Growth & Strategy
Personas
SaaS & Startup
Time to ROI
Long-term (6+ months)
Last year, I watched a promising AI startup burn through $2 million in funding trying to build "the perfect machine learning model" for their product. Six months later, they had zero paying customers. Their technology was impressive - the kind that wins hackathons and gets featured on Product Hunt. But here's the uncomfortable truth: nobody actually wanted to use it.
This story isn't unique. In my experience working with SaaS startups and AI companies, I've seen this pattern repeat over and over. Teams get so obsessed with the ML technology that they completely forget about the humans who are supposed to use it.
The traditional approach to product-market fit doesn't work for machine learning products. You can't just build an MVP, get user feedback, and iterate. ML products have a fundamentally different validation process because the "product" is often a black box that users can't easily evaluate.
Here's what you'll learn from my experience helping AI startups find their footing:
Why conventional PMF frameworks fail for ML products
The 3-layer validation system I use with AI clients
How to identify when your ML product actually solves a problem people will pay for
The difference between "cool technology" and "market-ready solution"
A framework for testing ML product assumptions without building the full system
Market Reality
What the AI industry tells you about PMF
If you've spent any time in the AI startup ecosystem, you've heard the same advice repeated everywhere. The conventional wisdom sounds logical on paper, but it's fundamentally flawed when applied to machine learning products.
Here's what every AI accelerator and startup guru tells you:
"Build an MVP and get user feedback"
"Focus on solving a real problem"
"Data will show you the way"
"If you build it well enough, customers will come"
"Start with the technology, find the market later"
This advice exists because it works for traditional software products. You can build a simple web app, show it to potential users, get immediate feedback, and iterate quickly. The feedback loop is clear and actionable.
But here's where it breaks down for ML products:
Machine learning products have what I call "evaluation lag." Users can't immediately judge whether your ML system is good or not. They need time to see patterns, understand accuracy, and trust the predictions. A user might try your recommendation engine once and think it's terrible, when in reality, it just needs more data about their preferences.
The second problem is "solution complexity." Traditional software solves obvious problems - you need to send emails, so you use an email tool. But ML products often solve problems users didn't know they had, or solve them in ways users don't immediately understand.
Finally, there's the "technical barrier." Most potential customers can't evaluate the quality of your machine learning approach. They can't tell if your model is actually good or just impressive-sounding. This creates a trust gap that traditional PMF approaches don't address.
The result? Teams spend months perfecting their algorithms while completely missing whether anyone actually wants their solution. By the time they realize there's no market fit, they've burned through resources and lost momentum.
Consider me as your business complice.
7 years of freelance experience working with SaaS and Ecommerce brands.
Six months ago, I started working with a startup that had built what they claimed was "revolutionary predictive analytics for e-commerce." The founders were brilliant - PhDs from top universities, impressive technical backgrounds, and a product that could predict customer behavior with 94% accuracy in their tests.
But here's what their impressive demo didn't show: they had zero paying customers after 8 months of building.
The company had fallen into what I now recognize as the "ML PMF trap." They'd spent all their time perfecting their machine learning model and almost no time validating whether anyone actually wanted predictive analytics for their e-commerce store. They assumed that better accuracy automatically meant better product-market fit.
When I dug deeper into their approach, the problems became obvious. They'd started with the technology - "We can predict customer behavior!" - and then tried to find businesses that might want this capability. This is completely backwards from how successful products are built.
Here's what they tried first (and why it failed):
Their initial strategy was classic "build it and they will come." They created a sophisticated dashboard showing predictions, confidence intervals, and detailed analytics. They spent months perfecting the UI and adding more ML models. The assumption was that once e-commerce owners saw how accurate their predictions were, they'd immediately want to buy.
The reality was different. When they finally started showing the product to potential customers, the feedback was consistently lukewarm. Store owners would look at the predictions and say "That's interesting, but what am I supposed to do with this information?"
The founders interpreted this as a UX problem. They thought they just needed to make the insights more actionable or the interface more intuitive. So they spent another few months building automated recommendations and alert systems.
But the real problem was deeper. They'd never validated the fundamental assumption that e-commerce businesses actually wanted predictive analytics in the first place. They'd confused "technically impressive" with "commercially valuable."
This is when they brought me in to help figure out what was going wrong with their go-to-market approach.
Here's my playbook
What I ended up doing and the results.
The first thing I did was completely ignore their machine learning model. Instead, I focused on understanding the actual problems their potential customers were trying to solve.
I spent two weeks interviewing e-commerce store owners, not about predictive analytics, but about their daily challenges. What I discovered was eye-opening: none of them were asking for better predictions. They were asking for better sales.
Here's the 3-layer validation framework I developed for ML products:
Layer 1: Problem Validation (Before Building)
Instead of starting with "We can predict customer behavior," we started with "What decisions are e-commerce owners struggling with?" I interviewed 50+ store owners and found that their biggest challenge wasn't predicting what customers would do - it was knowing which products to promote when they were running low on inventory.
This insight completely changed our approach. The problem wasn't "I need predictions" - it was "I need to know which products to push before I run out of stock."
Layer 2: Solution Validation (Manual First)
Before touching any ML code, we manually solved this problem for 5 test stores. I personally analyzed their sales data, identified products at risk of stockouts, and recommended which items to promote. We tracked whether our recommendations actually increased sales of those products.
The results were promising. 4 out of 5 stores saw immediate improvements in inventory turnover when they followed our manual recommendations. This validated that the solution approach was valuable, regardless of how we delivered it.
Layer 3: Technology Validation (ML Implementation)
Only after proving the manual approach worked did we implement the ML system to automate these recommendations. But here's the key: we didn't build a general "predictive analytics platform." We built a specific "inventory-based promotion recommender."
The difference in customer response was dramatic. Instead of "That's interesting," we started hearing "When can I start using this?" The same underlying ML technology, but positioned as a solution to a validated problem rather than a cool capability.
The Implementation Process:
We rebuilt their entire product strategy around this framework. First, we identified the specific decision points where e-commerce owners needed help: inventory management, seasonal planning, and promotion timing. Then we validated each use case manually before building the ML automation.
For the inventory use case, we created a simple Slack bot that would message store owners twice a week with specific products to promote, along with the reasoning. No complex dashboard, no technical jargon - just actionable recommendations they could implement immediately.
We A/B tested different messaging approaches and found that store owners responded best when we framed recommendations in terms of revenue impact rather than prediction accuracy. "Promote Product X this week to avoid a $2,000 stockout" worked much better than "94% confidence that Product X will sell out in 6 days."
Problem First
Always validate the customer problem before building any ML solution. Most failed AI products solve problems that don't exist.
Manual Testing
Prove your solution works manually before automating with ML. This validates both the approach and the value proposition.
Simple Delivery
Start with the simplest possible interface. Complex dashboards often hide whether the core value proposition is strong.
Revenue Framing
Frame ML insights in terms of business impact, not technical accuracy. Customers buy outcomes, not algorithms.
The transformation was remarkable. Within 3 months of implementing this framework, the startup went from zero to 15 paying customers. More importantly, their customer retention was 90% because they were solving real problems rather than showcasing cool technology.
The specific metrics that changed:
Customer acquisition time dropped from "never" to an average of 2.1 weeks from first demo to paid contract
Trial-to-paid conversion rate reached 73% (compared to industry average of ~15% for ML products)
Average deal size increased to $3,200/month because customers could clearly see ROI
Customer success stories became specific: "Increased promotion efficiency by 34%" instead of "Really accurate predictions"
But the most telling result was qualitative. Customer feedback shifted from "This is interesting technology" to "This tool directly impacts our revenue." When customers can articulate the specific business value your ML product provides, you've found product-market fit.
The unexpected outcome was that this approach actually improved their ML models. By focusing on specific use cases, they could collect much more targeted training data. Instead of trying to predict "customer behavior" in general, they were optimizing for "inventory-driven purchase likelihood" - a much more solvable and valuable problem.
What I've learned and the mistakes I've made.
Sharing so you don't make them.
Here are the key lessons I learned from this experience and subsequent ML product projects:
1. Technology-first thinking kills PMF
Starting with "We can do X with machine learning" almost always leads to solutions looking for problems. Instead, start with customer problems and use ML only when it's the best solution approach.
2. Manual validation is non-negotiable
If you can't solve the problem manually first, ML won't magically make it solvable. The manual process also helps you understand exactly what the ML system needs to optimize for.
3. Simple interfaces beat complex dashboards
Customers don't want to become data scientists. They want clear, actionable recommendations delivered in the simplest possible way. Your ML might be sophisticated, but your interface should be brain-dead simple.
4. Business metrics matter more than model metrics
A 70% accurate model that drives clear business outcomes beats a 95% accurate model that customers can't act on. Focus on metrics that matter to your customers, not metrics that matter to your ML team.
5. Specific beats general every time
"AI-powered business intelligence" is vague and hard to evaluate. "Inventory-based promotion recommender" is specific and immediately understandable. Narrow focus leads to stronger PMF.
6. Trust is the biggest barrier
Customers need to trust your ML recommendations enough to act on them. This trust is built through transparency about how recommendations are made and consistent accuracy in areas they can verify.
7. The manual-to-automated transition is critical
Don't jump straight from problem identification to full ML automation. The manual phase teaches you exactly what the automated system needs to do and helps you identify edge cases before they become customer problems.
How you can adapt this to your Business
My playbook, condensed for your use case.
For your SaaS / Startup
For SaaS startups building ML features:
Validate specific use cases manually before building ML automation
Focus on decision-support rather than prediction-showcasing
Start with simple recommendation delivery (email, Slack) before building dashboards
Measure business impact metrics, not just model accuracy
For your Ecommerce store
For ecommerce businesses considering ML tools:
Look for ML products that solve specific operational problems you already have
Test with manual processes first to validate the approach
Prioritize tools that integrate with your existing workflow
Focus on revenue impact rather than prediction accuracy