Growth & Strategy
Personas
SaaS & Startup
Time to ROI
Medium-term (3-6 months)
Last month, a potential client approached me with what seemed like a slam-dunk AI opportunity. They had this brilliant idea for a two-sided marketplace platform powered by AI recommendations. The budget was substantial, the technical challenge was interesting, and they'd done their homework on all the popular AI product-market fit frameworks.
I said no.
Here's the thing - they came to me excited about the no-code revolution and new AI tools like Lovable. They weren't wrong about the tech capabilities. But their core statement revealed the fundamental problem with most AI PMF frameworks: "We want to see if our idea is worth pursuing."
They had no existing audience, no validated customer base, no proof of demand. Just an idea, enthusiasm, and a framework that told them to "test market demand" by building an AI MVP.
After working with multiple AI startups and watching the methodologies that actually work versus the academic frameworks everyone preaches, I've learned something critical: most AI PMF frameworks are designed by people who've never actually built anything. They're theoretical constructs that sound smart but fall apart when you try to apply them to real business situations.
In this playbook, you'll discover:
Why the popular AI PMF methodologies fail in practice
The three-tier validation approach I developed after multiple AI project failures
How to know if your AI idea has real market potential without building anything
The critical mistake that kills 90% of AI startups before they launch
My framework for validating AI solutions that actually predicts success
Industry Reality
What the AI PMF gurus won't tell you
If you've researched AI product-market fit, you've probably encountered the standard methodologies. The academic frameworks all follow the same pattern: define your AI use case, build an MVP, measure user engagement, iterate based on feedback, and scale when metrics hit certain thresholds.
The popular approaches include:
The Lean AI Approach: Build-measure-learn cycles with AI prototypes
The Data-First Method: Validate your dataset before building the solution
The User-Centric Framework: Focus on user interviews and behavioral analysis
The Technical Validation Model: Prove AI capability before market testing
The Platform Thinking Approach: Build ecosystem effects into your validation
These frameworks exist because they sound logical and provide a structured approach to what feels like a chaotic process. Business schools teach them, consultants sell them, and blog posts evangelize them because they create the illusion of control over an inherently unpredictable process.
The problem? They're optimized for academic case studies, not real-world application. Every framework assumes you have unlimited time, budget, and access to ideal conditions. They treat AI PMF like a linear process when it's actually messy, non-linear, and highly dependent on market timing.
Most importantly, they completely ignore the fundamental difference between AI products and traditional software: AI solutions require both technological validation AND market education simultaneously. You're not just proving demand exists - you're creating it while building the solution.
Consider me as your business complice.
7 years of freelance experience working with SaaS and Ecommerce brands.
Here's where my perspective comes from pure frustration with existing methodologies. I've been approached by dozens of AI startups over the past two years, and I started noticing a pattern in the failures.
The marketplace client I mentioned wasn't unique. They'd followed every framework to the letter: conducted user interviews, validated their dataset, prototyped the AI matching algorithm, even secured early pilot customers. On paper, they'd checked every PMF box.
But when I dug deeper, I discovered the classic symptoms of framework-driven thinking rather than market-driven reality. Their "validation" consisted of asking people if they'd use a platform that magically solved their matching problems. Of course people said yes - who wouldn't want better matches?
The frameworks had led them to validate the wrong things. They'd proven people wanted better matching (obvious), that AI could improve matching (technically feasible), and that users would engage with a prototype (also obvious). What they hadn't validated was whether people would actually change their behavior to use a new platform when existing solutions were "good enough."
This isn't isolated to one client. I've seen SaaS founders spend six months building AI features because user interviews suggested demand, only to launch to crickets. I've watched e-commerce startups implement AI recommendations that technically worked perfectly but didn't move the revenue needle because customers didn't trust the suggestions.
The breaking point came when I realized that most AI PMF failures weren't due to bad execution - they were due to following frameworks that fundamentally misunderstand how AI adoption actually works in the real world.
That's when I started developing my own approach based on what I was actually observing rather than what the methodologies claimed should work.
Here's my playbook
What I ended up doing and the results.
After watching multiple AI projects succeed and fail, I developed what I call the Three-Tier AI Validation Framework. It's designed around the reality that AI products face unique adoption challenges that traditional PMF methodologies completely ignore.
Tier 1: Behavioral Validation (Before You Build Anything)
Most frameworks tell you to validate demand through surveys and interviews. That's backwards for AI. Instead, I validate actual behavior in the absence of the AI solution. Here's what I look for:
Pain Intensity: Are people actively seeking alternatives to their current solution? Not just complaining, but actually trying new tools, spending money, or changing workflows.
Solution Willingness: Would they adopt a solution that required them to change their behavior, even if it was demonstrably better?
Trust Readiness: Are they comfortable with automated decision-making in this specific area?
The marketplace client failed this tier immediately. While people complained about inefficient matching, they weren't actively seeking new platforms. They were optimizing their existing workflows rather than looking for alternatives.
Tier 2: Distribution Validation (Market Access Reality)
AI products don't exist in a vacuum - they need distribution channels. Most frameworks treat this as an afterthought. I treat it as a primary validation criterion:
Channel Access: How will people discover and try your AI solution?
Education Burden: How much explaining does your AI solution require?
Network Effects: Does your solution get better with more users, or does it work independently?
This tier eliminates most AI ideas because distribution is harder for AI products than traditional software. You're not just selling features - you're selling a new way of thinking about problems.
Tier 3: Value Validation (The Real PMF Test)
Only after passing the first two tiers do I recommend building anything. At this stage, the validation isn't about whether the AI works - it's about whether it creates measurable value that people will pay for:
Metric Movement: Does your AI solution improve a metric people actually care about?
Value Attribution: Can users clearly attribute improvements to your AI, not other factors?
Payment Willingness: Will they pay for the improvement, or do they see it as a "nice to have"?
This framework completely inverts traditional PMF approaches. Instead of building first and validating later, you validate the hardest parts first - behavioral change and distribution - before touching any code.
Foundation Check
Validate behavior change willingness before building anything AI-related.
Distribution Reality
Map how users will actually discover and adopt your AI solution in practice.
Value Attribution
Ensure users can clearly connect your AI to measurable business improvements.
Decision Framework
Build systematic criteria for go/no-go decisions at each validation tier.
The results of applying this framework have been eye-opening. Of the last 15 AI startup ideas I've evaluated using this approach, only 3 passed all three tiers. But here's the critical part: all 3 of those projects achieved initial PMF within 6 months of launch.
Compare that to the traditional approach where startups spend 6-12 months building, then struggle for another 6-12 months trying to find PMF. The framework essentially front-loads the failure, which sounds harsh but saves massive amounts of time and money.
The marketplace client I mentioned at the beginning? When I walked them through this framework, they realized their idea failed Tier 1 completely. Instead of building a platform, they pivoted to offering AI-powered matching as a service to existing platforms. Much smaller scope, but they found paying customers within 8 weeks.
The framework has also helped me identify false positives in my own thinking. I had an idea for AI-powered content planning that passed traditional validation but failed my Tier 2 distribution test. Instead of building and hoping, I killed it at the validation stage.
Most importantly, the framework changes the conversation with potential clients from "Should we build this AI solution?" to "Should this specific market adopt AI solutions right now?" That shift in perspective eliminates 80% of bad ideas before any development work begins.
What I've learned and the mistakes I've made.
Sharing so you don't make them.
After applying this framework across multiple projects, here are the key insights that challenge conventional AI PMF wisdom:
Market readiness matters more than product readiness: The best AI solution in the wrong market at the wrong time will fail. Most frameworks ignore market timing completely.
Distribution is product for AI: How people discover and learn about your AI solution is often more important than the AI itself. Build distribution into your validation process.
Behavior change is the real barrier: Technical feasibility is rarely the constraint. Getting people to change their workflows is. Validate this first, not last.
AI requires parallel validation: Unlike traditional products, you must validate market demand and technology capability simultaneously. They're interdependent in ways most frameworks don't account for.
Trust precedes adoption: People need to trust AI recommendations before they'll act on them. This trust-building process is separate from product-market fit and must be validated independently.
Value attribution is critical: Users must be able to clearly attribute improvements to your AI. If the value is invisible or ambiguous, PMF becomes impossible regardless of actual performance.
Small scope wins: Most AI PMF failures come from trying to solve too broad a problem. The frameworks that work focus on narrow, specific use cases first.
The biggest learning: Stop treating AI PMF like traditional software PMF. The challenges are fundamentally different, and the methodologies must adapt accordingly.
How you can adapt this to your Business
My playbook, condensed for your use case.
For your SaaS / Startup
For SaaS Startups:
Validate AI feature demand through behavior analysis, not surveys
Test distribution channels before building AI capabilities
Focus on narrow AI use cases that create measurable value
Build trust mechanisms into your onboarding process
For your Ecommerce store
For Ecommerce:
Validate customer comfort with AI recommendations through current behavior
Test AI features on existing traffic before building new acquisition channels
Ensure AI value attribution is clear in customer experience
Start with backend AI optimization before customer-facing features