The GenAI Divide: State of AI in Business 2025 report, about 95 % of generative AI pilot projects fail to produce meaningful profit or loss impact because teams focus on superficial automations rather than deeply integrated workflows. Only 5 % of pilots were linked to rapid revenue acceleration or scaled deployment.

AI failures aren’t due to lacking models — they’re due to workflow integration.
The MIT findings show that the key barrier isn’t the quality of AI technology itself, but how poorly it is integrated into existing operational systems and decision processes, causing most projects to stall before they deliver strategic value.

AI adoption varies widely around the world, creating uneven opportunities. The World Bank’s 2025 Digital Progress and Trends Report reports that while generative AI tools have reached more than half a billion global users within two years of ChatGPT’s launch, intentional organizational adoption remains limited (around 8 %) even in advanced economies. This highlights a gap between casual use and strategic deployment.

Emerging “Small AI” solutions are spreading rapidly, especially where barriers to entry are low. The World Bank also highlights a growing trend of affordable, easy-to-use AI applications (“Small AI”) running on mobile devices, particularly in sectors like agriculture, health, and education in low- and middle-income countries. These localized applications demonstrate that practical, domain-specific AI adoption is already underway outside the typical tech hubs.

AI adoption in business is shaped by leadership, workflow design, and use-case fit.

Research from MIT Sloan found that companies that truly embed AI into production workflows (not just experiments) are more likely to derive measurable value, and that adoption also correlates with organizational characteristics such as process innovation and leadership experience.

AI has turned possibility into the easiest part of the job.
Anyone can prompt their way into an MVP now.

The constraint isn’t technology anymore. It’s taste.

Ask yourself:“ Would this still matter if AI disappeared tomorrow?”

If the answer is no, you’re building a feature, not a foundation.

Founders who last look for three signals before writing a single prompt:

✅ A recurring human bottleneck — repetitive judgment, coordination, or decision fatigue
✅ A measurable outcome — saves time, reduces cost, increases confidence
✅ An emotional trigger — frustration, anxiety, or ambition strong enough to drive adoption

Bad framing: “AI that writes meeting notes.”
Better framing: “A shared memory layer that captures what your team forgets.”

The second one isn’t about automation. It’s about leverage.

Before falling in love with an idea, run it through this matrix:

The best ideas sit at the intersection of high friction and high frequency, embedded in existing flow.

“Find the bottleneck, not the blue sky.”

Example:
An AI code-review startup integrated directly into GitHub PR threads rather than launching a separate dashboard.

Adoption went from zero to 90 % in two sprints.

Forget “idea validation in a weekend.”
Real founders don’t sprint to validation — they cycle toward it.

The goal isn’t to confirm your hunch; it’s to stress-test it until what’s left is worth building.

A realistic validation loop takes 7 – 14 days — fast enough to learn, slow enough to think.

Start with unfiltered reality. Don’t ask friends. Don’t ask, “Would you use this?”
Study what people already do — and where they struggle.

Workflow:

Emotion signals energy.
Then ask: “Cluster these complaints by type — emotional, operational, informational.”

Now you’ve mapped the friction zones.

Turn patterns into one crisp statement:

“I believe [this group] struggles with [this friction] because [reason].
If AI could [specific capability], it would [desired outcome].”

Test this with 3 – 5 real users.
If they say “That’s exactly my life,” you’re onto something.
If they say “Interesting idea,” you’re still too abstract.

Skip code. Visualize.
Use Figma AI or ChatGPT to draft a one-screen mock.

“Create a dashboard for recruiters to drag resumes and get instant insights.
Keep it minimal and human.”

Show it on Zoom. Watch where they hover first.
Their confusion is data.

Then measure pull.
Ignore compliments; measure action.

Ask:

If yes, move ahead.
If not, re-frame the problem.

Use Galileo, Lovable, or v0.dev to make a clickable demo.
Return to the same users.

“Does this solve what you described last week?”

If the answer is no, you saved six months.
If yes — you’ve earned your first build sprint.

Validation isn’t about speed; it’s about compression — collapsing time between curiosity and clarity.
AI helps you learn faster, not skip learning.

Most AI products die because they’re 20 % better, not 10× different.

Ask:

If fewer than a third would rebuild it manually, keep iterating.

Transformation, not novelty, is your edge.

The best AI products hide inside existing behavior.
They don’t scream “AI.”

Example:
A B2B startup flagged churn-risk accounts directly inside Salesforce.
No dashboards. Just a subtle ⚠ icon.
Adoption: 95 %.

Users don’t want new tools.
They want familiar ones that think with them.

“Finding” product-market fit is a myth.
In AI, fit is a conversation.

You start with assumptions, test them through prompts, and let data talk back.

One founder built an AI interview assistant that summarized calls.
Users said, “Cool, but I still have to pull next steps.”
The new version auto-generated follow-up tasks by stakeholder.
Retention doubled.

Fit evolves through listening loops.

Timing kills more startups than tech does.

Check:

If any answer is “not yet,” pivot to workflow tooling first.

Don’t automate tasks — automate thinking context.

For this week’s feature, we asked Nicolas Babin, Business Strategist, Serial Entrepreneur (26 Startups), Board Member, and Author of The Talking Dog, a question that lies at the heart of innovation in the AI era:

“What’s your process for spotting high-impact AI product ideas worth building?”

Here’s how he put it:

Nicolas’s reflection is a masterclass in discernment — a reminder that true innovation isn’t about speed or hype, but about solving problems that have long resisted easy answers.

Every founder today starts with the same toolbox.
You can spin up a prototype with GPT-4, train an image model with Replicate, or plug into any API from Mistral to Claude.
Capital is abundant, code is commoditized, and talent is increasingly global.

So if technology and money are no longer the differentiator — what is?

It’s judgment.
Not just intuition — but judgment velocity: how fast you can sense, test, and adjust to reality.

This is what defines the modern founder’s edge.
AI can multiply your output.
But judgment determines whether that output compounds or collapses.

In the pre-AI era, decisions moved as fast as information.
You needed meetings, reports, decks — human bottlenecks everywhere.
Today, AI collapses that latency. Every insight, customer review, or competitive shift is visible instantly.

That means your ability to process, contextualize, and act on new signals is the only thing left that compounds.

Call it “founder reflex”:
the muscle of converting information into intelligent motion.

The best founders aren’t necessarily smarter.
They’re just more iterative.
They run more reflection loops per week, and that gives them a 10× surface area of learning.

Try running your startup like an AI-augmented decision lab.

Ask ChatGPT or Claude:

“What changed in user sentiment or market dynamics since yesterday?”

Feed it data — tweets, user feedback, sales notes, competitor updates, anything that captures real-time noise.
The goal isn’t to get answers. It’s to see patterns early.

AI will summarize themes like:

This is your radar.
Instead of waiting for quarterly reviews, you get a live pulse every morning.

Ask:

“Which of our assumptions are treated as facts but haven’t been tested?”

This one question can prevent six-figure mistakes.
AI can cross-reference your docs, memos, and press releases to highlight hidden leaps of faith — claims that sound solid but rest on thin data.

Example:

These are assumptions disguised as facts.
When surfaced daily, they become hypotheses you can test quickly, not anchors that slow you down.

Ask:

“What lessons today should reshape tomorrow’s priorities?”

Every founder collects hundreds of small insights per week that vanish into thin air — forgotten Slack threads, half-written notes, or gut feelings after calls.
By feeding those reflections to ChatGPT nightly, you turn transient awareness into durable knowledge.

The system’s power compounds over time.
Save each reflection into Notion or Obsidian with tags like user-sentiment, pricing, or retention.
Then, at the end of the month, run a meta-query:

“Summarize the recurring risks, opportunities, and blind spots across all my reflections.”

What emerges is not a report.
It’s a judgment mirror — a map of how your thinking evolved, where you over-corrected, and where intuition proved right.

It’s how you transform gut feeling into institutional intelligence.

A SaaS founder building a creator analytics tool adopted this rhythm:

After three weeks, they discovered their biggest churn driver wasn’t competition — it was confusion.
Users didn’t understand the dashboard metrics.
Within one sprint, they redesigned onboarding and cut churn by 18 %.

That’s judgment velocity in action — insight turned into improvement before metrics hit crisis levels.

With all the automation at our fingertips, it’s easy to forget the one variable that machines still can’t model — human emotion.

AI can map preference.
It can’t feel frustration.

It can describe empathy.
It can’t deliver it.

Every meaningful product still begins with the moment of discomfort — watching a real user struggle, hesitate, or sigh when something doesn’t make sense.
Those moments create emotional imprints that drive design far better than analytics alone.

Used right, AI doesn’t dilute empathy.
It scales it.

Imagine you run a marketplace app.
Instead of reading 10 support tickets, AI can summarize 1,000 user complaints, cluster them by emotion (“anger,” “confusion,” “disappointment”), and surface which moments consistently break trust.

You’re not replacing listening.
You’re expanding your capacity to hear.

AI surfaces patterns of pain; you bring context and compassion.

Automation without empathy is noise.
Empathy without automation is burnout.

The balance is co-building:

Design decisions should always pass through both lenses:

The best founders keep empathy loops alive even as they automate.
Because no matter how advanced AI becomes, taste, intuition, and courage remain human monopolies.

By 2026, every company will have roughly the same access:
APIs, models, infrastructure, even distribution channels.

Speed is no longer scarce.
Judgment is.

When capital, code, and compute converge, differentiation shifts upstream — into how leaders reason under uncertainty.

AI can:

But it can’t decide what matters to you.
That’s still human work.

Execution is abundant.
Values are rare.
And judgment is how values meet execution.

Founders who treat AI as a judgment assistant, not a decision engine, evolve faster.
They let the machine compress data — so they can expand discernment.

Good judgment creates fewer dead ends.
Each correct call saves months of rework and millions in opportunity cost.

Over time, that efficiency compounds — not linearly, but exponentially.
A founder who avoids three wrong bets in a year gains more than one who executes ten mediocre ones quickly.

AI can make you faster.
But clarity makes you unstoppable.

Tomorrow’s breakout startups won’t be “AI companies.”
They’ll be human companies with AI reflexes.

Their workflows will look less like rigid hierarchies and more like adaptive feedback systems:

Fifteen minutes per session.
But over quarters, you’ll build something priceless — a searchable history of your team’s reasoning.

This becomes your judgment graph — a living, evolving record of why decisions were made, not just what decisions were made.

Traditional companies document actions.
AI-first companies document thinking.

When decisions are logged as reflections — tagged, searchable, and revisitable — you can revisit old trade-offs, retrace logic, and onboard new teammates with context that usually takes months to absorb.

You’re not just scaling product development.
You’re scaling organizational clarity.

A health-tech startup using this rhythm noticed that every week, their reflections mentioned data privacy concerns.
It wasn’t flagged in metrics, but the repeated mention surfaced a pattern: users didn’t trust how data was handled.

Within a month, they introduced transparent dashboards showing exactly how data was used.
Trust scores rose 27 %, retention improved 14 %.

That’s the payoff of a human-AI reflection loop — small observations turning into outsized advantage.

AI isn’t replacing founders.
It’s refining them.

It removes the administrative fog — the note-taking, formatting, endless analysis — so you can focus on what truly matters: What’s worth building?

When execution is cheap, discernment becomes priceless.
Anyone can generate a product roadmap.
Few can identify which problem is worth devoting five years of their life to.

So before you chase the next big AI idea, pause and ask:

“Am I solving something the world needs — or just something AI can do?”

That single question separates builders from noise.

The world doesn’t need more products.
It needs clearer judgment.
Because when everyone can build fast, speed stops being an edge.

What remains rare — and powerful — is the ability to decide wisely.

AI gives you leverage.
Empathy gives you direction.
Judgment gives you durability.

The founders who merge all three will define the next decade — not as the ones who automated fastest, but as those who discerned deepest.

— Naseema
Writer & Editor, AIJ Newsletter

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