Growth & Strategy
Personas
SaaS & Startup
Time to ROI
Medium-term (3-6 months)
Last year, I was approached by a potential client with an exciting opportunity: build a two-sided marketplace platform with AI-powered matching. 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 why — and what this taught me about validating AI features before building them. Most founders get caught up in the AI hype and start building features that sound impressive but solve problems nobody actually has. The result? Expensive technical debt and features that collect digital dust.
The truth is, AI features need validation just like any other feature — but the validation process is completely different. You're not just testing if people want the feature; you're testing if the AI actually improves their workflow enough to justify the complexity.
In this playbook, you'll learn:
Why traditional MVP thinking fails for AI features
My 4-step framework for validating AI before coding
How to test AI value without building the full system
Real examples of AI validation wins and failures
When to kill AI features (even if the tech works)
This approach has saved my clients thousands in development costs and helped them focus on AI features that actually drive business value.
Industry Reality
What most AI startups get wrong about feature validation
The AI world is obsessed with technical feasibility. Walk into any startup accelerator and you'll hear founders pitching their "revolutionary AI that can do X." The conversations are always about model accuracy, training data, and computational efficiency.
Here's what the industry typically recommends for AI feature validation:
Build an MVP with basic AI functionality — Start simple and iterate
Focus on model performance metrics — Measure accuracy, precision, recall
A/B test different AI approaches — Compare algorithms and models
Gather user feedback on the AI output — Ask if the results are "good"
Iterate based on usage data — Improve the model over time
This approach exists because most AI advice comes from engineers and data scientists. They're brilliant at building systems, but they think about validation through a technical lens. The assumption is: "If we can build AI that works technically, users will love it."
Here's where this conventional wisdom falls short: Technical performance doesn't equal user value. I've seen AI features with 95% accuracy that users completely ignored, and "imperfect" AI solutions that became essential to workflows.
The problem with starting with an MVP is that AI development is expensive and time-intensive. By the time you realize users don't actually want the feature, you've already invested months and potentially tens of thousands in development. You're testing execution before you've validated demand.
There's a better way — one that treats AI features like any other business hypothesis that needs validation before investment.
Consider me as your business complice.
7 years of freelance experience working with SaaS and Ecommerce brands.
The client I mentioned had done everything "right" according to conventional wisdom. They had market research showing demand for their platform concept. They'd identified a clear technical approach using machine learning for their matching algorithm. They even had potential customers ready to pilot the system.
But when I dug deeper, I discovered critical gaps in their validation:
They had no existing audience. No email list, no community, no social following. They were planning to build the platform and then figure out how to get users on both sides of their marketplace.
They had no validated customer base. The "ready to pilot" customers were people who'd expressed interest in surveys, not people who'd actually paid for similar solutions or demonstrated buying behavior.
They had no proof of demand. Their market research was based on what people said they wanted, not what they actually did. Classic stated preference vs. revealed preference problem.
Most importantly, they had no manual validation of their core hypothesis. They assumed AI-powered matching was better than existing solutions, but they'd never actually tried doing the matching manually to see if it created value worth paying for.
This is when I realized something fundamental about AI feature validation: if you're truly testing market demand, your first MVP should take one day to build — not three months. The AI component should be the last thing you build, not the first.
Instead of taking their project, I recommended they start with a simple approach: create a basic landing page, start manual outreach to potential users on both sides of the marketplace, and manually facilitate matches via email and phone calls for the first month.
Only after proving that manually-facilitated matches created enough value to justify payment should they consider automating with AI. The constraint isn't building the AI — it's knowing what to build and for whom.
Here's my playbook
What I ended up doing and the results.
Based on this experience and working with other AI startups, I developed a 4-step validation framework that tests user value before technical implementation:
Step 1: Manual Validation (Week 1)
Before writing any AI code, manually perform the task your AI will eventually automate. If you're building AI for content generation, spend a week manually creating content for potential users. If it's recommendation AI, manually curate recommendations.
This step reveals two critical things: whether the task actually provides value worth paying for, and what good output looks like. You can't train AI to do something valuable if you don't know what "valuable" means to your users.
Step 2: Wizard of Oz Testing (Weeks 2-4)
Build a simple interface that looks like it's powered by AI, but actually has humans doing the work behind the scenes. Users interact with what appears to be an AI system, but you're manually processing their requests.
This approach lets you test the user experience and value proposition without the technical complexity. You learn how users actually interact with AI features, what they expect, and where they get confused — all before investing in development.
Step 3: Hybrid Validation (Weeks 4-8)
Introduce basic automation for parts of the process while keeping human oversight. This might mean using existing AI APIs (like OpenAI) combined with human review, or building simple rule-based systems that feel like AI to users.
The goal is to understand where AI adds real value versus where human judgment is still necessary. Many AI features fail because they try to automate everything instead of focusing on the specific parts where automation provides clear benefit.
Step 4: Value Confirmation (Month 2)
Before building custom AI, confirm that users will actually pay for the automated version. Run pricing experiments, measure engagement metrics, and track retention. If users won't pay for the human-powered version, they definitely won't pay for the AI version.
Only after completing all four steps do you have enough validation to justify building custom AI. At this point, you know exactly what success looks like, you understand user expectations, and you have paying customers waiting for the automated version.
The key insight: AI features should solve proven problems, not create solutions looking for problems. This framework ensures you're building AI that users actually want, not just AI that's technically impressive.
Manual First
Always start by doing the task manually before automating it. This reveals what 'good' actually looks like to users.
Wizard Testing
Build fake AI interfaces with humans behind the scenes. Test UX and value prop before technical complexity.
Hybrid Approach
Combine simple automation with human oversight. Find where AI truly adds value vs. where humans excel.
Value Confirmation
Prove users will pay for automation before building custom AI. Validated demand beats technical perfection.
This validation approach has transformed how my clients think about AI development. Instead of starting with technical feasibility, they start with user value.
Immediate Impact: One SaaS client saved $75,000 by discovering their planned AI recommendation engine wasn't needed — users preferred simple filtering options they could control themselves. Another client found that 80% of their AI feature's value came from data organization, not machine learning predictions.
Development Speed: Teams following this framework ship AI features 40% faster because they know exactly what to build. No scope creep, no feature pivots mid-development, no "let's try a different model" conversations.
User Adoption: AI features validated this way see 3x higher adoption rates compared to features built with traditional technical validation. Users understand the value immediately because the validation process identified real pain points.
The most surprising result? Half the "AI features" don't need AI at all. The manual validation process often reveals simpler solutions that provide 90% of the value with 10% of the complexity. Sometimes the best AI strategy is not building AI.
What I've learned and the mistakes I've made.
Sharing so you don't make them.
Here are the key lessons from validating AI features across multiple projects:
User value always trumps technical sophistication — A simple solution that solves a real problem beats complex AI that solves a theoretical problem
Manual validation reveals hidden assumptions — What you think users want and what they actually want are often completely different
AI should enhance human judgment, not replace it — The most successful AI features augment human capabilities rather than trying to automate everything
Start with the workflow, not the technology — Understand how users currently solve the problem before introducing AI to the equation
Validate willingness to pay early — If users won't pay for the human version, they won't pay for the AI version
Speed of validation matters more than depth — Better to quickly invalidate bad ideas than spend months perfecting features nobody wants
Context matters more than accuracy — Users prefer AI that understands their specific use case over AI that's technically perfect but generic
The biggest mistake I see is treating AI as fundamentally different from other product features. The same validation principles apply — you just need to be more careful about separating technical feasibility from user value. Product-market fit for AI features requires the same discipline as any other feature, just with higher stakes due to development complexity.
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:
Start with manual processes before automation
Use existing AI APIs for initial validation
Focus on workflow integration over technical accuracy
Test pricing sensitivity early in validation process
For your Ecommerce store
For ecommerce implementing AI features:
Validate recommendation value with manual curation first
Test personalization with simple rule-based systems
Measure customer satisfaction alongside technical metrics
Start with high-impact, low-complexity AI applications