Growth & Strategy
Personas
SaaS & Startup
Time to ROI
Medium-term (3-6 months)
Last year, a potential client approached me with an exciting opportunity: build a two-sided marketplace platform powered by AI features. The budget was substantial, the technical challenge was interesting, and it would have been one of my biggest projects to date.
I said no.
Not because the project was bad, but because of one red flag statement: "We want to test if our AI idea works." They had no existing audience, no validated customer base, and no proof of demand. Just enthusiasm and a belief that AI would solve everything.
This experience taught me something crucial: AI doesn't automatically create market fit - it amplifies what already exists. Most founders are so focused on building cool AI features that they skip the fundamental question: does anyone actually need this?
After turning down that project, I developed a systematic checklist to help founders validate AI solutions before building them. It's saved multiple clients from expensive dead ends and helped others pivot to actual market opportunities.
Here's what you'll learn from my AI solution market fit framework:
Why most AI startups fail at validation (and it's not technical issues)
The 4-layer checklist I use to evaluate AI market fit
Real examples of AI solutions that passed vs failed the test
How to validate AI demand before writing a single line of code
The difference between AI hype and genuine market need
This isn't theory - it's a practical framework born from saying "no" to the wrong opportunities and "yes" to the right ones. Let's dive into what actually works.
Market Reality
What every AI founder believes about validation
If you've been in the AI space for more than five minutes, you've probably heard the standard startup advice applied to AI solutions. The conventional wisdom goes something like this:
"Build an MVP, get user feedback, iterate quickly." Sounds logical, right? But here's what most AI advisors won't tell you: this approach is fundamentally broken for AI solutions.
The typical AI validation playbook suggests:
Start with the technology - Pick your AI model, build the core functionality, then find users
Demo-driven validation - Show people cool AI features and ask if they'd use it
Feature-first thinking - Lead with what your AI can do, not what problems it solves
Technical metrics first - Focus on accuracy, speed, and model performance
"AI is the product" - Position AI as the main value proposition
This conventional wisdom exists because most AI content is written by technical people who understand algorithms better than business models. They assume that if you build something technically impressive, customers will automatically want it.
But here's where this falls apart in practice: customers don't buy AI - they buy solutions to their problems. They don't care about your neural network architecture. They care about whether you can solve their specific pain point better than existing alternatives.
The result? Hundreds of technically brilliant AI solutions that nobody actually uses. I've seen founders spend months perfecting their AI models while never validating whether anyone has the problem they're solving.
That's why I developed a completely different approach to AI market fit validation.
Consider me as your business complice.
7 years of freelance experience working with SaaS and Ecommerce brands.
The turning point came during that platform project consultation. The founders were brilliant - PhD-level AI expertise, solid technical vision, and genuine passion for solving marketplace inefficiencies. But when I asked them three simple questions, everything fell apart:
"Who have you talked to that has this problem?" - Silence.
"How are people solving this today?" - Vague gestures toward existing platforms.
"Would you pay for this solution yourself?" - "Well, we're not the target customer..."
Right there, I knew this project was heading for disaster. Not because the AI wouldn't work - it probably would have been technically impressive. But because they were building a solution in search of a problem.
This wasn't my first rodeo with AI validation failures. I'd watched several other clients make similar mistakes:
One client spent six months building an AI-powered content optimization tool that could improve blog engagement by 15-20%. Sounds great, right? Except when we finally tested it with real users, we discovered that content creators cared more about speed and simplicity than optimization. They were already happy with "good enough" content that published quickly.
Another built an AI scheduling assistant that could optimize meeting times across multiple calendars and time zones. Technically brilliant, but it required everyone in the meeting to use their platform - a coordination problem they never considered.
The pattern was always the same: impressive technology, no genuine market need. These founders were solving problems they assumed existed rather than problems they'd validated through real user research.
That's when I realized the AI validation process needed to be completely different from traditional startup validation. AI solutions have unique challenges - they're often "black boxes" to users, they require more trust, and they usually replace existing human workflows. Standard MVP validation doesn't account for these factors.
So instead of taking on projects that were likely to fail, I started developing a systematic way to evaluate AI market fit before anyone writes code.
Here's my playbook
What I ended up doing and the results.
After turning down that platform project, I spent the next three months developing what I call the AI Solution Market Fit Checklist. It's a four-layer validation framework that I now use with every potential AI client.
Here's the framework that's saved multiple projects from expensive failures:
Layer 1: Problem Validation (Before Any AI Discussion)
I start by completely ignoring the AI component. Instead, I focus on the underlying problem:
Can you describe the problem without mentioning AI or technology?
How are people solving this today? (And don't say "manually" - get specific)
What does the current solution cost in time/money/frustration?
How often does this problem occur?
If founders can't clearly articulate the problem without mentioning their AI solution, that's a red flag. The best AI solutions solve problems that existed long before AI was available.
Layer 2: AI Necessity Test
Next, I evaluate whether AI is actually necessary:
Could this be solved with simple automation or existing tools?
What makes AI uniquely suited for this problem?
What's the minimum viable AI that would provide value?
How much accuracy/sophistication do users actually need?
Many "AI" solutions are actually automation problems dressed up in ML clothing. True AI solutions require pattern recognition, prediction, or decision-making that simple rules can't handle.
Layer 3: Trust and Adoption Barriers
This is where most AI validation fails. I evaluate the human factors:
How much trust does your solution require from users?
What existing workflow would this replace?
Who gets blamed if the AI makes a mistake?
How will users know if the AI is working correctly?
AI solutions often fail not because the technology doesn't work, but because humans don't trust or understand it. This layer identifies adoption barriers early.
Layer 4: Market Timing and Readiness
Finally, I assess whether the market is ready for an AI solution:
Are competitors using AI for similar problems?
How AI-savvy is your target market?
What's the learning curve for adoption?
Is this a vitamin or a painkiller?
I walk through each layer systematically, scoring responses and identifying gaps. Only solutions that pass all four layers get my recommendation to move forward.
The key insight: AI market fit isn't just about having good technology - it's about having technology that solves a validated problem in a way users can understand and adopt.
Problem Validation
Start with the problem not the technology - if you can't explain the pain point without mentioning AI you're building a solution looking for a problem
AI Necessity
Don't use AI because it's trendy - use it because it's the only viable solution to your specific problem type
Adoption Barriers
Trust is your biggest competitor - users need to understand how and when your AI works or they'll never adopt it consistently
Market Readiness
Timing matters - even perfect AI solutions fail if the market isn't ready to understand and pay for intelligent automation
Using this framework, I've evaluated over a dozen AI solution concepts in the past year. Here's what the data shows:
Pass Rate: Only 3 out of 12 AI concepts passed the full checklist. Most failed at Layer 1 (problem validation) or Layer 3 (adoption barriers). This might seem harsh, but it's actually good news - we caught potential failures before expensive development.
The solutions that passed the checklist showed common characteristics:
Clear, existing pain points that people were already trying to solve
High-frequency problems where small improvements created significant value
Transparent AI functionality where users could understand and verify the output
Low-risk use cases where mistakes were recoverable
More importantly, this framework saved clients an estimated $200,000+ in development costs by identifying doomed projects early. The three solutions that passed the checklist went on to successful launches and are now generating revenue.
One client pivoted from an AI-powered "smart scheduling" tool to a simple calendar analytics dashboard - and found much stronger market demand for the simpler solution.
What I've learned and the mistakes I've made.
Sharing so you don't make them.
Here are the key lessons from applying this checklist across multiple AI projects:
AI hype masks fundamental business problems - Most AI solutions fail because they solve non-existent problems, not because the AI doesn't work
Users buy outcomes, not technology - Lead with what you solve, not how smart your algorithms are
Trust is harder to build than accuracy - A 95% accurate AI that users don't trust is worthless
Start stupidly simple - The minimum viable AI is usually much simpler than you think
Market timing is everything - Even great AI solutions can be too early for their market
Manual validation comes first - Prove people want the solution by doing it manually before automating with AI
Adoption > Innovation - A simple AI solution that people actually use beats a sophisticated one that sits unused
The biggest mistake I see founders make is treating AI validation like traditional startup validation. AI solutions have unique challenges around trust, explainability, and workflow integration that require specialized evaluation frameworks.
Before building any AI solution, use this checklist ruthlessly. It's better to kill a bad idea early than to spend months building something nobody wants.
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:
Validate the core problem exists in your existing user base first
Start with rule-based automation before adding AI complexity
Make AI optional - never force users to depend on it
Focus on high-frequency, low-risk use cases initially
For your Ecommerce store
For E-commerce stores considering AI:
Prioritize recommendation accuracy over sophistication
Test with simple A/B experiments before building complex AI
Ensure AI improves existing metrics like conversion rate
Keep fallback options when AI recommendations aren't available