Growth & Strategy
Personas
SaaS & Startup
Time to ROI
Short-term (< 3 months)
Last year, a potential client approached me with what seemed like a dream project: build a sophisticated AI-powered two-sided marketplace platform. The budget was substantial, the technical challenge was exciting, and it would have been one of my biggest projects to date.
I said no.
This decision shocked them—and honestly, it surprised me too. But here's what I've learned after years of watching AI startups burn through budgets and time: the biggest risk isn't building the wrong thing, it's building anything at all before you know if people want it.
Most founders treating AI product development like traditional software development. They assume that because the technology is powerful, the demand must exist. They skip the fundamental question: "Does anyone actually have the problem I think they have?"
In this playbook, you'll learn:
Why AI projects fail before they even launch (and it's not technical reasons)
The lean validation framework that saved my client thousands of dollars
How to test AI product-market fit in days, not months
When building an AI MVP is worth it (and when it's not)
Real examples of manual validation that proved demand before development
This isn't about avoiding AI—it's about being smart with how you validate AI opportunities. Let's dive into why most AI startups get this backwards.
Industry Reality
What every AI startup founder believes about building first
Walk into any startup accelerator or scroll through Product Hunt, and you'll see the same pattern repeating: AI founders rushing to build sophisticated platforms before they understand their market. The conventional wisdom goes something like this:
"AI is so powerful that once people see what it can do, they'll immediately understand the value."
This leads to a predictable playbook that most AI startups follow:
Start with the technology - Pick an AI capability (LLMs, computer vision, etc.) and build around it
Build the MVP first - Spend 3-6 months creating a functional product with core AI features
Launch and iterate - Release to users and adjust based on feedback
Scale the technology - Add more AI features to increase value
Find product-market fit - Eventually discover the right use case through trial and error
The reasoning seems sound: AI tools like no-code platforms and API integrations have made development faster and cheaper than ever. Why not build first and validate later?
This approach exists because AI feels different from traditional software. The capabilities are so impressive in demos that founders assume the product-market fit will be obvious once users try it. VCs reinforce this by funding "AI-first" companies based on technical capabilities rather than proven demand.
But here's where this conventional wisdom breaks down: AI complexity doesn't equal user value. I've seen countless AI startups with impressive technology that nobody actually wants to pay for. The problem isn't the AI—it's that they never validated whether the AI was solving a real problem worth paying to fix.
Consider me as your business complice.
7 years of freelance experience working with SaaS and Ecommerce brands.
So there I was, sitting across from two enthusiastic founders who had just pitched me their "revolutionary AI marketplace platform." They had grand visions, a solid technical background, and enough budget to build something impressive.
Their excitement was infectious. They'd done their homework on the technical side—they knew exactly which AI models to use, how to handle the data processing, and had even mapped out the entire platform architecture. The project would have been complex but manageable with modern AI tools.
But then they said something that made me pause: "We want to see if our idea is worth pursuing."
That's when I started asking the uncomfortable questions:
"How many potential users have you talked to?"
"What problem are they currently solving this way?"
"How much are they paying for their current solution?"
"Have you tried to solve this manually for even one customer?"
The answers revealed the real problem: They had no existing audience, no validated customer base, and no proof of demand. Just an idea and enthusiasm for the technology.
This reminded me of every other AI project I'd seen fail. Not because the technology didn't work, but because they built solutions to problems that either didn't exist or weren't painful enough for people to pay to solve.
I realized I had to choose: take their money and build something that might fail, or help them avoid an expensive mistake. The decision became clear when I remembered my own experience with AI implementation—the most successful AI projects I'd worked on started with manual validation, not sophisticated technology.
Here's my playbook
What I ended up doing and the results.
Instead of taking their money to build a platform, 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 lean validation framework I recommended:
Step 1: Manual Market Validation (Week 1)
Before writing a single line of code, we needed to prove people had the problem they thought they were solving. I suggested they:
Create a simple landing page explaining their value proposition
Start manual outreach to potential users on both sides of their marketplace
Conduct 20+ customer interviews to understand current pain points
Identify how people currently solve this problem (and what they pay)
Step 2: Manual Service Delivery (Week 2-4)
Instead of building automated systems, I recommended they manually match supply and demand:
Use email, WhatsApp, or Slack to connect buyers and sellers
Handle transactions manually through existing payment systems
Document every friction point and user feedback
Track key metrics: conversion rates, transaction values, user retention
Step 3: Demand Validation Before Automation (Month 2)
Only after proving demand manually would we consider building automation:
Test pricing models with actual transactions
Identify the most valuable features users actually request
Build a waitlist of users eager for the automated solution
Validate unit economics work at scale
The AI Component Strategy
Here's what I learned about incorporating AI into lean validation: Start with the workflow, add intelligence later. Most AI startups do this backwards—they start with impressive AI capabilities and try to find workflows that need them.
Instead, we would:
Prove the manual process creates value
Identify repetitive tasks that could be automated
Add AI to enhance the proven workflow, not replace human judgment
Test AI features with existing users who already see value
This approach flips the traditional "AI-first" model on its head. Instead of building AI and hoping for product-market fit, you find product-market fit first and use AI to scale what already works.
Manual First
Start with human processes before automation to validate real demand and understand user behavior
Constraint Validation
Use limitations as features - manual processes often reveal what users actually value vs. what they say they want
Problem Evidence
Document every friction point during manual delivery - this becomes your product roadmap and competitive moat
Economics Proof
Validate unit economics work manually before adding the complexity and cost of AI automation systems
The founders initially resisted this approach. "But we have the budget to build the full platform," they argued. "Why not just build it and see what happens?"
That's exactly the mindset that kills AI startups. Here's what happened when they followed the lean validation approach:
Week 1 Results: Reality Check
After 50+ customer interviews, they discovered their initial assumption was wrong. The problem they thought was worth $100/month to solve was actually only causing $20/month in pain. Users had workarounds that were "good enough."
Week 3 Pivot: New Opportunity
However, during the interviews, they discovered a related problem that users were paying $500/month to solve poorly. This wasn't their original idea, but the pain was real and quantified.
Month 2 Validation: Proven Demand
By manually delivering the solution to the new problem, they generated $5,000 in revenue with just 10 customers. More importantly, they had a waitlist of 50+ prospects ready to pay for an automated solution.
The total investment? Less than $2,000 in time and basic tools. Compare that to the $30,000+ they would have spent building their original platform idea.
What I've learned and the mistakes I've made.
Sharing so you don't make them.
This experience taught me five critical lessons about AI product-market fit that every founder needs to understand:
AI doesn't create demand, it amplifies existing demand - If people aren't willing to pay for a manual solution, they won't pay for an automated one
Your first MVP should be your marketing and sales process, not your product - Distribution and validation come before development, especially with AI
Manual delivery reveals the real product requirements - You can't design good AI without understanding the human workflow it's replacing
In the age of AI and no-code, the constraint isn't building—it's knowing what to build - Technology is abundant, validated demand is scarce
The best AI products feel like magic because they solve real problems - Not because they showcase impressive technology
The biggest mistake I see AI founders make is treating their product like traditional software. They think because AI is powerful, the value proposition will be obvious. But AI products need even more validation because the complexity can mask whether you're actually solving a valuable problem.
If I had taken that project and built their original platform, we would have created another impressive demo that nobody wanted to pay for. Instead, by forcing them through lean validation, they discovered a better opportunity and built a business, not just a product.
How you can adapt this to your Business
My playbook, condensed for your use case.
For your SaaS / Startup
For SaaS startups exploring AI features:
Survey existing customers about manual tasks they'd pay to automate
Test AI features with your most engaged users first
Start with workflow enhancement, not replacement
Validate willingness to pay premium for AI capabilities
For your Ecommerce store
For ecommerce businesses considering AI tools:
Manually test personalization strategies before automating
Validate customer service automation with human oversight
Test AI recommendations with small customer segments
Ensure AI enhances, not replaces, human customer experience