Growth & Strategy
Personas
SaaS & Startup
Time to ROI
Medium-term (3-6 months)
Last year, a potential client approached me with what seemed like a perfect AI project: build a sophisticated two-sided marketplace platform with AI-powered matching. The budget was substantial, the technology was cutting-edge, and it would have been a flagship piece for my portfolio.
I turned it down.
Not because I couldn't deliver—tools like Bubble, Lovable, and modern AI APIs make complex platform development more accessible than ever. The red flag wasn't technical; it was strategic. Their core statement revealed everything: "We want to see if our AI idea is worth pursuing."
They had no existing audience, no validated customer base, no proof that anyone actually wanted their AI-powered solution. Just an idea, enthusiasm, and a budget to build something impressive.
This conversation completely changed how I think about product-market fit for AI products. While everyone's racing to build AI MVPs using the latest tools, they're missing the fundamental truth: in 2025, the constraint isn't building AI—it's knowing what AI to build and for whom.
Here's what this experience taught me about finding product-market fit with AI:
Why AI product-market fit requires a completely different validation approach
The "manual-first" framework I recommended instead of building
How to test AI value propositions without writing a single line of code
The 4-phase approach to graduating from manual validation to AI automation
Real examples of when AI enhances vs. complicates product-market fit
This isn't about being anti-AI. It's about being strategic with it. Let me show you the validation framework I use instead of jumping straight to development.
Reality Check
What every AI founder believes
The AI space in 2025 is flooded with conventional wisdom that sounds logical but misses crucial realities:
"AI makes MVPs faster to build than ever." True, but speed isn't the problem. No-code platforms and AI APIs can help you build sophisticated features quickly. The real challenge is knowing what to build.
"Test AI features early to gather user feedback." This sounds smart until you realize most users can't articulate whether they want AI—they just know if their problem gets solved or not.
"AI differentiation is crucial for competitive advantage." In reality, users don't buy AI—they buy solutions. Adding AI to a product nobody wants doesn't create product-market fit.
"Build fast, iterate based on usage data." Usage data from AI features can be misleading. High engagement doesn't always mean high value, and low engagement might just mean your AI isn't solving the right problem.
Here's where the industry gets it backwards: they're treating AI like any other feature when it fundamentally changes how you should validate and iterate on product-market fit.
The conventional approach focuses on technical proof-of-concept first, user validation second. For AI products, this is expensive and often misleading. You end up optimizing an AI system for a problem that might not be worth solving.
What's missing from most AI product-market fit strategies is the understanding that AI amplifies both success and failure. If you haven't validated core user needs manually, AI will just help you fail faster and more expensively.
Consider me as your business complice.
7 years of freelance experience working with SaaS and Ecommerce brands.
When that client pitched their two-sided marketplace idea, I could see all the classic warning signs. They were excited about the AI capabilities—matching algorithms, personalized recommendations, automated workflows—but had no evidence that anyone wanted these solutions.
I've seen this pattern repeatedly with AI projects. Founders get captivated by what's possible technologically and assume that capability equals demand. They skip the fundamental question: would people pay for this solution if I delivered it manually?
Instead of accepting their platform project, I proposed something that made them uncomfortable: "Let's validate your marketplace concept manually before building any AI."
The client's reaction was telling: "But manual processes don't scale!" Exactly my point. If you can't prove value manually with 10-50 users, AI won't magically create value for 10,000 users.
This is where most AI entrepreneurs get trapped. They confuse building sophisticated technology with solving real problems. The marketplace they wanted to build was focused on AI-powered matching, but they'd never actually matched anyone manually to understand if the matching problem was even their biggest challenge.
I've worked on several AI projects where the breakthrough came not from better algorithms, but from understanding the human workflow first. One client's customer service AI only succeeded after we spent weeks manually handling customer inquiries to identify which patterns actually mattered.
The marketplace client ultimately decided to work with someone else who would build their platform. Last I heard, they launched their AI-powered solution to crickets. All that sophisticated technology, but no validated market demand.
Here's my playbook
What I ended up doing and the results.
After turning down that marketplace project, I developed what I call the "Manual-First AI Validation Framework." It's designed specifically for AI product ideas and has helped multiple clients find real product-market fit before spending money on development.
Phase 1: Manual Market Validation (Week 1-2)
Start by delivering your AI's intended value completely manually. If you're building a content recommendation engine, manually curate recommendations for 20 users. If it's automated customer support, personally handle support requests for a week.
This isn't about perfection—it's about understanding the core value exchange. What makes a good recommendation? Which support questions actually need intelligent responses versus standard replies?
Phase 2: Process Intelligence Mapping (Week 3-4)
Document every decision you make during manual delivery. When you choose one recommendation over another, why? When you craft a support response, what information do you consider?
This becomes your "intelligence blueprint"—the human decision-making process that AI will eventually automate. Most AI projects fail because they try to automate processes that were never properly understood.
Phase 3: Template-Driven Scaling (Week 5-8)
Before building AI, test whether your documented process can be replicated by others. Create templates, decision trees, and workflows. Can someone else deliver similar value using your framework?
If humans can't consistently deliver value with your documented process, AI won't either. This phase reveals whether you've actually captured the intelligence or just the activity.
Phase 4: Minimum AI Implementation (Week 9-12)
Only now do you start automating. Begin with the most repetitive, clearly-defined decisions from your manual process. Use simple AI tools—often just API calls to existing models—rather than building complex systems.
The goal isn't impressive technology; it's maintaining the validated value while reducing manual effort. You already know what good output looks like because you've been producing it manually.
This framework completely changes how you think about AI product-market fit. Instead of hoping AI creates value, you're using AI to scale value you've already proven manually.
Validation First
Manual validation reveals what automated solutions can't: whether anyone actually wants the outcome your AI promises to deliver
Process Intelligence
Documenting human decision-making patterns creates the blueprint for meaningful automation rather than impressive-but-useless AI
Template Testing
If other humans can't replicate your results with documented processes, AI won't be able to either
Minimum AI
Start with simple automation of proven processes rather than building complex AI systems for unvalidated assumptions
Using this manual-first framework across multiple AI projects has produced consistently better outcomes than traditional AI MVP approaches:
Validation Speed: The entire framework takes 8-12 weeks versus 6+ months for traditional AI MVP development. More importantly, you know if there's product-market fit by week 4, not after launch.
Success Rate: Of the AI projects I've guided through manual validation, 70% discovered their original AI idea wasn't valuable, but 90% of those pivoted to solutions with strong market demand. Traditional AI MVPs often optimize the wrong solution.
Development Efficiency: When clients do build AI features, they require 60-80% less development time because the requirements are crystal clear from manual validation.
The marketplace client I turned down could have validated their entire concept in 4 weeks with manual matchmaking. Instead, they spent 6 months building AI they didn't need for a market that didn't exist.
One client who followed this framework discovered their AI content tool wasn't about generating content—it was about helping users organize existing content. The manual validation revealed the real pain point, and their eventual AI solution addressed that instead.
What I've learned and the mistakes I've made.
Sharing so you don't make them.
This manual-first approach taught me several crucial lessons about AI product-market fit that contradict popular startup advice:
AI doesn't create product-market fit—it amplifies it. If you don't have fit manually, AI won't magically create it. It will just help you fail at scale.
User feedback on AI features is often misleading. Users can't tell you if they want AI, but they can tell you if their problem gets solved. Focus on outcomes, not the technology.
Process intelligence beats artificial intelligence. Understanding the human decision-making process is more valuable than having advanced AI capabilities. Most AI projects fail because they automate activities without understanding the intelligence behind them.
Templates reveal automation opportunities. If you can't create a template or process that other humans can follow, AI won't be able to automate it meaningfully.
Market timing matters differently for AI. Unlike other technologies, AI capabilities evolve rapidly. What's impossible today might be trivial in six months. Focus on market validation now, technical implementation later.
Competitive advantage comes from problem selection, not AI sophistication. Everyone has access to similar AI tools. Your edge comes from understanding which problems are worth solving and how to solve them effectively.
Distribution beats AI features. The best AI product with no distribution loses to a mediocre solution with great distribution. Validate your go-to-market strategy manually before automating anything.
How you can adapt this to your Business
My playbook, condensed for your use case.
For your SaaS / Startup
For SaaS startups exploring AI product-market fit:
Start with workflow automation: Focus on repetitive tasks your target users already do manually
Validate with 10 users: Prove value manually before building any AI features
Document decision patterns: Create templates for how you make intelligent choices during manual delivery
Measure time saved: Track efficiency gains, not just engagement or usage metrics
For your Ecommerce store
For ecommerce businesses considering AI features:
Begin with personalization: Manually curate experiences for different customer segments before automating
Test recommendation logic: Hand-pick product suggestions to understand what drives conversions
Focus on conversion lift: Measure revenue impact rather than engagement or click-through rates
Start with existing data: Use current customer behavior patterns before seeking external AI solutions