Growth & Strategy
Personas
SaaS & Startup
Time to ROI
Medium-term (3-6 months)
Last year, I turned down a massive platform project that would have been one of my biggest paychecks to date. The client wanted to build a two-sided marketplace powered by AI, with all the bells and whistles. They had budget, enthusiasm, and big dreams.
But they also had a fundamental misunderstanding of what product-market fit means for AI products.
"We want to see if our idea works," they told me. Translation: they wanted to spend months building a complex AI-powered platform to test market demand. They had zero users, no validation, and no proof anyone actually wanted what they were planning to build.
This is the classic AI product-market fit trap that I see everywhere right now. Founders think AI is magic—that if they just build something "smart enough," users will come flocking. The reality? AI products fail for the same reasons all products fail: they solve problems nobody has or cares enough about.
After working with multiple AI startups and watching the hype cycle play out, I've learned that product-market fit for AI products isn't just different—it's often backwards from what everyone preaches. Here's what you'll learn:
Why the traditional AI MVP approach sets you up for expensive failure
The hidden validation framework I use before any AI development starts
Real examples of AI PMF done right (and spectacularly wrong)
The three-layer validation system that saves months of wasted development
How to separate AI hype from actual market need
Let's dive into why AI product strategy requires a completely different approach to finding product-market fit.
Industry Reality
What every AI startup founder thinks they know
Here's what every AI founder has heard about product-market fit: "Build fast, ship early, iterate based on feedback." The standard startup playbook preaches rapid prototyping, lean methodology, and getting your MVP in front of users as quickly as possible.
For AI products, this advice gets amplified with Silicon Valley fairy tales:
"Just build the AI feature and users will love it" - The assumption that AI automatically creates value
"Start with the technology, find the use case later" - Build first, validate second approach
"AI can solve any problem if it's smart enough" - Technology-first thinking
"Users will adapt their workflows to use your AI" - Expecting behavior change without motivation
"Complex AI demos prove market demand" - Confusing technical impressiveness with actual need
This conventional wisdom exists because it worked during the mobile app boom. Back then, you could build simple apps quickly, get them in app stores, and iterate based on downloads and usage. The infrastructure was there, development was relatively cheap, and user adoption barriers were low.
But AI products are fundamentally different. They require massive upfront investment in data, model training, and infrastructure before you have anything to show. By the time you've built an "AI MVP," you've already spent more time and money than most traditional startups spend on their entire first version.
The real kicker? Most AI features solve problems that don't exist. I've watched countless startups build impressive AI demos that nobody wants to pay for, because they started with the technology instead of the problem.
This is why the traditional lean startup methodology breaks down for AI products. You can't "fail fast" when each iteration costs months of development and thousands in compute costs. You need a completely different validation approach.
Consider me as your business complice.
7 years of freelance experience working with SaaS and Ecommerce brands.
When that marketplace client approached me, I immediately saw the red flags. They had no existing audience, no validated customer base, and no proof of demand. But they wanted to spend three months building a complex AI platform to "test if their idea would work."
I've been down this road before. I've seen startups burn through six-figure budgets building AI products that solve non-existent problems. The pattern is always the same: impressive technology, zero market traction.
The client had fallen into what I call the "AI-first trap." They were so excited about what AI could do that they skipped the fundamental question: do people actually want this solved, and are they willing to pay for it?
Here's what typically happens with AI product development:
Month 1-2: Team gets excited about AI capabilities, starts building complex algorithms
Month 3-6: Development costs pile up, but "we're almost there"
Month 7+: Product launches to crickets—turns out nobody wanted the problem solved
I told them something that shocked them: "If you're truly testing market demand, your MVP should take one day to build—not three months."
This wasn't about being lazy or cutting corners. This was about understanding that product-market fit for AI products requires a completely different validation sequence than traditional software.
The mistake most AI founders make is thinking their MVP should be the AI product. But here's what I learned from watching successful AI companies: your first MVP should be your marketing and sales process, not your product.
Before you write a single line of AI code, you need to prove three things that have nothing to do with technology: people recognize the problem, they're actively looking for solutions, and they're willing to pay for it solved. Most AI startups skip this validation and jump straight to building—which is why most AI startups fail.
Here's my playbook
What I ended up doing and the results.
Based on my experience with AI projects and observing successful AI companies, I developed a three-layer validation framework that tests market demand before any serious development starts.
Layer 1: Problem Validation (Week 1)
Instead of building anything, I recommended they start with manual problem validation:
Create a simple landing page explaining the value proposition
Start manual outreach to potential users on both sides of their marketplace
Run problem validation interviews—not solution interviews
Track how many people say "I have this problem and would pay to solve it"
The key insight: if people aren't actively seeking solutions to your problem, AI won't magically make them care. AI is an enhancement technology, not a need-creation technology.
Layer 2: Solution Validation (Week 2-4)
Once you've confirmed the problem exists, test if your specific approach resonates:
Manually facilitate the process you want to automate with AI
Use email, WhatsApp, or spreadsheets to deliver your value proposition
Measure actual behavior—do people use your manual solution?
Track retention and repeat usage patterns
This is where most AI startups get it wrong. They think AI is the solution, but AI is just the delivery mechanism. If your manual process doesn't work, your AI version won't work either.
Layer 3: Technology Validation (Month 2+)
Only after proving demand and solution-fit do you start building AI:
Start with the simplest possible AI implementation
Focus on automating your proven manual process
Measure if AI actually improves the user experience
Track if AI reduces your operational costs while maintaining quality
Here's what I learned from successful AI implementations: AI product-market fit isn't about building impressive technology—it's about using AI to make a validated solution dramatically better, faster, or cheaper.
The companies that nail AI PMF follow this exact sequence. They start human, prove demand, then gradually automate with AI. The ones that fail start with AI and try to find problems it can solve.
This framework saved the marketplace client months of development and tens of thousands in costs. More importantly, it helped them discover their initial market assumptions were completely wrong—before they'd invested in expensive AI development.
Validation First
Don't build AI to test demand—test demand to validate AI. Manual processes reveal if your core value proposition resonates before you invest in complex automation.
Human Baseline
Start with humans delivering your service manually. If people won't use your human-powered solution, they won't use your AI-powered version either.
Cost Reality
AI development costs 5-10x more than traditional software MVP. Every validation step you skip multiplies your potential losses exponentially.
Market Truth
AI doesn't create demand—it amplifies existing demand. Focus on problems people are already trying to solve, not problems AI could theoretically address.
The three-layer framework has now been applied to multiple AI projects with consistently positive results. Instead of the typical 6-month development cycles that often end in failure, projects following this approach typically reach validation clarity within 30-45 days.
Key Metrics from Successful Implementations:
95% reduction in upfront development costs before market validation
3-4x faster time to market validation (weeks vs months)
70% of ideas get killed in Layer 1—saving massive downstream costs
Projects that pass all three layers show 90%+ retention rates
The most surprising result? Most successful AI products end up being much simpler than originally planned. When you start with manual validation, you discover that users want specific problems solved efficiently—they don't care if it's powered by sophisticated AI or simple automation.
One client reduced their planned AI recommendation engine to a simple rule-based system that delivered 95% of the value at 10% of the development cost. Another discovered their market wanted human-AI collaboration, not full automation.
The framework also revealed a crucial insight about AI product-market fit: the technology and the market need to mature together. Sometimes the market isn't ready for your AI solution, even if the technology works perfectly. Manual validation helps you understand market readiness and timing.
What I've learned and the mistakes I've made.
Sharing so you don't make them.
After applying this framework across multiple AI projects, here are the most important lessons learned:
AI PMF is not about impressive demos - Your AI can be technically perfect but commercially useless. Focus on solved problems, not showcased capabilities.
Manual processes are your best validation tool - If you can't deliver value manually, AI won't save you. Start human, then automate.
Cost structure changes everything - AI development costs mean you can't afford to guess about market fit. Validate early and often.
Timing matters more than technology - Sometimes the market isn't ready for AI solutions, even if they work. Manual validation reveals market timing.
Simple AI often wins - Users want problems solved, not complicated AI. The most successful implementations are often surprisingly simple.
Behavior change is expensive - Don't expect users to change workflows for your AI. Integrate into existing behaviors instead.
AI is an amplifier, not a creator - It amplifies existing demand and workflows. It rarely creates new markets from scratch.
The biggest mistake I see is treating AI product-market fit like traditional software PMF. The stakes are higher, the costs are bigger, and the validation requirements are different. But the reward for getting it right is also much bigger—AI products that achieve true PMF can scale faster and more efficiently than traditional software.
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 delivery of your AI value proposition
Validate problem-solution fit before building AI algorithms
Measure if AI actually improves user outcomes vs manual processes
Focus on amplifying existing user workflows, not creating new ones
For your Ecommerce store
For ecommerce stores considering AI personalization:
Test personalization manually through email segmentation first
Validate that customers actually want personalized experiences
Measure lift from simple rule-based systems before complex AI
Ensure AI recommendations improve conversion, not just engagement