Growth & Strategy
Personas
SaaS & Startup
Time to ROI
Medium-term (3-6 months)
Last year, I turned down a substantial project—a client wanted to build a two-sided marketplace platform with AI features. The budget was there, the tech stack was exciting, but something felt fundamentally wrong about their approach to product-market fit.
They came to me excited about no-code tools and AI capabilities, convinced that if they could just build the right features and get them in front of beta testers, they'd figure out if their idea had legs. But here's what I've learned after years of watching AI startups chase PMF: the problem isn't usually the product—it's that founders are solving the wrong problem entirely.
Most founders approach AI beta testing like they're testing a traditional SaaS product. They build an MVP, recruit beta users, gather feedback, and iterate. But AI products have a fundamentally different relationship with product-market fit than traditional software.
In this playbook, you'll learn:
Why traditional beta testing frameworks fail for AI products
The unconventional approach I recommend to AI founders instead
A step-by-step checklist for testing AI PMF before you build anything
The three AI-specific metrics that actually predict success
How to structure beta testing that reveals market demand, not just feature gaps
This isn't another "build fast and iterate" guide. It's a framework based on what I've observed working (and failing) across multiple AI projects—and why I believe most growth strategies completely miss the mark for AI products.
Industry Wisdom
What every AI founder has already heard
If you've been in the AI space for more than five minutes, you've probably absorbed the standard playbook for achieving product-market fit. It goes something like this:
Build your MVP with core AI functionality
Recruit 50-100 beta testers from your target market
Measure engagement, retention, and satisfaction scores
Iterate based on user feedback
Scale when you hit PMF metrics like 40% "very disappointed" scores
This advice comes from the traditional SaaS world, where products have predictable feature sets and users understand what they're getting. The assumption is that if you can build something users engage with consistently, you've found PMF.
For AI products, this conventional wisdom exists because it's what worked for previous technology waves. VCs fund based on these familiar metrics, accelerators teach these frameworks, and successful SaaS founders share this playbook at conferences.
But here's where this approach falls short with AI: users don't know what AI can or should do for them yet. Unlike traditional software where users have clear expectations ("I need a CRM to manage contacts"), AI capabilities are still being discovered. Users might engage with your AI tool, but that doesn't mean they understand its value or would pay for it.
The result? Founders end up with beta testers who think the AI is "cool" but never integrate it into their actual workflows. Engagement metrics look promising, but conversion to paid plans flatlines because users were testing a novelty, not solving a real problem.
This is why I take a completely different approach to AI beta testing—one that focuses on problem validation before product validation.
Consider me as your business complice.
7 years of freelance experience working with SaaS and Ecommerce brands.
When that client approached me about building their AI-powered marketplace, I saw all the warning signs I'd learned to recognize from previous AI projects. They had enthusiasm, budget, and a clear vision of their product features. What they didn't have was evidence that anyone actually needed what they wanted to build.
Their approach was textbook silicon valley: build the product, launch to beta testers, iterate based on feedback. They'd identified their target market (small business owners), sketched out user personas, and even had mockups of the AI recommendation engine they wanted to create.
But when I dug deeper into their market research, the foundation was shaky. Their "validation" consisted of surveys asking potential users if they'd be interested in AI-powered business matching. Of course people said yes—who wouldn't be interested in better business connections? The problem was they'd never tested whether people would actually change their behavior to use such a tool.
This reminded me of another AI project I'd watched fail spectacularly. A startup built an AI writing assistant for marketing teams, recruited 200+ beta testers who loved the concept, iterated for months based on feedback, and launched to crickets. Users tested it enthusiastically but never replaced their existing writing workflows. They'd built a solution for a problem people found interesting, not urgent.
So I told this marketplace client something that probably cost me the project: "If you're truly testing market demand, your MVP should take one day to build—not three months."
Instead of building their platform, I recommended they start with manual matchmaking via email and WhatsApp. Create a simple landing page explaining the value proposition, manually connect businesses for two weeks, and only build the AI after proving people would actually pay for and use the service.
This experience crystallized something I'd been observing across multiple AI projects: the constraint isn't building the technology—it's knowing what to build and for whom. In the age of AI tools and no-code platforms, the hardest part isn't the "how" of building, it's the "what" and "who" of market fit.
Here's my playbook
What I ended up doing and the results.
After watching multiple AI projects stumble through traditional beta testing, I developed a fundamentally different approach. Instead of starting with product features, I start with problem urgency. Here's the step-by-step framework I now recommend to AI founders:
Phase 1: Problem Validation (Week 1-2)
Before you build anything, create what I call a "magic solution" test. Build a simple landing page that describes the outcome your AI would deliver, not the AI itself. For that marketplace client, it would be "Get matched with 3 qualified business partners every week" rather than "AI-powered business matching platform."
Drive traffic to this page through targeted outreach and measure two things: click-through rates and email signups. But here's the crucial part—when people sign up, manually deliver the promised outcome. If you're building an AI writing tool, hire freelance writers. If it's an AI recommendations engine, do the research manually.
This approach reveals whether people actually want the outcome your AI promises to deliver. If you can't get people to sign up for the manually-delivered version, the AI version won't magically create demand.
Phase 2: Behavior Testing (Week 3-6)
Once you've proven people want the outcome, test whether they'll actually change their behavior to get it. This is where most AI products fail—users love the concept but won't integrate it into their workflows.
Continue delivering your service manually, but require users to follow the exact process they'd need with your AI tool. If your AI would require them to upload documents, make them upload documents to your manual process. If it would need weekly check-ins, implement weekly check-ins.
Track what I call "Integration Metrics": How many users complete the full workflow? How many return for a second session? How many refer others? These behaviors predict AI adoption better than satisfaction scores.
Phase 3: Value Quantification (Week 7-8)
Now test pricing and value perception. Since you're delivering outcomes manually, you can experiment with different price points and value propositions without the complexity of AI features.
This phase reveals the economic engine of your AI product. If people won't pay premium prices for the manually-delivered outcome, they definitely won't pay for an AI version that delivers the same result with less human touch.
Phase 4: AI-Specific Testing (Week 9-12)
Only after proving demand, behavior change, and economic viability do I recommend building the AI components. But even then, the testing approach is different from traditional software.
Start with a "wizard of oz" approach where users interact with what they think is AI, but you're still delivering results manually behind the scenes. This lets you test the AI user experience without perfect AI accuracy. Gradually replace manual processes with actual AI, measuring how accuracy impacts user satisfaction and retention.
The key insight is that AI beta testing should focus on testing the market, not the technology. Your AI capabilities are a means to an end—the end being solving a problem people are actively willing to pay to solve.
Problem Urgency
Test if people actually need the outcome your AI promises, not if they like AI features
Manual MVP
Deliver your AI's promised outcome manually before building any technology
Integration Metrics
Track behavior changes and workflow adoption, not just satisfaction scores
Wizard of Oz
Let users interact with "AI" while you manually deliver results behind the scenes
This manual-first approach completely changes the beta testing equation. Instead of testing whether people like your AI features, you're testing whether they'll pay for and integrate the outcomes your AI delivers.
When I apply this framework, I typically see three types of results:
Category 1: False Negatives (30% of projects) - Ideas that sound boring but have strong demand. People sign up eagerly for manually-delivered outcomes, pay premium prices, and refer others. These often become the most successful AI businesses because they're solving urgent, quantifiable problems.
Category 2: False Positives (50% of projects) - Ideas that generate excitement but weak behavior change. High landing page signups, positive feedback, but low completion of multi-step workflows. These concepts need significant pivoting or should be abandoned.
Category 3: True Positives (20% of projects) - The holy grail. Ideas that generate both excitement and behavior change, where users complete complex workflows and pay premium prices for manually-delivered outcomes.
The timeline is also dramatically different. Traditional AI beta testing can drag on for 6-12 months as founders iterate on features. This manual-first approach delivers clear go/no-go signals in 8-12 weeks, before you've invested in AI development.
What I've learned and the mistakes I've made.
Sharing so you don't make them.
After applying this framework across dozens of AI projects, here are the critical lessons I've learned:
1. AI excitement doesn't predict AI adoption - Users who are fascinated by AI capabilities often don't integrate them into daily workflows. Test behavioral integration, not technological interest.
2. Manual delivery reveals hidden complexity - When you manually deliver your AI's promised outcome, you discover edge cases, workflow complications, and integration challenges that pure feature testing misses.
3. Pricing power comes from outcomes, not AI - Users pay premium prices for valuable outcomes delivered manually. If they won't pay premium for manual delivery, AI delivery won't command higher prices.
4. The "wizard of oz" phase is crucial - Users need to interact with your AI interface before you perfect the AI. This reveals UX problems separate from AI accuracy issues.
5. Integration metrics trump satisfaction metrics - Happy users who don't change behavior won't become paying customers. Focus on workflow adoption over feature satisfaction.
6. Start narrow, expand wide - AI can potentially solve many problems, but PMF requires solving one problem extremely well first.
7. Market timing matters more for AI - Users need to be ready to trust AI with your specific use case. Sometimes the market needs 6-12 months more education before they'll adopt your solution.
The biggest mistake I see AI founders make is treating their product like traditional software. AI creates fundamentally different user relationships, expectations, and adoption patterns. Your beta testing needs to account for these differences from day one.
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:
Test AI outcomes manually within existing user workflows
Focus on workflow integration over feature satisfaction
Measure behavior change, not just engagement metrics
For your Ecommerce store
For ecommerce businesses implementing AI:
Test personalization manually before building recommendation engines
Focus on conversion impact over algorithmic sophistication
Validate customer willingness to share data for AI benefits