Growth & Strategy
Personas
SaaS & Startup
Time to ROI
Short-term (< 3 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.
Not because I couldn't do it, but because they were asking the wrong question entirely. They wanted to "test if their idea works" by building a complex AI-powered platform. But here's what I've learned after years of working with startups: if you're truly testing market demand, your MVP should take one day to build—not three months.
This experience taught me something crucial about AI product development: the constraint isn't building anymore—it's knowing what to build and for whom. In the age of AI and no-code tools, everyone can build. The hard part is validation.
In this playbook, you'll learn:
Why traditional MVP thinking fails for AI products
My step-by-step validation framework that costs almost nothing
How to test AI demand before writing a single line of code
Real examples of AI PMF validation that actually worked
When to build vs when to pivot (and how to tell the difference)
This approach has saved my clients thousands in development costs and months of wasted time. More importantly, it's helped them find product-market fit faster by focusing on the right problem from day one.
Industry Reality
What every AI startup founder believes about validation
Walk into any startup accelerator, and you'll hear the same advice repeated like a mantra: "Build fast, ship early, iterate quickly." The lean startup methodology has convinced entrepreneurs that the best way to validate an idea is to build an MVP and see if people use it.
For AI products, this advice gets even more seductive. Founders think: "AI can build anything now, so let's just build it and see what happens." I've seen countless startup teams rush to build AI-powered solutions because the technology makes it feel achievable.
Here's what most AI founders believe they need to do:
Build a working prototype to demonstrate the AI capabilities
Launch to get user feedback and iterate based on usage data
Use no-code/AI tools to build faster and cheaper than ever
Test multiple features to see what resonates with users
Scale once they find traction in the product metrics
This approach exists because it worked in the past when building was expensive and slow. The lean startup methodology made sense when creating a basic web app took months and significant resources. But we're not in that world anymore.
The problem with this conventional wisdom? It optimizes for building, not learning. In today's AI-enabled world, the bottleneck isn't technical execution—it's market understanding. You can build almost anything with AI tools, but that doesn't mean you should.
Most founders end up with a beautiful AI product that solves a problem nobody actually has, or worse, solves a problem people have but won't pay for. They've confused "can we build it?" with "should we build it?"
Consider me as your business complice.
7 years of freelance experience working with SaaS and Ecommerce brands.
When that marketplace client came to me, they had all the classic signs of premature building syndrome. No existing audience, no validated customer base, no proof of demand—just an idea and enthusiasm for what AI could theoretically accomplish.
Their plan was textbook lean startup: build an AI-powered two-sided marketplace, launch it, and see if people would use it. They'd heard that new AI tools could make development faster and cheaper, so why not just build and test?
But here's what I've observed working with dozens of SaaS startups: the companies that succeed don't start by building—they start by proving demand exists. The ones that fail spend months building products that nobody wants, even when those products work perfectly.
I told them something that initially 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. It was about recognizing that in 2025, the constraint isn't technical capability—it's market understanding. We can build almost anything with AI and no-code tools. The hard part is figuring out what people actually want and will pay for.
The client pushed back: "But we need to show the AI capabilities to get people excited!" This is where most founders get stuck. They think the product IS the value proposition, when really the product is just a delivery mechanism for solving a real problem.
I've seen this pattern too many times: startups that build impressive AI demos but can't find paying customers. They have amazing technology solving problems that don't exist, or solving real problems that people won't pay to fix.
Here's my playbook
What I ended up doing and the results.
Instead of building their platform, I walked the client through my step-by-step AI PMF validation process. This framework has saved multiple clients from expensive building mistakes and helped them find real market opportunities faster.
Step 1: Problem Validation (Week 1)
Before building anything, prove the problem exists and people care about solving it. Create a simple landing page or Notion document explaining the value proposition. Not the features—the outcome.
For the marketplace client, this meant describing the pain point their platform would solve, not the AI technology that would power it. The page should answer: "What problem does this solve and who has this problem?"
Step 2: Manual Solution Testing (Week 2-4)
This is the crucial step most founders skip. Manually provide the solution your AI product would eventually automate. For marketplaces, this means manually connecting buyers and sellers via email or WhatsApp.
I told the client: "Become the AI for a month." If you can't manually create value for 10-20 customers, building automation won't magically create value for thousands.
Step 3: Payment Validation (Week 3-4)
Can you get people to pay for the manually-delivered solution? This is where most "great ideas" die. People might love your solution, but love doesn't pay the bills.
For AI products specifically, test if people will pay for the outcome, not the technology. Nobody buys AI—they buy better results faster.
Step 4: Scale Constraint Identification (Month 2)
Once you've manually delivered value to paying customers, identify what's preventing you from scaling. These constraints become your product requirements.
Only build features that remove proven scaling constraints, not hypothetical nice-to-haves.
Step 5: MVP Definition (Month 2)
Now define your actual MVP based on real constraints, not assumed ones. Your MVP should automate the manual processes you've already proven work, starting with the biggest bottlenecks.
This approach flips traditional thinking: instead of building to test if something works, you manually prove it works, then build to scale what's already working.
Problem First
Validate the problem exists before falling in love with your AI solution. Too many founders build amazing technology for problems nobody has.
Manual Testing
Become the AI yourself first. If you can't manually deliver value to 20 customers, automation won't magically create value for 2000.
Payment Reality
Test if people will actually pay for the outcome, not just praise the idea. Love doesn't pay the bills—customers do.
Constraint Focus
Only build features that solve proven scaling constraints, not hypothetical nice-to-haves. Let real bottlenecks drive your roadmap.
The marketplace client initially resisted this approach, but agreed to test it for one month. The results spoke for themselves.
Week 1 Results: Their landing page got 47 signups in the first week, but when they reached out to these "interested" users via email, only 12 responded, and only 3 were willing to have a discovery call.
Week 2-3 Results: During those discovery calls, they realized their original problem assumption was wrong. Users didn't want a marketplace—they wanted better vendor vetting. This insight would have been impossible to discover through a built product.
Week 4 Results: They pivoted to manually providing vendor vetting as a service. Within two weeks, they had 5 paying customers at $200/month each—$1,000 in monthly recurring revenue before writing a single line of code.
The manual process revealed what their eventual AI product should actually do: automate vendor research and due diligence, not facilitate marketplace transactions. This was a completely different product than what they originally planned to build.
Six months later, they launched their actual AI product—a vendor vetting tool—and hit $10K MRR within 60 days because they'd already proven market demand and had a waiting list of customers who'd experienced the value manually.
What I've learned and the mistakes I've made.
Sharing so you don't make them.
This experience taught me several crucial lessons about AI PMF validation:
Technology capability doesn't equal market need. Just because AI can build something doesn't mean you should build it.
Manual validation reveals insights automated testing can't. You learn different things from doing the work manually versus watching usage analytics.
Payment validates demand better than any metric. People vote with their wallets, not their survey responses.
The best AI products automate proven manual processes. Don't use AI to create new behaviors—use it to scale existing ones.
Problem evolution is normal and valuable. The problem you end up solving is rarely the one you started with.
Early revenue beats early users. 5 paying customers teach you more than 500 free users.
Constraints drive better product decisions. When you know exactly what's preventing you from scaling, you know exactly what to build.
The biggest lesson? In the age of AI, the competitive advantage isn't building faster—it's learning faster. Anyone can build with AI tools, but not everyone can identify the right problems to solve.
How you can adapt this to your Business
My playbook, condensed for your use case.
For your SaaS / Startup
For SaaS startups looking to validate AI product-market fit:
Start with customer development, not product development
Test willingness to pay before building features
Use manual processes to validate automated solutions
Focus on outcomes, not AI capabilities in your messaging
For your Ecommerce store
For ecommerce businesses exploring AI features:
Validate AI use cases through manual testing first
Test customer willingness to pay for AI-enhanced experiences
Focus on conversion impact, not just engagement metrics
Prioritize proven bottlenecks over hypothetical improvements