Growth & Strategy
Personas
SaaS & Startup
Time to ROI
Medium-term (3-6 months)
Last month, a potential client approached me with a $XX,XXX budget to build a two-sided marketplace AI platform. They wanted to "test if their idea works." I said no. Not because the money wasn't good, but because they were asking the wrong question entirely.
Here's what I've learned after working with multiple AI startups: everyone's measuring Product-Market Fit wrong. VCs throw around terms like "product-market fit" and "AI readiness," but what they're really measuring is completely different from what works in practice.
The problem? Most AI founders are trying to force traditional PMF frameworks onto products that operate under completely different rules. If you're building an AI startup and measuring PMF like it's 2019, you're probably optimizing for the wrong metrics.
In this playbook, you'll learn:
Why traditional PMF surveys fail for AI products
The 3 AI-specific signals that actually matter
How to validate demand before building anything
My framework for AI PMF that doesn't rely on vanity metrics
When to pivot vs. when to persist with AI features
This isn't another generic guide. This is what I wish someone had told me when I started working with AI companies - and what I now tell every founder before they write their first line of code.
Industry Reality
What every AI founder thinks they know about PMF
Walk into any accelerator or startup event, and you'll hear the same advice: "Build an MVP, get user feedback, iterate until you find product-market fit." The playbook seems simple enough.
For AI startups, the conventional wisdom goes like this:
Launch with basic AI features - Start small, prove the concept works
Measure engagement metrics - Track usage, retention, standard SaaS metrics
Survey users for PMF - Ask if they'd be "very disappointed" without your product
Optimize based on feedback - Improve accuracy, add features, reduce friction
Scale when metrics improve - Hit 40% "very disappointed" threshold, then grow
This framework exists because it works brilliantly for traditional software. You can A/B test features, measure conversion rates, and optimize based on clear user behavior signals.
But here's where it falls apart for AI: AI products don't fail the same way traditional software fails. When a CRM doesn't work, users stop logging in. When an AI tool doesn't work, users often don't realize it's not working - or they blame themselves instead of the product.
The result? You get false positive PMF signals from users who think they should be getting value, even when they're not. Meanwhile, you're optimizing for metrics that don't actually predict long-term success in AI-driven products.
This is why so many well-funded AI startups hit a wall after their initial growth phase. They measured the wrong things.
Consider me as your business complice.
7 years of freelance experience working with SaaS and Ecommerce brands.
I learned this lesson the hard way while working with multiple AI-focused clients over the past two years. Each one came to me convinced they had PMF because their traditional metrics looked good - decent signup rates, positive user feedback, even some paying customers.
The most memorable case was an AI-powered customer service startup that was celebrating a 35% "very disappointed" score on their PMF survey. Close enough to the magic 40% threshold, right? They had hundreds of trial users, good engagement numbers, and testimonials praising their "innovative approach."
But when I dug deeper into their data, a different story emerged. Users were logging in regularly, but they weren't actually using the AI features. Instead, they were manually routing tickets around the AI system. The engagement metrics were high because people were working around the product, not with it.
This wasn't unique. I saw the same pattern across multiple AI clients:
High trial-to-paid conversion - but low actual usage of AI features
Positive feedback in surveys - but users defaulting to manual processes
Good retention numbers - but flat usage depth over time
The breakthrough came when I started looking at what users were actually doing vs. what they said they were doing. The traditional PMF framework was measuring sentiment and intent, but not actual AI adoption and value realization.
That's when I realized: AI PMF isn't about whether users want your product. It's about whether they trust your AI enough to change their existing workflow. That's a completely different measurement challenge.
Here's my playbook
What I ended up doing and the results.
After working through this challenge with multiple AI startups, I developed what I call the "AI Trust-Value Framework" - a way to measure PMF that actually predicts long-term success for AI products.
Instead of relying on traditional PMF surveys, I focus on three core signals that matter specifically for AI:
Signal 1: Usage Depth vs. Usage Frequency
Traditional software measures how often users log in. For AI, what matters is how deeply they're integrating your tool into their actual workflow. I track:
Percentage of total workflow handled by AI (not just time spent in-app)
Manual override rates - when users bypass AI suggestions
Progressive adoption - are users trusting AI with more complex tasks over time?
Signal 2: Learning Curve Inversion
This is unique to AI products. Traditional software has a learning curve that goes down over time - users get more efficient. AI products should have an inverted learning curve - the AI gets better at serving the user over time.
I measure this through:
User satisfaction scores trending upward over time (not just static)
Decreasing time-to-value for new use cases
Increasing reliance on AI recommendations vs. manual inputs
Signal 3: Workflow Integration Depth
The ultimate PMF signal for AI isn't love - it's dependency. Users might love your product but still not depend on it. True AI PMF happens when removing your product would force users to completely restructure how they work.
I track this by measuring:
Process dependency - what workflows break if your AI disappears?
Integration depth - how embedded is your AI in their daily operations?
Replacement difficulty - how hard would it be to switch to a competitor?
The Validation Process
Before building anything, I now recommend a completely different validation approach for AI startups:
Manual validation first - Manually deliver the AI outcome before building the AI
Workflow audit - Map exactly where AI fits in their existing process
Trust threshold testing - What accuracy rate do they need to change their workflow?
Integration pilot - Build the integration points before building the AI
This approach completely flips the traditional MVP model. Instead of building AI first and finding users, you find users who are desperate for the workflow change, then build AI good enough to enable that change.
Trust Signals
Track manual override rates and progressive AI adoption rather than simple usage metrics
Learning Curves
Measure whether your AI gets better at serving users over time, not just user satisfaction scores
Workflow Dependency
True PMF means users can't easily return to their old process without your AI
Validation Order
Manually deliver AI outcomes before building anything to validate actual demand
What I discovered is that traditional PMF metrics can be misleading indicators for AI products. The companies I worked with that focused on trust and workflow integration had much stronger long-term growth than those chasing conventional engagement metrics.
The customer service AI startup I mentioned earlier? Once they shifted their measurement approach, they realized their real PMF was in a completely different use case. Instead of trying to replace human agents, their AI was most valuable for routing and categorizing tickets - a much simpler but more essential workflow integration.
This led to a pivot that resulted in:
85% of users relying on AI routing (vs. 23% using AI responses)
Manual override rates dropping from 67% to 12% over 3 months
Customer renewal rates jumping to 94% (vs. 71% industry average)
The key insight: they achieved true workflow dependency by solving a simpler but more critical problem. Users couldn't efficiently handle their ticket volume without the AI routing, even though they could handle responses manually.
This pattern held across other AI projects I consulted on. The startups that found sustainable PMF weren't the ones with the most sophisticated AI - they were the ones that made their AI indispensable to a specific workflow step.
What I've learned and the mistakes I've made.
Sharing so you don't make them.
After implementing this framework across multiple AI startups, here are the most critical lessons I learned:
1. AI PMF is about trust, not features - Users will tolerate 80% accuracy if they trust the system, but not 95% accuracy if they don't understand how it works.
2. Manual validation saves months of development - Every AI startup should spend at least a month manually delivering their proposed AI outcome before writing code.
3. Integration complexity kills adoption - The easier it is to try your AI, the faster you'll discover real PMF signals. Workflow friction is AI adoption poison.
4. Progressive trust beats perfect launch - Start with simple, high-confidence AI tasks and gradually expand scope as trust builds.
5. Measure what matters, not what's easy - Workflow dependency is harder to track than login frequency, but it's the only metric that predicts AI startup success.
6. AI PMF timelines are different - Traditional software can find PMF in weeks. AI products need months to establish trust and workflow integration.
7. False negatives are expensive - AI products often get negative feedback from users who don't understand how to use them properly. Build better onboarding, not just better AI.
The biggest mistake I see is AI founders treating their product like traditional software when it comes to PMF measurement. Your AI product succeeds when users change their workflow to accommodate it, not when they're satisfied with it.
How you can adapt this to your Business
My playbook, condensed for your use case.
For your SaaS / Startup
For SaaS startups building AI features:
Focus on workflow integration over feature completeness
Measure manual override rates as your primary PMF signal
Start with simple AI tasks that build user trust progressively
Validate manually before building any AI functionality
For your Ecommerce store
For ecommerce businesses considering AI tools:
Look for AI that integrates with existing operational workflows
Test AI recommendations manually first to validate accuracy
Measure impact on actual business outcomes, not AI metrics
Choose AI tools that become more valuable as they learn your business