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 MVPs in 2025. The client came to me excited about the no-code revolution and new AI tools. They'd heard these tools could build anything quickly and cheaply. They weren't wrong — technically, you can build a complex AI 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.
This experience reinforced a principle I now share with every client considering an AI MVP: In the age of AI and no-code, the constraint isn't building — it's knowing what to build and for whom.
Here's what you'll learn from my approach to AI product validation:
Why most AI MVPs fail before they even launch
The validation framework that saved my client $50,000
How to test AI product-market fit without building anything
The critical difference between AI demos and real validation
When to actually start building your AI MVP
Market Reality
What the AI hype cycle teaches us about validation
The AI industry is drowning in a sea of "solutions looking for problems." Every startup accelerator, every tech blog, every LinkedIn guru is preaching the same gospel: build fast, ship faster, iterate based on feedback.
The conventional wisdom goes like this:
Identify a problem (usually one you personally experience)
Build an AI-powered solution using the latest tools
Launch quickly to get user feedback
Iterate based on usage data and user interviews
Scale once you find product-market fit
This approach exists because the barrier to building has never been lower. AI tools and no-code platforms have democratized product development. Anyone can build a chatbot, create a recommendation engine, or deploy a machine learning model.
But here's where this conventional wisdom falls short: easy building has created a validation crisis. When you can build anything in weeks, you skip the hard work of understanding whether anyone actually wants what you're building.
The result? A graveyard of beautifully built AI products that solve problems nobody cared about enough to pay for. I see it everywhere — sophisticated AI platforms with zero users, brilliant technical solutions addressing imaginary market needs, and founders who spent months building before they spent days validating.
The problem isn't the building phase. The problem is treating building as validation.
Consider me as your business complice.
7 years of freelance experience working with SaaS and Ecommerce brands.
So when this client approached me with their marketplace idea, every red flag in my validation framework started flashing. They wanted to "test if their idea works" by building a full platform. This is exactly backwards.
The client was a small team with a solid background in logistics, and they'd identified what seemed like a real problem: connecting specialized service providers with businesses that needed their expertise. Think of it as an "Upwork for industrial consultants." On paper, it made sense.
Their plan was textbook startup methodology: build the platform using AI for smart matching, launch with basic features, get users, iterate based on feedback. They'd budgeted three months and a substantial amount of money. They were ready to move fast.
But when I dug deeper, the validation gaps became obvious:
No audience research: They hadn't talked to potential consultants or businesses about their current solutions. They just assumed people would want a better way to connect.
No demand testing: They hadn't tried to manually broker even one connection between a consultant and a business to see if the value proposition was real.
No competitive analysis: They knew competitors existed but hadn't analyzed why existing solutions were failing or succeeding.
Technology-first thinking: They were excited about AI matching algorithms before they understood if basic matchmaking was even valuable.
This is the classic "solution in search of a problem" trap, made worse by AI hype. The availability of powerful AI tools had convinced them that technological capability equals market opportunity.
Instead of taking their money to build something that might fail, I shared my validation framework with them. What happened next changed how I think about AI product development entirely.
Here's my playbook
What I ended up doing and the results.
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."
Here's the validation playbook I walked them through:
Week 1: Manual Demand Validation
Instead of building a platform, I had them create a simple landing page explaining their value proposition. Not a functional marketplace — just a clear description of what they wanted to do and why it would be valuable. Then we started manual outreach to both sides of their marketplace.
For consultants, they reached out to 50 specialists in their network. The question wasn't "would you use our platform?" but "how do you currently find clients, and what problems do you face?" For businesses, they contacted 30 companies that might need consulting services with a similar approach.
Week 2-4: Manual Matchmaking
Here's where it got interesting. Instead of building matching algorithms, they started manually connecting consultants with businesses via email and WhatsApp. No fancy interface, no AI, no platform — just human-powered matchmaking.
This is where the real validation happened. They discovered that while businesses did need consultants, the problem wasn't discovery — it was trust and verification. Businesses weren't looking for more options; they were looking for confidence that their chosen consultant could deliver.
Month 2: Refining the Real Problem
Through manual operations, they learned that their original AI-matching concept was solving the wrong problem. The valuable service wasn't algorithmic matching based on skills and location. It was vetting, verification, and providing guarantees about consultant quality.
This insight only came from doing the work manually. If they'd built the original platform, they would have spent months optimizing matching algorithms while missing the real value proposition entirely.
Month 3: Testing Willingness to Pay
Only after proving they could manually create value did we test pricing. They started charging a small fee for their vetting and guarantee service. The demand was immediate and clear — businesses would pay for confidence, not just connections.
The lesson became crystal clear: Your MVP should be your marketing and sales process, not your product. Distribution and validation come before development.
Manual First
Start with human-powered processes before automating anything. If you can't do it manually, AI won't magically make it work.
Real Problems Only
Don't assume technological capability equals market need. Validate the underlying problem before building the solution.
Payment Validation
Test willingness to pay before building features. Manual processes can often charge immediately if the value is real.
Iteration Speed
Manual validation lets you pivot in days, not months. Each conversation can change your entire approach.
The results spoke for themselves. By the end of three months, my client had:
Validated actual demand: 15 businesses had paid for their manual vetting service, proving people would pay for their value proposition.
Refined their business model: They pivoted from "AI-powered matching" to "verified consultant marketplace with guarantees" — a much stronger positioning.
Built a waiting list: 40+ consultants and 25+ businesses were ready to join their platform once it launched, because they'd experienced the value firsthand.
Saved development costs: They avoided building the wrong product and spending months on features nobody wanted.
Achieved faster time-to-revenue: They were generating income from month one through manual operations, rather than waiting for a platform to be built.
Most importantly, when they eventually did build their platform (six months later), they built exactly what their validated market wanted. Their AI features supported a proven business model rather than being the business model.
The platform launched with immediate traction because every feature was based on real demand validated through manual operations.
What I've learned and the mistakes I've made.
Sharing so you don't make them.
Here are the key lessons I learned from this validation experience:
Manual first, always. If you can't provide value manually, automation won't fix the fundamental problem. AI should amplify proven processes, not create them.
Technology excitement clouds market reality. The availability of AI tools makes it tempting to build first and validate later. Resist this urge completely.
Real validation requires real money. People saying they "would use" your product is meaningless. People paying for your manual service is everything.
The problem you think you're solving is rarely the problem worth solving. Manual operations reveal the real pain points that matter to customers.
Speed to learning beats speed to building. You can validate core assumptions in weeks through manual processes that would take months to test through product development.
Distribution is harder than development. Proving you can find and serve customers matters more than proving you can build features.
Pivot early, pivot cheaply. Manual validation lets you change direction based on a conversation, not months of development work.
The biggest pitfall in AI product validation is treating building as validation. In today's environment, the constraint isn't "can we build this?" but "should we build this?" Manual validation gives you certainty before you invest in development.
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 products:
Start with manual customer success processes before building AI automation
Test your core value proposition through human-powered services
Validate willingness to pay before building freemium AI features
Use AI to enhance proven workflows, not create unproven ones
For your Ecommerce store
For ecommerce businesses considering AI validation:
Test AI-powered recommendations manually through email campaigns first
Validate AI customer service through manual chat support before automation
Prove AI inventory predictions work through manual forecasting experiments
Build AI features only after manual processes show clear ROI