Product–Market Fit in the AI Era | How to Measure Trust, Not Just Users

Product–Market Fit in the AI Era | Bettori

Here’s the deal.

Most “product–market fit” (PMF) thinking comes from a world of static apps and predictable features. AI products don’t live there. They’re probabilistic, they keep changing as models improve, and users judge them on trust, not just “wow” moments.

So in the AI era, PMF is less about“Do people like my app?”and more about“Do people trust this AI enough to let it change how they decide and work?”

What PMF really means for AI products

The classic idea from Marc Andreessen: PMF is when a good market pulls a product out of you so fast you can barely keep up. In practice, people use proxies like:

Users keep coming back
They’re willing to pay
They recommend it to others

That still matters. But with AI, there are three extra layers:

Workflow Fit

Is the AI living where people actually work?

Decision Fit

Are better decisions happening because of it?

Trust Fit

Do people believe the system will behave reasonably most of the time?

This is where Miqdad Jaffer’s work is useful. As Product Lead at OpenAI and teacher of the 4D Method, he pushes teams to define AI-specific risks, metrics, and guardrails in an AI-focused Product Requirements Document (PRD), rather than treating AI like “just another feature.”Case Studies

PMF in Action

Klarna

When AI Moves the Needle

2.3MConversations handled in Month 111m → 2mResolution time drop

That is AI PMF: Clear problem (volume), clear movement in metrics (cost/speed).

Twist:Over-optimizing cost led to quality concerns later. PMF is a dynamic balance.

Notion AI

Embedded in Habits

Notion didn’t launch a separate “AI app.” They injected AI into the workspace millions already lived in. Users aren't "trying Notion because of AI"—they are staying longer because AI reduces friction.

"PMF = Do people use the AI as part of their daily workflow without thinking 'this is AI'?"

GitHub Copilot

Speed & Satisfaction

55%Faster coding task completion

PMF here shows up as faster cycle time and better developer experience. However, strong PMF often comes with pushback (worry about code quality/slop). You want a net positive on speed + quality.

So what actually changes?

Old PMF Questions (Still Valid)

• Who is my specific user?
• What job are they hiring this for?
• Are they coming back and paying?

New PMF Questions (The AI Delta)

What decision is this AI influencing?
What is my “good enough” quality bar? (90% vs 99.9%)
Where does trust break first when I scale?

A Simple AI PMF Scorecard

Layer 1: User & Business

Repeat Usage (Daily/Weekly)Task Completion RateTime SavedRisk/Cost Impact

Layer 2: Model & Trust

Accuracy / PrecisionHallucination RateEscalation to HumansAI Behavior Complaints

Layer 3: System & Ethics

• Does the AI behave consistently across user segments and edge cases?

• Are there clear guardrails for high-risk actions?

• Is data usage aligned with privacy expectations?

Building PMF into your PRD

If you’re writing a PRD for an AI feature, don’t just write:
“User can chat with an AI assistant to answer questions.”

Instead, Ask:

What exactly will this assistant own in the workflow?
Where must it stop and hand over?
What will we measure in weeks 1, 4, and 12?

Three questions to ask your team this week

1. If our AI disappeared tomorrow...

What specific decisions or workflows would get noticeably worse? (If "nothing", you don't have PMF).

2. Which metric proves it's not a demo?

Time-to-resolution, error rate, NPS specifically for AI.

3. Where will trust break first if we 10x?

Support quality? Edge cases? Privacy? Regulatory pressure?

“We have users whose work and decisions are materially better because they trust our AI system enough to rely on it every day.”

— That’s the bar.

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