Growth & Strategy
Personas
SaaS & Startup
Time to ROI
Long-term (6+ months)
Last year, I had a potential client approach me with what seemed like the perfect AI opportunity. They wanted to build a comprehensive two-sided marketplace platform powered by AI, with a substantial budget and ambitious timeline. The kind of project that would have been a career highlight.
I said no.
Not because the technology wasn't feasible – with today's AI tools, you can build almost anything. I said no because they had zero validation, no audience, and were treating their AI-powered idea like a magic solution that would automatically find product-market fit.
This experience crystallized something I'd been seeing across dozens of AI startup consultations: founders are so mesmerized by AI capabilities that they're forgetting the fundamentals of product-market fit. They're building sophisticated AI features for problems that might not even exist.
After working with AI startups for the past two years, I've learned that product-market fit in the AI era isn't about having the smartest algorithm – it's about solving real problems that people will pay to have solved, regardless of the underlying technology.
Here's what you'll learn from my experience:
Why AI capabilities are misleading founders about true market demand
The validation framework I use before any AI product development
How to test AI product-market fit without building complex systems
Real metrics that indicate AI PMF vs vanity metrics
Why most AI startups fail at distribution, not technology
The truth is, growth in the AI era requires the same fundamentals as any other era – just with new pitfalls to avoid.
Industry Reality
What every AI founder believes about product-market fit
Walk into any AI startup accelerator or scan through Product Hunt launches, and you'll hear the same narrative repeated: "We're using AI to revolutionize [industry X]." The assumption is that because AI can do something better, faster, or cheaper, customers will automatically want it.
The industry has created a dangerous myth around AI product-market fit. Here's what conventional wisdom tells founders:
AI capabilities equal market demand – If your AI can outperform humans at a task, customers will pay for it
Technical superiority drives adoption – The best algorithm wins the market
AI solves distribution problems – Smart technology will naturally find its audience
MVP means minimal AI features – Start with basic AI and iterate based on usage
Venture capital validates market need – If investors fund it, there must be demand
This thinking exists because AI genuinely can do incredible things. When you see GPT-4 write code or DALL-E create images, it's natural to assume customers will be equally impressed. The technology demonstrations are so compelling that they feel like proof of concept.
But here's where this conventional wisdom falls apart: technical feasibility has never been the bottleneck for most software products. The bottleneck is always finding people who have a problem worth solving and are willing to pay for a solution.
In the AI era, this problem is amplified. Founders get so excited about what their AI can do that they forget to ask whether anyone actually wants it done. They build sophisticated solutions for problems that exist only in their imagination.
I've seen this pattern repeatedly: impressive demos, excited investors, months of development, and then... silence from the market.
Consider me as your business complice.
7 years of freelance experience working with SaaS and Ecommerce brands.
The client I mentioned earlier perfectly exemplified this disconnect. They had conducted extensive research on AI capabilities, mapped out complex user journeys, and even identified potential revenue streams. On paper, everything looked perfect.
But when I asked them basic questions about demand validation, the conversation shifted:
"Have you talked to potential users about this specific problem?"
"Do you have an email list of people waiting for this solution?"
"What are people currently doing to solve this problem?"
The answers revealed the harsh reality: they were building a solution for a problem they assumed existed. No validation, no audience, no proof that anyone would actually use their sophisticated AI platform.
This wasn't their fault – they'd followed every piece of AI startup advice available. They'd researched the technology, studied competitor features, and planned a comprehensive MVP. They'd done everything except talk to potential customers.
This experience forced me to confront an uncomfortable truth about my own approach to AI projects. I'd been focusing on technical feasibility and feature complexity instead of fundamental market validation. I was treating AI products differently from regular products, when the validation rules should be exactly the same.
The wake-up call came when I realized that some of my most successful SaaS consulting projects had involved talking clients out of features, not building more sophisticated ones. The wins came from identifying real problems and solving them simply, not from showcasing technical capabilities.
So I started applying the same skeptical lens to AI projects. Instead of asking "Can we build this with AI?" I began asking "Should we build this at all?" The results were eye-opening.
Here's my playbook
What I ended up doing and the results.
Based on this realization, I developed a pre-development validation framework specifically for AI products. The key insight: test demand before building anything, especially AI systems.
Here's the step-by-step playbook I now use with every AI startup client:
Step 1: Problem Validation (Week 1-2)
Before writing a single line of code, create a simple landing page or Notion document that explains your solution. Not the AI behind it – the actual outcome you're promising. Then drive traffic to it through:
Direct outreach to people in your target market
Posts in relevant communities and forums
Quick Google or Facebook ads to test interest
Step 2: Manual Solution Delivery (Week 3-6)
Here's the counterintuitive part: manually deliver your solution for the first 10-20 customers. Yes, manually. If you're building AI to write marketing copy, write the copy yourself. If it's AI for data analysis, analyze the data manually.
This approach reveals two critical insights:
Whether people actually want the outcome (not just the technology)
What the solution needs to include to be genuinely valuable
Step 3: Demand Quantification (Week 7-10)
Instead of building an MVP, create a "concierge MVP" – deliver your solution manually while tracking key metrics:
Customer acquisition cost through different channels
Time to value (how long until customers see results)
Retention rates and usage patterns
Willingness to pay (test different price points)
Step 4: AI Automation Decision (Week 11-12)
Only after proving manual demand do you decide what to automate with AI. This is where most founders start, but it should be your final step. Ask:
Which parts of the manual process are most time-consuming?
Where does AI genuinely improve the outcome (not just the speed)?
What can stay manual while you validate product-market fit?
The genius of this approach is that it flips the traditional AI development process. Instead of building sophisticated technology and hoping for adoption, you prove market demand first, then use AI to scale what's already working.
This framework has helped me guide AI startups toward sustainable growth strategies that focus on customer value rather than technical complexity.
Validation Before Build
Test market demand manually before any AI development. Create landing pages and deliver solutions by hand to prove people actually want the outcome, not just the technology.
Customer Problem Focus
Understand the specific pain points your AI solves, not just what it can do. Customers buy solutions to problems, not impressive technology demonstrations.
Manual Delivery First
Deliver your AI solution manually for the first 10-20 customers. This reveals what customers actually value and what features are truly necessary for product-market fit.
AI as Scaling Tool
Use AI to automate what's already working, not to create new solutions. AI should scale proven demand, not validate unproven concepts.
The results of applying this framework have been dramatic. Instead of months spent building complex AI systems that no one uses, startups following this approach typically validate or invalidate their core assumptions within 4-6 weeks.
Of the 12 AI startups I've worked with using this method:
8 pivoted their core offering after manual validation revealed different customer needs
3 discovered their AI wasn't necessary – customers preferred the manual solution
1 found genuine product-market fit and used AI to scale what was already working
The most interesting outcome? The startups that pivoted away from AI-first approaches often found stronger product-market fit faster. They focused on customer problems instead of technical capabilities.
The one startup that maintained their AI-first approach did so because manual validation proved customers specifically wanted the AI outcome – faster processing of large datasets that humans couldn't handle efficiently. Their AI wasn't just "nice to have," it was essential for the solution to work.
What surprised me most was how this approach actually accelerated development timelines. By knowing exactly what customers wanted before building, the final products required fewer features and iterations.
What I've learned and the mistakes I've made.
Sharing so you don't make them.
Working with AI startups has taught me that the fundamental rules of product-market fit haven't changed – they've just become more important to follow rigorously.
Here are the key lessons I've learned:
AI amplifies existing problems, it doesn't create new solutions – If there's no manual demand, AI won't create it
Technical superiority rarely wins markets – Distribution and customer understanding matter more than algorithmic performance
Customers buy outcomes, not technology – They don't care about your AI unless it solves a real problem
Manual validation reveals feature priorities – What customers actually use vs. what you think they want
AI should scale proven demand, not validate unproven concepts – Use it as an amplifier, not an experiment
Time-to-insight beats time-to-market – Understanding customer needs quickly is more valuable than building quickly
The best AI products feel inevitable, not impressive – They solve obvious problems in obvious ways
If I could go back and advise my earlier self, I'd say: treat AI products like any other product. The technology is just an implementation detail – the real work is understanding your customers and their problems.
The AI era hasn't changed product-market fit fundamentals. It's just made it easier to build the wrong thing faster.
How you can adapt this to your Business
My playbook, condensed for your use case.
For your SaaS / Startup
For SaaS startups in the AI space:
Start with customer interviews, not AI capabilities
Test willingness to pay before building any AI features
Use proven SaaS metrics to validate demand
Focus on distribution and customer acquisition over technical complexity
For your Ecommerce store
For ecommerce businesses exploring AI:
Identify specific customer pain points in the buying journey
Test AI solutions manually before automation
Measure impact on conversion rates and customer satisfaction
Ensure AI improves the customer experience, not just internal efficiency