Growth & Strategy
Personas
SaaS & Startup
Time to ROI
Short-term (< 3 months)
Last year, a potential client came to me with an exciting AI platform idea and a substantial budget. They wanted to build a sophisticated two-sided marketplace with AI matching algorithms, automated workflows, and all the bells and whistles.
I said no.
Not because I couldn't deliver—AI integrations have never been easier to implement. But because their opening statement revealed everything wrong with how most founders approach AI feature testing: "We want to see if our AI idea works by building it first."
They had zero validated users, no proof anyone wanted their specific AI solution, and no understanding of the actual problems they were trying to solve. Just enthusiasm for AI technology and faith that building first would lead to answers.
This conversation completely changed how I think about testing AI features. While everyone's rushing to implement AI-powered solutions, most are testing in the most expensive way possible—by building first and hoping users will validate later.
After working on dozens of AI implementation projects and seeing the rise of no-code AI tools, I've learned that the best way to test AI features isn't with code—it's with humans pretending to be AI.
In this playbook, you'll discover:
Why building AI features first is the most expensive form of market research
The "Wizard of Oz" method for testing AI without algorithms
My 3-phase framework for validating AI features before any development
Real examples of how manual AI testing saved clients $50K+ in development costs
When to actually start building AI features (spoiler: it's later than you think)
Industry Reality
What the AI startup world preaches about testing
Walk into any tech accelerator or scroll through AI startup Twitter, and you'll hear the same advice about testing AI features:
"Build fast, iterate quickly! Use no-code tools to ship AI features in days!"
"Test your AI model with real users and gather feedback immediately!"
"Deploy your MVP and let the market validate your AI approach!"
This sounds logical until you realize what "fast" actually means in practice. Even with tools like Bubble, Zapier, or AI APIs, you're still talking about:
Weeks of development to integrate AI APIs and build user interfaces
Complex data pipelines to feed your AI models with meaningful information
User onboarding flows that explain how your AI features work
Error handling for when AI models produce unexpected results
Continuous model training based on user interactions and feedback
The conventional wisdom treats AI feature testing like A/B testing button colors. But testing AI is fundamentally different—you're not optimizing an existing solution, you're validating whether the AI solution itself addresses a real problem that users are willing to pay for.
Most AI advice assumes you know what to build. But the hardest part isn't building AI features—it's knowing which AI features are worth building in the first place.
Consider me as your business complice.
7 years of freelance experience working with SaaS and Ecommerce brands.
That client's request forced me to articulate something I'd been thinking about for months: if you're truly testing whether an AI feature works, you shouldn't need AI to test it.
Instead of taking their project, I shared a different approach. Here's what I learned from applying manual AI testing across multiple client situations:
The Problem with AI-First Testing
Every time clients wanted to "test" AI features by building them first, the same pattern emerged:
Months spent building AI integrations and user interfaces
Launch to crickets because no validated audience existed
Painful realization that the AI was solving a problem users didn't actually have
Expensive pivots trying to find product-market fit after development
The breakthrough came when I realized that most AI features can be simulated manually before any development. Users don't care if your "AI recommendation engine" is actually a human analyst in the background—they care whether the recommendations are valuable.
My First Manual AI Test
Rather than building their marketplace platform, I proposed a one-day test:
Create a simple landing page describing their AI matching service
Manually reach out to potential users on both sides of the marketplace
Act as the "AI" by manually analyzing user needs and making matches
Present recommendations as if they came from an algorithm
The result? Within two weeks, we discovered their core assumption was wrong. Users didn't want an AI to make matches—they wanted an AI to help them evaluate matches they found themselves. We saved months of development time by learning this through manual testing instead of building first.
Here's my playbook
What I ended up doing and the results.
Based on that experience and several similar projects since, I developed a systematic approach to testing AI features that saves months of development and thousands in costs:
Phase 1: The Wizard of Oz Test (Week 1-2)
The goal is to simulate your AI feature using human intelligence, then present the results as if they came from AI:
Identify the core AI behavior you want to test (recommendations, automation, analysis, etc.)
Create a minimal interface where users can input data and expect AI output
Manually process user inputs using human intelligence and expertise
Deliver results formatted as if they came from an AI system
Track user behavior and satisfaction with the "AI" outputs
Phase 2: Pattern Recognition (Week 3-4)
Once you have manual results, analyze what actually makes users happy with your AI feature:
Document your manual decision process—what factors influence your recommendations?
Identify user feedback patterns—which types of AI outputs get the best response?
Map the data requirements—what information do you actually need to replicate your manual process?
Validate the value hypothesis—are users willing to pay for this AI capability?
Phase 3: Hybrid Testing (Month 2)
Before building full AI automation, create a hybrid system that combines AI tools with human oversight:
Integrate basic AI APIs (like OpenAI or Anthropic) to handle simple tasks
Route complex decisions to human review before presenting to users
A/B test AI vs. human outputs to understand where each performs better
Gradually increase AI automation only for tasks where it matches human performance
Phase 4: Development Decision (Month 3+)
Only after proving demand and understanding requirements should you invest in custom AI development:
Calculate the automation ROI—how much manual work would AI eliminate?
Define success metrics—what does "good enough" AI performance look like?
Plan the development roadmap based on proven user workflows
Build incrementally while maintaining the proven manual backup system
Manual First Approach
Start with human intelligence to simulate AI behavior - validate the concept before building algorithms
Data Collection Strategy
Track what makes users happy with your "AI" when it's actually human-powered intelligence
Hybrid Validation
Combine simple AI tools with human oversight to test automation before full development
Business Model Proof
Validate users will pay for AI value before investing in complex technical implementation
Applying this framework across multiple client projects revealed patterns most founders miss when they build AI-first:
User Expectations vs. AI Reality
In 7 out of 8 manual AI tests I've run, users were happier with human-generated results than they would have been with typical AI outputs. This taught me that AI features often need to exceed human performance to feel valuable—matching human performance feels disappointing.
The Speed Paradox
Manual testing feels slower than building AI features, but it's actually 10x faster to get meaningful validation. One client saved 4 months of development time by discovering their core AI assumption was wrong in week 2 of manual testing.
Pricing Clarity
Manual testing makes pricing conversations easier because users experience the value directly. When you frame AI features around outcomes ("AI recommendations that increase your conversion rate by 15%") instead of capabilities ("machine learning recommendation engine"), pricing becomes straightforward.
Technical Requirements Reality
Manual testing reveals the minimum viable AI performance needed. Most clients assumed they needed sophisticated AI when simple API calls plus human review would meet user needs at 10% of the development cost.
What I've learned and the mistakes I've made.
Sharing so you don't make them.
The most important lesson from testing AI features manually? Users don't buy AI—they buy outcomes that happen to be delivered by AI.
Here are the key insights that emerged from this approach:
AI features need to be 3x better than manual processes to feel worth the switch—matching human performance feels disappointing
Manual testing reveals edge cases that you'd never anticipate in AI development planning
User feedback on "AI" behavior is more honest when they think it's automated vs. human-generated
Simple AI + human review often outperforms complex AI models for years before automation ROI makes sense
The hardest part isn't building AI—it's knowing which human behaviors are worth automating
Manual testing forces you to understand the business logic before automating it
Users will pay for AI value before you build AI if you can prove the value manually
The framework works because it separates two different risks: market risk (do people want this?) and technical risk (can we build this?). By testing market risk first with manual processes, you only invest in technical risk after proving demand.
How you can adapt this to your Business
My playbook, condensed for your use case.
For your SaaS / Startup
For SaaS founders considering AI features:
Start with manual customer support before building AI chatbots
Test AI recommendations by having team members curate suggestions manually
Validate pricing for AI features through manual service delivery first
Use simple AI APIs with human review before custom model development
Build manual backup systems for when AI features fail
For your Ecommerce store
For ecommerce entrepreneurs exploring AI:
Test AI product recommendations by manually curating suggestions based on purchase history
Validate AI chatbot value by having team members handle support manually first
Simulate AI inventory management by testing manual demand forecasting processes
Test AI personalization by manually segmenting customers and customizing experiences
Use human analysis to simulate AI insights before building automated reporting