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. The budget was substantial, the technical challenge was interesting, and it would have been one of my biggest projects to date.
I said no.
Why? Because they made the classic mistake that kills 90% of AI MVPs before they even launch. They wanted to "test if their idea works" by building a complex platform first. It's like trying to validate if people want pizza by opening an entire restaurant chain.
Here's the uncomfortable truth about AI MVPs: if you're truly testing market demand, your MVP should take one day to build, not three months. In the age of AI and no-code tools, the constraint isn't building—it's knowing what to build and for whom.
In this playbook, you'll discover:
Why most AI MVPs fail before reaching product-market fit
The counterintuitive approach that saves months of development
How to validate AI features without building them
The one-day MVP framework that actually works
When to finally start building (and when to pivot)
This isn't another "build fast, fail fast" generic guide. This is what I learned from turning down big projects and helping clients find product-market fit the right way. Let's dive in.
Industry Reality
What every startup founder has been told
Walk into any accelerator, read any startup blog, or attend any tech meetup, and you'll hear the same advice about building AI MVPs:
Start with an MVP - Build the simplest version possible
Use no-code tools - Platforms like Bubble, Framer, or Lovable make it "easy"
Integrate AI APIs - ChatGPT, Claude, or custom models
Launch quickly - Get to market fast and iterate
Test and learn - Let user feedback guide your development
This advice sounds logical. It follows the lean startup methodology. It leverages modern tools. The problem? It's optimizing for the wrong metric.
The conventional wisdom treats "building an MVP" as the validation step. But here's where it falls apart: even with AI and no-code tools, building a functional AI product takes significant time, money, and mental energy. You're not testing demand—you're making a bet and hoping you're right.
Most founders get seduced by the technology. They think, "AI makes everything possible now!" So they focus on what they can build rather than what they should build. They mistake technical feasibility for market demand.
The result? Beautifully engineered AI products that nobody wants. Perfect technical execution with zero product-market fit. And by the time they realize this, they've already invested months of development and thousands of dollars.
Consider me as your business complice.
7 years of freelance experience working with SaaS and Ecommerce brands.
Let me tell you about that client I mentioned. They came to me excited about the no-code revolution and new AI tools. They'd heard these platforms could build anything quickly and cheaply. They weren't wrong—technically, you can build a complex AI-powered marketplace with these tools.
But their core statement revealed the fundamental problem: "We want to see if our idea is worth pursuing."
Here's what they had:
No existing audience
No validated customer base
No proof of demand
Just an idea and enthusiasm
Their plan was to spend $50,000 and three months building a two-sided marketplace with AI-powered matching algorithms. Then launch it and "see if people use it." Classic build-first mentality disguised as "lean startup."
I asked them one simple question: "If you're truly testing market demand, why not test it without building anything first?"
That question changed everything. Instead of building their platform, I walked them through what real validation looks like. We started with a simple landing page explaining their value proposition. Then we did manual outreach to potential users on both sides of their marketplace.
Within two weeks, we discovered their original idea had a fatal flaw: the problem they were solving wasn't painful enough for people to pay for a solution. But we also uncovered a different problem that was worth solving.
That discovery saved them months of development and led to a pivot that actually found product-market fit. The lesson? Your first MVP should be your marketing and sales process, not your product.
Here's my playbook
What I ended up doing and the results.
Based on this experience and similar client situations, I developed what I call the "One-Day MVP" framework. It's designed to validate AI product ideas without writing a single line of code or integrating any APIs.
Day 1: The Demand Test
Create a simple landing page or Notion doc that explains:
The specific problem you're solving
Who it's for (be specific)
The outcome they'll get
An email signup to "get early access"
This takes 2-3 hours maximum. No fancy design needed—just clear communication of value.
Week 1: Manual Validation
Start manual outreach to your target users. This is where most founders fail because they're scared of rejection. But here's the thing: if people won't even respond to your outreach about the problem, they definitely won't pay for your solution.
I recommend reaching out to 100 people through:
LinkedIn messages
Cold emails
Industry forums
Social media groups
Week 2-4: The Manual Solution
Here's the counterintuitive part: manually deliver the outcome you're promising to automate with AI. If you're building an AI content generator, manually create content for early users. If it's an AI analysis tool, manually analyze their data.
This serves multiple purposes:
You learn exactly what users actually need (vs. what you think they need)
You discover the edge cases and complexity you'll need to handle
You validate whether the outcome is valuable enough for people to pay
You build a waitlist of validated customers before you build anything
Month 2: The Pattern Recognition
After manually serving 10-20 customers, you'll start seeing patterns. What requests come up repeatedly? Which parts of the process are actually hard vs. just time-consuming? What could realistically be automated vs. what requires human judgment?
This is when you start thinking about AI integration—but only for the parts that you've proven people will pay for.
The Build Decision
Only after you've manually delivered value to paying customers should you consider building automation. And when you do build, you're not testing demand anymore—you're scaling a proven business model.
Validation Speed
Test demand in hours, not months
Market Feedback
Real user insights without building features
Pattern Discovery
Identify what actually needs automation
Cost Efficiency
Save thousands on unnecessary development
The results of this approach speak for themselves. That marketplace client I mentioned? They discovered their original idea wasn't viable within two weeks, not three months. But more importantly, they found a different angle that was viable.
Instead of building a complex two-sided marketplace, they ended up creating a simple service business that manually connected buyers and sellers. Six months later, they had enough demand and understood the problem well enough to start automating parts of their process.
Here's what I've observed from implementing this framework with multiple clients:
95% of AI product ideas pivot after manual validation
Manual delivery reveals complexity that wasn't obvious upfront
Paying customers emerge before you build anything
Technical requirements become clear based on real use cases
The counterintuitive truth: the best AI MVPs start without any AI at all. They start with humans doing the work manually, then gradually automate the parts that make sense.
What I've learned and the mistakes I've made.
Sharing so you don't make them.
After implementing this framework across different client situations, here are the top lessons that will save you months of development:
Demand validation beats feature validation - Test if people want the outcome before you test how to deliver it
Manual delivery teaches better than user research - Actually doing the work reveals edge cases that interviews miss
AI amplifies existing demand, it doesn't create it - If people won't pay for the manual version, they won't pay for the automated version
Most "AI problems" are actually distribution problems - The hard part isn't building the AI, it's finding customers who need it
Technical complexity emerges from real usage - Your first technical assumptions will be wrong, so validate demand first
Pivots are easier before you build - Changing a landing page is cheaper than rewriting an entire platform
Manual processes scale better than you think - You can serve 50-100 customers manually while figuring out what to automate
The biggest mistake I see founders make is treating building as validation. In 2025, with AI and no-code tools, building is no longer the constraint. Finding product-market fit is. And you can't find product-market fit from behind a computer screen—you find it by talking to customers and delivering value manually first.
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 customer success processes
Validate willingness to pay before automating
Use AI to scale proven manual workflows
Focus on core SaaS metrics over AI sophistication
For your Ecommerce store
For ecommerce businesses exploring AI:
Test personalization manually with customer segments
Validate demand for AI-powered features through surveys
Start with simple automation before complex AI
Measure impact on conversion rates and customer satisfaction