Growth & Strategy
Personas
SaaS & Startup
Time to ROI
Short-term (< 3 months)
Last year, a potential client approached me with what seemed like every developer's dream: build a sophisticated two-sided marketplace platform powered by AI. The budget was substantial, the technical challenge was fascinating, and with tools like Lovable and new AI APIs, it would have been a flagship project.
I said no.
Not because I couldn't deliver. The technology exists to build complex AI platforms faster than ever. But their core statement revealed a fundamental problem: "We want to test if our AI idea works."
They had no existing audience, no validated customer base, no proof of demand. Just an idea and enthusiasm for AI technology.
This conversation taught me something crucial about AI startup validation that most founders are getting completely wrong in 2025. While everyone's obsessing over which AI models to use or which no-code platform to choose, they're missing the most important question: does anyone actually want what you're building?
Here's what you'll discover in this playbook:
Why your AI MVP should take one day, not three months
The manual validation framework I recommended instead of building
How to prove AI product-market fit before writing a single line of code
My 4-phase validation system for AI startups
When to graduate from validation to actual AI development
This isn't anti-technology advice. This is about building AI products that people actually want to use.
Market Reality
What the AI startup bubble teaches us
The current AI startup landscape is obsessed with technology-first thinking. Accelerators push founders to "leverage cutting-edge AI models." VCs ask "What's your AI differentiation?" Product Hunt celebrates the most technically impressive demos.
The standard playbook looks like this:
Choose your AI stack - GPT-4, Claude, or the latest model
Pick a development platform - Bubble, Webflow, or custom code
Build an impressive demo - Something that shows off AI capabilities
Launch and hope for adoption - Assume the technology will create demand
Iterate based on usage metrics - Fix technical issues, not market fit
This approach exists because AI makes building feel productive. You can prototype features quickly, generate impressive demos, and show tangible progress. It's easier to focus on what you can control (technology) than what you can't (market demand).
The problem? Building the wrong thing perfectly is still building the wrong thing. Most AI startups fail not because their technology doesn't work, but because they solved problems that people either didn't have or weren't willing to pay to solve.
The conventional wisdom assumes that if you build something technically impressive, users will find value in it. But AI capabilities don't automatically translate to user value. The market is littered with technically perfect AI products that nobody uses.
Consider me as your business complice.
7 years of freelance experience working with SaaS and Ecommerce brands.
When this client approached me, I recognized the pattern immediately. They were excited about the solution but couldn't articulate the problem clearly.
Here's what they told me: "We want to build an AI-powered two-sided marketplace that connects X with Y. We think AI can make the matching process more efficient."
The red flags were obvious:
"We think" instead of "we know"
No existing relationships with either side of the marketplace
No evidence that current matching methods were inadequate
Focus on AI efficiency rather than user outcomes
They had fallen into what I call the "AI-first trap" - starting with the technology and working backward to find problems it could solve, rather than starting with real problems and determining if AI was the best solution.
The conversation that followed changed how I approach AI startup validation entirely.
I told them: "If you're truly testing market demand, your MVP should take one day to build, not three months."
Their response? "But we need to show the AI capabilities to get users interested."
That's when I knew they were approaching this completely backward. They were assuming AI features would create demand, rather than proving demand existed first and then building the minimum AI necessary to serve it.
Here's my playbook
What I ended up doing and the results.
Instead of building their platform, I walked them through what I call the "Wizard of Oz AI Validation Framework." The goal: prove people want your solution before you build any AI at all.
Phase 1: The Manual MVP (Day 1)
I had them create a simple landing page explaining their value proposition - but without mentioning AI at all. The focus was purely on the outcome: "Get matched with qualified [X] in under 24 hours."
Behind the scenes, they would handle all matching manually. No algorithms, no machine learning, just good old-fashioned human research and networking.
Phase 2: The Demand Test (Week 1)
Next, we targeted potential users on both sides of their marketplace through direct outreach. Not to sell AI capabilities, but to offer the core service manually.
For suppliers: "We're building a curated network of [Y]. Would you be interested in qualified leads?"
For buyers: "We can connect you with vetted [X] providers. What specific criteria matter most to you?"
Phase 3: The Value Validation (Week 2-4)
With manual processes, they facilitated actual matches between real users. This phase revealed:
What criteria actually mattered for matching (not what they assumed)
Which side of the marketplace was harder to acquire
What price point people would actually pay
Where the real friction points existed in the process
Phase 4: The AI Assessment (Month 2)
Only after proving manual demand did we evaluate where AI could add value:
Which manual processes were time-intensive but could be automated?
What data did they now have to train AI models effectively?
Where would AI improvements directly impact user satisfaction?
What was the minimum AI implementation needed to scale their proven model?
The breakthrough insight: AI should amplify your proven manual process, not replace an unproven hypothesis.
Manual First
Start with human processes before automation to understand what actually needs to be optimized
Demand Validation
Prove people will pay for outcomes before building the technology to deliver them
AI Assessment
Only add AI where it clearly improves your already-validated manual process
Minimum Viable AI
Build the smallest AI implementation that meaningfully improves user experience
The results spoke for themselves. After 30 days of manual validation:
They discovered their original AI matching idea was solving the wrong problem. Users didn't care about "more efficient matching" - they cared about "higher quality matches." The manual process revealed that relationship quality, not speed, was the key differentiator.
They found a different, simpler AI application. Instead of complex matching algorithms, they realized AI could better serve them by analyzing successful match patterns and suggesting improvements to their manual curation process.
They generated revenue before building anything complex. The manual MVP brought in their first $3,200 in revenue, proving demand existed and giving them runway to make better technology decisions.
They saved months of development time. What would have been a 3-month platform build became a 2-week validation experiment, followed by targeted AI implementations only where proven necessary.
Most importantly, they built confidence in their market understanding before making significant technology investments. When they finally did build AI features, they knew exactly which problems needed solving and had real user data to guide development.
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 startup validation:
1. Technology-first thinking is expensive validation
Building AI features to test market demand is like hiring a Formula 1 driver to see if people want transportation. You're optimizing the wrong variable.
2. Manual processes reveal user priorities
When you handle everything manually, you quickly discover what users actually value versus what you assume they want. This insight is impossible to get from automated systems.
3. AI works best as an amplifier, not a creator
AI should make your proven processes better, not create processes from scratch. Start with manual, prove it works, then automate intelligently.
4. Real validation requires real money changing hands
People will say they want almost anything in a survey. They'll only pay for things they actually value. Manual validation forces this reality check early.
5. The constraint isn't building, it's knowing what to build
In 2025, you can build almost anything with AI tools. The hard part is building something people want to use and pay for.
6. Time-to-insight beats time-to-market
Getting market insights in 30 days beats launching the wrong product in 90 days, even if your competitors ship first.
7. Manual validation creates better AI training data
When you finally do build AI features, you'll have real user behavior data instead of synthetic training data. This leads to more effective AI implementations.
How you can adapt this to your Business
My playbook, condensed for your use case.
For your SaaS / Startup
For SaaS startups considering AI features:
Start manual: Offer your core service with human processes first
Measure outcomes: Track user satisfaction and retention, not just AI accuracy
Identify bottlenecks: Only automate manual processes that limit your ability to scale
Build incrementally: Add one AI feature at a time, measuring impact before adding more
For your Ecommerce store
For e-commerce brands exploring AI tools:
Manual curation first: Hand-pick product recommendations before building recommendation engines
Test personalization manually: Create custom experiences for key customers before automating
Focus on conversion impact: Only implement AI features that demonstrably improve sales metrics
Start with existing data: Use your current customer behavior to guide AI implementation priorities