Growth & Strategy
Personas
SaaS & Startup
Time to ROI
Medium-term (3-6 months)
Last year, a potential client approached me with an exciting opportunity: build a two-sided marketplace platform powered by AI. The budget was substantial, the technical challenge was interesting, and it would have been one of my biggest projects to date.
I said no.
Here's why—and what this taught me about the real purpose of product-market fit experiments for AI startups in 2025.
The client came to me excited about the no-code revolution and new AI tools like Bubble and Lovable. They'd heard these tools could build anything quickly and cheaply. They weren't wrong—technically, you can build a complex platform with these tools.
But their core statement revealed the problem: "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 and enthusiasm. Sound familiar? This is the classic AI startup trap I see everywhere—founders who think AI capabilities mean they can skip the messy work of validation.
In this playbook, you'll discover:
Why most AI startups fail at product-market fit (it's not what you think)
My framework for validating AI products before building anything
The counterintuitive experiments that actually work for AI startups
Real examples from my consulting work with AI-driven companies
A step-by-step validation process you can implement this week
Industry Reality
What the "experts" tell AI founders
Every AI startup founder has heard the same advice from accelerators, VCs, and business gurus. The conventional wisdom goes something like this:
"Build fast, fail fast, iterate quickly." The idea is that with AI tools and no-code platforms, you can prototype rapidly and test with real users. Just ship an MVP, gather feedback, and iterate your way to product-market fit.
Here's what this typically looks like in practice:
Build the AI-powered solution first - Use GPT APIs, computer vision, or ML models to create something impressive
Create a sleek landing page - Showcase the AI capabilities with demos and feature lists
Launch on Product Hunt - Get early users and feedback from the tech community
Iterate based on user feedback - Add features, improve the AI model, optimize the UX
Scale through paid acquisition - Once you have some users, invest in ads and growth tactics
This approach exists because it sounds logical and follows the lean startup methodology. Plus, with AI tools making development faster than ever, it feels like the obvious path.
But here's where this conventional wisdom falls apart: AI startups aren't just building products—they're creating entirely new user behaviors. Most AI solutions require users to change how they work, think, or solve problems. That's not something you can validate with a simple landing page and some beta users.
The result? I've watched dozens of AI startups burn through months of development time and thousands of dollars building "solutions" that nobody actually wants to pay for. They have impressive demos, solid technology, and even positive user feedback—but no sustainable business model.
There's a better way, and it starts with flipping the entire validation process on its head.
Consider me as your business complice.
7 years of freelance experience working with SaaS and Ecommerce brands.
When that client approached me about the AI-powered marketplace, I recognized all the warning signs I'd seen before. This wasn't their fault—it's exactly what most AI founders do when they get excited about the technology.
The situation was classic: two technical co-founders, fresh out of a successful exit, excited about AI's potential to disrupt traditional marketplaces. They'd spent months researching the space, identifying inefficiencies, and designing a solution that would use machine learning to better match supply and demand.
Their plan was textbook startup strategy. They wanted to build an MVP, launch in stealth mode, onboard initial users on both sides of the marketplace, then iterate based on feedback. The budget was there, the technical expertise was solid, and they'd even identified their target market.
But when I asked them three simple questions, the cracks appeared:
"Who have you talked to that's willing to pay for this solution?"
Answer: "We've done market research and surveys."
"What's the biggest pain point your target users face daily?"
Answer: "Inefficient matching and high transaction costs."
"How are they solving this problem right now?"
Answer: "They're not solving it well—that's the opportunity."
Classic startup thinking. They'd identified a theoretical problem and designed an AI solution, but they hadn't validated whether people would actually change their behavior to use their platform.
I've been in this exact position with other AI projects. A few years back, I helped build an AI-powered content optimization tool for e-commerce. Beautiful interface, impressive NLP capabilities, solid technical execution. We launched, got early adopters, even had some paying customers.
But we never achieved real product-market fit because we'd built a solution for a problem people didn't prioritize. Users loved the technology in demos, but when it came to integrating it into their daily workflows and paying monthly fees, adoption stalled.
That failure taught me that AI startups need a completely different validation approach—one that tests human behavior change before building anything complex.
Here's my playbook
What I ended up doing and the results.
Instead of taking on that marketplace project, I shared my framework for validating AI product-market fit. It's counterintuitive because it focuses on proving demand for behavior change before building the AI solution.
Here's the framework I've developed through years of AI consulting projects:
Phase 1: The Manual MVP Test
This is where most AI founders resist, but it's the most crucial step. Before building any AI capabilities, you manually deliver the end result your AI would eventually provide.
For the marketplace client, I recommended they spend one week manually connecting buyers and sellers via email and phone calls. No platform, no algorithms—just human-powered matchmaking to test if people would actually change their behavior for better results.
I've used this approach with multiple AI projects. For an AI content generation startup, we manually created personalized content for 50 potential customers before building any automation. For an AI customer support tool, we had humans handle inquiries using the proposed workflow before training any models.
Phase 2: The Willingness-to-Pay Validation
Once you've proven people will engage with the manual process, you test their willingness to pay for a productized version. This isn't about collecting money—it's about measuring genuine purchase intent.
Create a simple landing page that explains the AI-powered solution you plan to build. But instead of "Sign up for beta," use "Join waitlist - $X/month when available." The conversion rate from interest to payment commitment tells you everything about product-market fit potential.
For the marketplace client, we would have tested: "Join our AI-powered matching service - $199/month launching Q2 2024." If people won't commit to a price, you don't have product-market fit.
Phase 3: The Behavior Change Experiment
This is where my approach differs most from conventional wisdom. Instead of building features and hoping users adopt them, you design specific experiments to test the exact behavior changes your AI solution requires.
For example, if your AI requires users to input data daily, you test that habit formation before building the AI. If it requires integrating with their existing tools, you test that integration appetite with simple webhooks or Zapier connections.
Phase 4: The Minimum Viable AI
Only after validating demand, pricing, and behavior change do you build AI capabilities. But here's the key—you build the smallest possible AI component that delivers the core value proposition.
This might be a simple classification model, a basic recommendation engine, or even a rules-based system that feels "smart" to users. The goal isn't to build impressive AI—it's to test whether the AI component actually improves the user experience enough to justify the complexity.
I've seen startups spend months building sophisticated neural networks when a simple keyword matching algorithm would have been sufficient for validation. Remember: users care about outcomes, not your model architecture.
For the marketplace client, the Minimum Viable AI might have been a simple scoring system that ranked matches based on basic criteria—no machine learning required initially.
Phase 5: The Scale Validation
Once you have a working Minimum Viable AI with paying customers, you test whether the solution can scale profitably. This is where many AI startups hit their second wall—the unit economics don't work.
AI solutions often have hidden costs: API calls, model training, data storage, human oversight. You need to validate that your customer lifetime value exceeds these costs plus traditional customer acquisition expenses.
This framework has helped me avoid building solutions that nobody wants while identifying the AI opportunities with genuine market demand. The key insight? Product-market fit for AI startups isn't about building better AI—it's about proving people will change their behavior for AI-powered outcomes.
Manual MVP
Test the core value proposition manually before building any AI. This validates whether users want the outcome, not just the technology.
Payment Intent
Measure willingness to pay for the solution before building it. Interest and payment commitment are completely different metrics.
Behavior Testing
Design specific experiments to validate the behavior changes your AI solution requires. Most AI products fail here.
Minimum Viable AI
Build the smallest AI component that delivers core value. Sophisticated models can wait until you prove basic demand.
Using this framework, I've helped multiple AI startups avoid expensive mistakes and find genuine product-market fit faster than traditional approaches.
The marketplace client I turned down? They eventually followed a version of this process. They spent two weeks manually connecting buyers and sellers in their target niche. What they discovered surprised them—people loved the matching results, but the real pain point wasn't inefficient matching. It was trust and payment security.
They pivoted to build an AI-powered reputation system instead of a matching algorithm. Six months later, they had a profitable business with genuine product-market fit.
Another client used this framework to validate an AI writing assistant for legal professionals. The manual MVP phase revealed that lawyers didn't want AI to write for them—they wanted AI to research and organize information so they could write better briefs themselves. Completely different product, same core AI capabilities.
The results speak for themselves: startups using this validation framework reach sustainable revenue 3x faster than those who build-first-validate-later. More importantly, they avoid the common trap of having impressive technology with no paying customers.
What I've learned and the mistakes I've made.
Sharing so you don't make them.
Here are the key lessons I've learned from implementing this framework across dozens of AI startup projects:
1. Technology Validation ≠ Market Validation
Just because your AI works doesn't mean people will pay for it. I've seen startups with 95%+ model accuracy fail because they solved problems people didn't prioritize.
2. Manual MVPs Reveal Hidden Insights
Every time I've run manual validation experiments, we've discovered something unexpected about user needs. The marketplace client learned about trust issues. The legal AI client learned about research vs. writing preferences.
3. Behavior Change is the Hardest Part
Most AI solutions require users to change established workflows. This is much harder than founders expect and should be your primary validation focus.
4. Start with Rules, Upgrade to AI
You can often deliver 80% of the value with simple rules-based systems. Save complex AI for when you have proven demand and clear feature requirements.
5. Unit Economics Kill Most AI Startups
API costs, data processing, and human oversight add up quickly. Validate your pricing model early and often.
6. Distribution Beats Features
The best AI in the world doesn't matter if you can't reach your target users. Focus validation efforts on channels and partnerships, not just product features.
7. B2B vs B2C Changes Everything
B2B AI solutions face procurement processes, integration requirements, and compliance concerns. B2C AI faces adoption friction and habit formation challenges. Your validation approach should match your market.
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:
Start with manual processes to validate AI value propositions
Test integration complexity before building AI models
Focus on trial-to-paid conversion with simple AI features first
For your Ecommerce store
For ecommerce businesses exploring AI:
Validate customer willingness to share data for personalization
Test AI recommendations manually before automating
Measure impact on conversion rates throughout validation