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.
OK, so why would I turn down a lucrative project? Because they made the same mistake 90% of startups make when they hear about no-code AI tools and platforms like Bubble or Lovable. They think the hard part is building the product. Spoiler alert: it's not.
The client came to me excited about the AI revolution and wanted to "test if their idea works." But here's the thing - if you're truly testing market demand, your MVP should take one day to build, not three months.
In this playbook, you'll learn:
Why most AI MVP projects fail before they even launch
The 1-day validation framework I recommend to all clients
How to validate AI demand without building anything
When to actually start building your AI MVP
Real examples of validation approaches that worked
Check out our AI product-market fit guide and SaaS playbooks for more startup insights.
Market Reality
The Standard AI MVP Approach Everyone Gets Wrong
Here's what every startup accelerator and "build fast" guru will tell you about AI MVP validation:
Build a prototype quickly - Use no-code tools, AI APIs, get something working
Launch to beta users - Find early adopters willing to test
Iterate based on feedback - Improve the product based on user input
Scale when ready - Add more features, get more users
Raise funding - Show traction and get investment
This advice sounds logical, right? And yes, it can work. But there's a fundamental problem: you're optimizing for the wrong thing.
Most founders think the biggest risk is "Can we build this?" Thanks to AI and no-code tools, the answer is almost always yes. You can absolutely build a functional AI MVP in weeks or months.
But the real question should be: "Will people actually pay for this?" And that's where this conventional approach falls short. By the time you've built your AI MVP, you've already invested weeks or months without knowing if there's genuine demand.
The problem gets worse with AI products because they often require significant data preparation, model training, and user education. You might build something technically impressive that nobody wants to use - or worse, that they'll try once and abandon.
Most AI MVPs die not because they're technically flawed, but because they solve problems people don't actually have or aren't willing to pay to solve.
Consider me as your business complice.
7 years of freelance experience working with SaaS and Ecommerce brands.
So here's the story about that marketplace client. They came to me after hearing about AI tools that could build "anything quickly and cheaply." They weren't wrong - technically, you can build complex platforms with modern AI and no-code 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
Now, this wasn't their fault. The current startup narrative around AI makes it seem like the constraint is building, when actually the constraint is knowing what to build and for whom.
I've seen this pattern repeatedly in my consulting work. Clients get excited about AI capabilities and want to build something impressive. But they skip the fundamental question: does anyone actually want this?
The marketplace idea itself was solid - connect supply and demand in a specific niche. But they wanted to spend three months building a complex platform when they could have tested the core hypothesis in days.
This experience reinforced something I now share with every client: In the age of AI and no-code, the constraint isn't building - it's knowing what to build and for whom.
Distribution and validation come before development. Always.
Here's my playbook
What I ended up doing and the results.
Instead of taking their money to build a platform, I gave them what I call the "1-Day Validation Framework." This approach has saved countless clients from building products nobody wants.
Here's exactly what I recommended:
Day 1: Create a Simple Value Proposition Test
Forget about AI for now. Create a simple landing page or Notion doc that explains:
What problem you're solving
Who you're solving it for
How your solution works (conceptually)
A clear call-to-action ("Get early access")
Week 1: Manual Outreach and Discovery
Start reaching out to potential users on both sides of your marketplace:
Identify 20-50 potential supply-side users
Identify 20-50 potential demand-side users
Send personalized messages explaining the concept
Ask specific questions about their current pain points
Week 2-4: Manual Matching Process
Here's where it gets interesting. Instead of building a platform, manually facilitate connections:
Use email, WhatsApp, or Slack to connect supply and demand
Handle payments manually (PayPal, Stripe checkout links)
Document every interaction and feedback point
Track what works and what doesn't
Month 2: Prove Demand Before Building
Only after you've manually facilitated 10-20 successful transactions should you consider building automation. This approach proves:
Real demand exists on both sides
People will actually pay for the service
You understand the friction points
You know what features actually matter
The key insight: Your MVP should be your marketing and sales process, not your product. If you can't validate demand manually, no amount of AI or automation will save you.
For AI-specific validation, I recommend testing the core intelligence manually first. Can you provide the "AI" recommendations yourself? If your AI would suggest certain matches or optimizations, try making those suggestions manually and see if users find them valuable.
This approach works because it focuses on the hardest part first: proving people want what you're building. The technical implementation comes later, when you already know it's worth building.
Manual First
Test core value proposition without any technology - just manual processes to prove demand exists
Specific Validation
Focus on 10-20 real transactions before building anything automated
User Discovery
Talk to actual potential customers, not friends and family who'll be polite about bad ideas
Build Later
Only automate processes you've already proven work manually at small scale
The client I turned down? They initially thought I was crazy. But six months later, they reached out again to thank me.
Instead of building first, they followed the validation framework. They discovered that while their core marketplace idea had merit, their initial target market was completely wrong. The real demand came from a different user segment they hadn't even considered.
By validating manually first, they:
Saved months of development time
Discovered the real market opportunity
Built genuine relationships with early customers
Had paying customers before building the platform
This experience taught me that in today's build-fast culture, the most contrarian advice is often "don't build yet." The companies that succeed are usually the ones that validate demand first, then build the minimum technology needed to serve that demand.
For AI products specifically, this approach is even more critical because AI adds complexity that can mask fundamental product-market fit issues.
What I've learned and the mistakes I've made.
Sharing so you don't make them.
Here are the key lessons learned from applying this validation approach across multiple AI projects:
Technology is never the constraint anymore - With AI APIs and no-code tools, you can build almost anything. The real challenge is knowing what to build.
Manual validation scales better than you think - You can manually serve 50-100 customers while learning what they actually need.
Paying customers beat excited prospects every time - Someone saying "this is cool" means nothing. Someone paying for it means everything.
AI features should solve proven problems - Don't add AI because it's trendy. Add it because it solves a specific problem you've validated.
Distribution beats product quality - A mediocre product with great distribution will outperform a perfect product nobody knows about.
Your first customers are your best teachers - They'll tell you exactly what to build if you listen carefully.
Timing matters more than technology - Building too early is just as bad as building too late.
The biggest mistake I see founders make is treating validation as a checkbox rather than an ongoing process. Validation never stops - it just evolves from "do people want this?" to "how can we serve them better?"
Remember: your goal isn't to build an AI MVP. Your goal is to build a sustainable business that happens to use AI.
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:
Test AI recommendations manually before automating
Validate user workflows with simple tools first
Focus on solving specific user problems, not showcasing AI capabilities
Use human-in-the-loop approaches for initial validation
For your Ecommerce store
For ecommerce stores considering AI features:
Test recommendation logic with manual product suggestions
Validate demand for personalization through customer surveys
Start with simple automation before advanced AI features
Measure impact on conversion rates, not just engagement metrics