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.
Now, before you think I've lost my mind, let me explain. This wasn't about the money or the complexity. It was about what I've learned after years of watching startups burn through cash building the wrong thing at the wrong time.
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-powered platform with these tools. But their core statement revealed the real problem: "We want to see if our idea is worth pursuing."
That single sentence told me everything I needed to know about why their approach was backwards. Here's what you'll learn from this experience:
Why most AI MVPs are built for the wrong reasons
The fundamental difference between testing an idea and building a product
My framework for when AI actually belongs in an MVP
A step-by-step approach to validate AI product ideas without coding
Real examples of AI MVPs that worked (and why)
This isn't another "build fast, fail fast" sermon. This is about the hard truth I've learned: in the age of AI and no-code, the constraint isn't building—it's knowing what to build and for whom.
Industry Reality
What the startup world preaches about AI MVPs
Walk into any startup accelerator or scroll through Product Hunt, and you'll hear the same advice repeated like a mantra: "Build an AI MVP quickly, test it with users, and iterate based on feedback." The logic seems sound—use AI to prototype faster, validate your assumptions, then scale what works.
Here's what everyone's saying you should do:
Start with AI as the core differentiator - "AI will make your product 10x better than competitors"
Build a functional prototype ASAP - "Get something working in weeks, not months"
Focus on technical feasibility - "Prove the AI can work, then find users"
Use no-code tools to move fast - "Anyone can build an AI product now"
Launch and learn from user feedback - "Users will tell you what they want"
This conventional wisdom exists because it worked during the mobile app boom and the SaaS explosion. The pattern was clear: identify a problem, build a solution, find product-market fit, then scale. AI feels like the next obvious evolution of this playbook.
The problem is that AI isn't just another feature you can bolt onto an existing business model. It's not like adding a search function or a mobile app. AI fundamentally changes how value is created and delivered, which means the traditional MVP validation process often misses the point entirely.
Most founders get excited about the AI capabilities without understanding the market dynamics that make AI valuable. They build sophisticated models that can do impressive things, then struggle to find people who actually need those things done. The result? Impressive demos that nobody wants to pay for.
This approach puts the cart before the horse. You're optimizing for technical achievement when you should be optimizing for market validation.
Consider me as your business complice.
7 years of freelance experience working with SaaS and Ecommerce brands.
When this client approached me, they had everything that looks like a solid AI MVP foundation. A compelling vision for a two-sided marketplace, a clear understanding of their target market's pain points, and enough budget to build something substantial.
But as we dug deeper into their strategy, red flags started appearing. They had no existing audience, no validated customer base, and no proof of demand. Just an idea and enthusiasm for AI's potential to "disrupt" their chosen industry.
Their plan was textbook startup advice: spend three months building an AI-powered platform, launch it, and see if people used it. If it worked, they'd invest more. If it didn't, they'd pivot or shut down.
Here's what I told them during our strategy call that changed everything: "If you're truly testing market demand, your MVP should take one day to build—not three months."
The silence on that Zoom call was telling. They'd been so focused on what they could build with AI that they'd never questioned whether they should build it at all.
I explained that their real challenge wasn't technical—it was market validation. They didn't need to prove that AI could power their marketplace (it obviously could). They needed to prove that people actually wanted what they were planning to sell.
This is where most AI MVPs go wrong. Founders get seduced by the technology's capabilities and skip the fundamental question: "Does this solve a problem people are actively trying to solve, and are they willing to pay for it?"
Instead of building their platform, I recommended starting with something much simpler: manual validation of their core hypothesis. Test whether the market existed first, then figure out how AI could serve that market better.
Here's my playbook
What I ended up doing and the results.
Here's exactly what I recommended to that client, and what I now use with every AI startup that approaches me:
Step 1: Manual Market Validation (Week 1)
Instead of building anything, start with a simple landing page or Notion document explaining your value proposition. Your goal isn't to collect signups—it's to test if people understand and care about what you're offering.
Create three versions of your value proposition and test them with potential users. Don't mention AI at all. Focus entirely on the outcome you're promising to deliver. If people don't care about the outcome, they won't care about how cleverly you deliver it.
Step 2: Manual Process Simulation (Week 2-4)
This is the part that saves you months of development time. Instead of building your AI-powered solution, manually deliver the service to a small group of early adopters.
For the marketplace client, this meant manually matching supply and demand via email and WhatsApp. No algorithms, no automation, no AI. Just human intelligence solving the core problem their AI was supposed to address.
The goal is to understand the complete user journey, identify friction points, and validate that people will actually change their behavior to use your solution. Most AI MVPs fail not because the AI doesn't work, but because users don't want to change how they currently solve their problems.
Step 3: Pattern Recognition and AI Opportunity Mapping
Only after proving market demand do you start thinking about where AI adds genuine value. Look for patterns in your manual process:
Which tasks are repetitive and time-consuming?
Where do users need personalized recommendations?
What decisions require processing large amounts of data?
Which processes could benefit from predictive capabilities?
This approach flips the traditional AI MVP process. Instead of starting with "What can AI do?" you start with "What do users actually need?" Then you identify where AI creates the biggest improvement over manual processes.
Step 4: Minimum AI Implementation
When you do start building, focus on the smallest possible AI implementation that delivers disproportionate value. This might be a simple recommendation engine, basic automation, or pattern recognition—not a full-scale intelligent platform.
The key insight from my experience: users don't care about your AI capabilities. They care about outcomes. A simple automation that saves them 30 minutes daily is more valuable than a sophisticated model that improves accuracy by 5%.
Build your AI MVP around user workflows, not AI capabilities. Start with tools like Bubble for rapid prototyping, but focus on solving real problems rather than showcasing technical prowess.
Market Validation
Prove demand exists before building anything. Most AI MVPs fail because founders fall in love with technology rather than customer problems.
Process Testing
Manually deliver your service first. This reveals user behavior patterns and friction points that no amount of user research can uncover.
AI Integration
Only add AI where it creates genuine value over manual processes. Users pay for outcomes, not impressive technology.
Iteration Framework
Build the smallest possible AI feature that delivers disproportionate value. Perfect the core workflow before adding complexity.
The client I turned down initially felt frustrated by my recommendation. They wanted to build something impressive, not run manual experiments. But three months later, they sent me an update that changed their perspective entirely.
They'd followed the validation framework and discovered something crucial: their original marketplace idea solved a problem that only existed in their imagination. But the manual validation process revealed a different, much larger opportunity they'd never considered.
Instead of building a complex two-sided marketplace, they pivoted to a simple AI-powered tool that automated one specific workflow their target users struggled with daily. The manual validation process didn't just save them from building the wrong thing—it helped them discover the right thing.
Six months after our initial conversation, they had paying customers and clear product-market fit. More importantly, they understood exactly where AI added value and where it was just unnecessary complexity.
This pattern repeats with every AI startup I work with. The ones that succeed start with market validation, not technology validation. They build AI MVPs that solve real problems, not AI MVPs that showcase interesting capabilities.
The validation-first approach consistently leads to better outcomes because it forces you to understand your users before you understand your technology. In the AI era, market insight is more valuable than technical capability.
What I've learned and the mistakes I've made.
Sharing so you don't make them.
Question your assumptions before you code - Most AI MVP failures happen because founders build solutions to problems that don't exist or aren't painful enough to pay for
Manual validation reveals hidden insights - Running your service manually for a few weeks teaches you more about user behavior than months of user interviews
AI should amplify solutions, not create them - The best AI MVPs start with proven manual processes and use AI to make them faster, cheaper, or more accurate
Users care about outcomes, not technology - A simple automation that saves 30 minutes daily beats a sophisticated model that improves accuracy by 5%
Start small, then scale intelligently - Build the minimum AI feature that delivers maximum value, then expand based on user feedback
Market timing matters more than technical capability - Even perfect AI is worthless if users aren't ready to change their behavior
Budget constraints force better decisions - The client with unlimited budget often builds unnecessarily complex solutions while bootstrapped founders focus on core value
The biggest lesson? An AI MVP should validate market demand, not technical feasibility. In 2025, we know AI works. The question is whether anyone wants what you're building with it.
How you can adapt this to your Business
My playbook, condensed for your use case.
For your SaaS / Startup
For SaaS startups building AI MVPs:
Focus on workflow automation over feature complexity
Start with rule-based systems before machine learning
Test with 10 manual users before building AI features
Measure time saved, not technical metrics
For your Ecommerce store
For ecommerce businesses exploring AI MVPs:
Begin with personalization rather than prediction
Test recommendation logic manually first
Focus on conversion lift over engagement metrics
Start with existing customer data before external sources