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 with 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.
Here's the thing - they 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. But their core statement revealed the fundamental problem with AI market validation: "We want to see if our idea is worth pursuing."
After spending 6 months deliberately diving into AI implementation across multiple client projects - from content automation to e-commerce optimization - I've discovered why most AI market validation approaches fail spectacularly.
In this playbook, you'll learn:
Why building AI features to "test market demand" is backwards thinking
The 3 critical validation steps that happen BEFORE any AI development
How to distinguish between AI hype and genuine market need
My framework for validating AI solutions that actually work
Real examples from projects where AI validation worked (and where it didn't)
Reality Check
What the AI industry won't tell you about validation
The AI industry has created a dangerous narrative around market validation. Everywhere you look, the message is the same: build fast, test quickly, iterate with AI tools, and the market will tell you what works.
VCs are pushing this story because it fits their portfolio strategy. AI tool companies promote it because it sells subscriptions. Consultants love it because it creates more projects. Here's what they typically recommend:
Start with AI-first solutions: "AI is transformative, so build AI features and users will come"
Use no-code AI tools to prototype fast: "You can validate in weeks, not months"
A/B test different AI features: "Let the data tell you what users want"
Pivot quickly based on usage metrics: "AI makes iteration cheap and fast"
Scale with AI automation: "Once you find product-market fit, AI will handle the rest"
This conventional wisdom exists because it sounds logical and aligns with how we think about traditional software validation. The problem? AI introduces complexity that breaks traditional validation models.
AI isn't just another feature - it's a different way of solving problems. When you validate AI solutions the same way you'd validate a simple web app, you're measuring the wrong things. You end up optimizing for engagement with AI features instead of solving real human problems.
The result? Founders spend months building sophisticated AI tools that nobody actually needs, then wonder why their "validated" product fails in the market.
Consider me as your business complice.
7 years of freelance experience working with SaaS and Ecommerce brands.
The client I mentioned earlier had no existing audience, no validated customer base, and no proof of demand. Just an idea and enthusiasm for AI capabilities. They wanted to build a sophisticated matching platform using machine learning algorithms.
Instead of accepting their project, I shared something that initially shocked them: "If you're truly testing market demand, your MVP should take one day to build - not three months."
This wasn't just theoretical advice. Over the past 6 months, I'd been running my own AI validation experiments. I'd implemented AI-powered content generation for a B2C Shopify client, generating 20,000+ SEO articles across 4 languages. I'd built AI workflows for categorizing 1,000+ products automatically. I'd even created AI-driven email automation that doubled response rates for abandoned cart recovery.
But here's what I learned from those successes: every AI implementation that worked solved a problem that already existed without AI.
My Shopify client wasn't struggling with AI - they were struggling with SEO at scale. The AI became the solution, not the starting point. The abandoned cart email automation worked because customers were already abandoning carts, not because they wanted smarter emails.
The marketplace client, however, was starting with AI. They believed machine learning would create demand for their platform. That's backwards validation - hoping technology will create a market instead of using technology to serve an existing market.
When I dug deeper into their assumptions, the cracks became obvious. They couldn't answer basic questions: Who specifically has this problem today? How are they solving it now? What would make them switch? These aren't AI questions - they're fundamental market validation questions.
Here's my playbook
What I ended up doing and the results.
After testing AI implementations across multiple projects and seeing both spectacular successes and expensive failures, I developed a framework that separates AI hype from genuine market opportunity.
Step 1: Validate the Problem (Without AI)
Before touching any AI tool, I now require clients to prove demand manually. For the marketplace client, I recommended they spend one day creating a simple landing page explaining their value proposition, then manually connect buyers and sellers via email for two weeks.
This isn't about building - it's about proving someone will pay for the outcome. AI should enhance a solution that already works manually, not create a solution from scratch.
When I implemented AI content generation for my Shopify client, they already had a content strategy that worked. They were manually creating product descriptions and blog posts that drove traffic. The AI scaled what was already working - it didn't create a new strategy.
Step 2: Test AI as Enhancement, Not Innovation
Every successful AI implementation I've managed started with this question: "What repetitive, time-consuming task could AI handle better?" Never: "What innovative AI feature should we build?"
For example, my B2B SaaS client was manually sorting new products into 50+ categories. This took hours per week and created bottlenecks. An AI workflow solved this specific operational problem. The validation happened when we proved the manual process was both necessary and painful.
Step 3: The 10x Rule for AI Justification
I learned this from watching failed AI projects: if AI doesn't make something 10x better, it's probably not worth the complexity. Marginal improvements get abandoned when the novelty wears off.
The content generation project worked because AI made content creation 20x faster while maintaining quality. Writing product descriptions manually took 2 hours per day; AI reduced it to 10 minutes. That's transformative, not incremental.
Step 4: Revenue Validation Before Feature Validation
The most dangerous mistake I see is optimizing AI features based on usage metrics instead of revenue impact. High engagement with AI features means nothing if it doesn't translate to business results.
When I built AI-powered email sequences, I didn't measure open rates or click rates first. I measured revenue recovery from abandoned carts. The AI was succeeding only if it drove more sales, not more engagement.
Step 5: The Human Fallback Test
Every AI solution I implement must have a human fallback. If the AI breaks, stops working, or gets expensive, can the business survive? If not, you've built dependency, not enhancement.
This test reveals whether you're solving a real problem or just creating AI theater. Real problems have manual solutions - they're just inefficient. AI theater problems only exist when the AI exists.
Manual First
Prove demand works without AI before adding complexity. Every successful AI project I've managed started with manual validation.
10x Impact
AI should make something dramatically better, not marginally improved. Look for 10x improvements in speed, cost, or quality.
Revenue Focus
Measure business outcomes, not AI engagement metrics. Features that don't drive revenue get abandoned.
Fallback Plan
Always have a human alternative. If AI dependency breaks your business, you haven't enhanced - you've created risk.
The framework transformed how I approach AI validation. Instead of expensive failures, I now have a 90% success rate with AI implementations because I validate the market need first.
The marketplace client followed my advice: they created a simple Notion page and manually connected 12 buyers with sellers over 3 weeks. They discovered their matching algorithm wasn't needed - buyers preferred browsing all options themselves. They saved $50,000+ by validating manually first.
My content generation client went from 300 monthly visitors to 5,000+ in 3 months, with AI handling 90% of content creation. But this worked because they already had proven content strategies.
The abandoned cart email project increased recovery revenue by 240% within 6 weeks. Success came from solving an existing problem (cart abandonment) with AI enhancement, not creating new AI features.
Most importantly, I stopped losing clients to failed AI projects. When validation happens before development, both client satisfaction and project success rates improve dramatically.
What I've learned and the mistakes I've made.
Sharing so you don't make them.
Here are the seven critical lessons from 6 months of real AI implementation:
AI amplifies existing business models - it rarely creates new ones. Focus on scaling what works, not inventing what might work.
Manual validation is faster and cheaper than AI prototyping. Spend days proving demand, not months building features.
Usage metrics lie in AI projects. People will play with AI features without paying for outcomes.
The best AI solutions feel invisible. Users care about results, not the technology delivering them.
AI complexity grows exponentially. Start simple and add sophistication only when revenue justifies it.
Most "AI-first" ideas are solutions looking for problems. Start with problems looking for solutions.
The hardest part isn't building AI - it's knowing when not to. The best AI validation sometimes means avoiding AI entirely.
How you can adapt this to your Business
My playbook, condensed for your use case.
For your SaaS / Startup
For SaaS startups validating AI features:
Start with customer development interviews about current pain points, not AI possibilities
Test manual workflows first, then automate with AI only if 10x improvement is possible
Focus on operational AI (internal efficiency) before customer-facing AI features
For your Ecommerce store
For e-commerce stores considering AI validation:
Identify repetitive tasks that slow growth (content creation, categorization, customer service)
Test AI on small product subsets before scaling across entire catalog
Measure revenue impact, not engagement metrics, when validating AI features