Growth & Strategy
Personas
SaaS & Startup
Time to ROI
Short-term (< 3 months)
Last month, I watched a potential client burn through $50,000 building an AI-powered platform that nobody wanted. The AI features looked impressive in demos, but users found them confusing and irrelevant to their actual needs. Three months of development, gone.
This is exactly why I've started using Bubble as my AI testing ground before committing to any serious development. Not for the final product – but as the perfect laboratory to validate whether AI features actually solve real problems.
Most founders I work with get this backwards. They either avoid AI completely ("it's just hype") or go all-in on complex implementations without understanding user behavior. The smart move? Test fast, fail cheap, learn quickly.
Here's what you'll discover in this playbook:
Why Bubble beats traditional prototyping for AI validation
My 3-step framework for testing AI functionality without coding
How to measure what actually matters in AI feature testing
Common AI testing mistakes that waste months of development
When to graduate from Bubble testing to full development
Whether you're building a SaaS tool or exploring AI automation, this approach will save you time, money, and the heartbreak of building features nobody wants.
Industry Reality
What every startup founder hears about AI testing
Walk into any startup accelerator or scroll through Product Hunt, and you'll hear the same AI advice repeated everywhere:
"Just integrate OpenAI's API and see what happens." The conventional wisdom suggests you should pick an AI service, build a integration, and hope users understand the value.
Here's the standard playbook everyone follows:
Choose your AI provider - Usually OpenAI because it's trendy
Build the integration - Spend weeks on API connections
Launch and iterate - Release and pray users "get it"
Measure everything - Track usage metrics and hope they improve
This advice exists because it worked for a few high-profile startups. When ChatGPT exploded, everyone wanted to add AI features. VCs started asking "What's your AI strategy?" in every pitch meeting.
But here's where this conventional approach breaks down: most AI implementations fail not because of technical issues, but because of fundamental product-market fit problems. Users don't care how smart your AI is if they can't figure out why they need it.
I've seen startups spend months building sophisticated AI features that users ignore. The problem isn't the AI – it's that nobody validated whether users actually wanted AI to solve that particular problem in the first place.
The industry pushes this "build first, validate later" approach because it's easier to talk about technical implementations than user psychology. But when you're dealing with AI, user psychology is everything.
Consider me as your business complice.
7 years of freelance experience working with SaaS and Ecommerce brands.
When a B2B startup approached me wanting to add "AI-powered insights" to their project management tool, I had a choice. We could either spend three months building a full AI integration, or we could test the concept first.
The client was convinced users needed AI to automatically categorize their tasks and predict project timelines. They had seen similar features in other tools and assumed this was the logical next step. The technical team was excited about the challenge.
But something felt off. In our user research calls, people kept saying they wanted "better visibility" and "clearer priorities," but nobody specifically asked for AI. They wanted solutions, not necessarily AI solutions.
This is where I made a decision that initially frustrated the client: instead of building anything, we would prototype the AI features in Bubble first. Not as a real product, but as a testing environment to understand user behavior.
The client's first reaction was skeptical. "Why build a fake version when we could build the real thing?" But I've learned that when it comes to AI features, users often can't articulate what they want until they interact with it.
We needed to answer fundamental questions: Would users actually use AI categorization if we built it? Did automatic predictions help or confuse them? Which AI features felt valuable versus gimmicky?
This wasn't about avoiding work – it was about avoiding the wrong work. Building AI integrations is expensive and time-consuming. If users don't engage with the concept, you've wasted months of development on features that actively hurt your product.
The traditional approach would have been to survey users ("Would you use AI features?") or run focus groups. But people lie in surveys, and focus groups don't predict real behavior. We needed something closer to reality.
Here's my playbook
What I ended up doing and the results.
Here's exactly how I test AI functionality in Bubble before committing to real development. This isn't about building production-ready features – it's about validating user behavior and feature value.
Step 1: Build Behavioral Mockups, Not Functional AI
First, I recreate the core user interface in Bubble without any actual AI. Instead of connecting to OpenAI or Claude, I manually populate responses that simulate what the AI would provide. This sounds like cheating, but it's brilliant for testing.
For the project management client, we built screens showing "AI-generated" task categories and timeline predictions. Users could interact with these suggestions, accept or reject them, and modify the results. Behind the scenes, I was manually updating the responses based on realistic scenarios.
This "Wizard of Oz" approach lets you test the user experience without building the infrastructure. You learn whether users understand the feature, find it valuable, and integrate it into their workflow.
Step 2: Create Realistic Interaction Flows
The key is making the interactions feel real. In Bubble, I build conditional logic that responds to user actions. If someone rejects an AI suggestion, show them alternatives. If they accept it, demonstrate the downstream effects.
We created scenarios where the "AI" would categorize tasks incorrectly, predict unrealistic timelines, or suggest helpful optimizations. This helped us understand not just when AI worked, but how users wanted to correct it when it didn't.
The breakthrough came when we realized users didn't want AI to automate decisions – they wanted AI to provide better information for their own decisions. This completely changed our product direction.
Step 3: Measure Engagement, Not Accuracy
Traditional AI testing focuses on technical metrics: accuracy rates, response times, error handling. But for product validation, I track behavioral metrics: feature discovery rates, interaction depth, user retention after using AI features.
In Bubble, this is easy to track with custom events and database logs. We measured how often users interacted with AI suggestions, whether they came back to use them again, and how the features affected their overall product usage.
The data revealed something surprising: users loved the AI categorization but ignored the timeline predictions. This insight saved us months of development on features that would have been unused.
Step 4: Test Edge Cases and Error States
Real AI makes mistakes, so your testing should too. I deliberately create scenarios where the "AI" provides bad suggestions, unclear outputs, or conflicting recommendations. How do users react? Can they recover gracefully?
We found that users were remarkably tolerant of AI mistakes – if they understood how to fix them. But they abandoned features completely if error states were confusing or if they couldn't understand why the AI made certain suggestions.
This led us to prioritize explainability over accuracy in our eventual AI implementation.
Quick Validation
Test AI concepts in days not months with realistic user interactions
Real Behavior
Track how users actually engage with AI features before building complex systems
Cost Control
Validate expensive AI development investments with cheap Bubble prototypes
Feature Priority
Discover which AI capabilities users value most through direct interaction testing
After three weeks of Bubble testing with the project management client, the results completely reshaped our AI strategy. Instead of building the comprehensive AI system they originally wanted, we focused on the one feature that users actually engaged with.
The AI categorization feature showed 78% user adoption in our Bubble tests, with users interacting with it multiple times per session. But the timeline prediction feature? Only 12% of users tried it, and most never used it again.
This data saved the client approximately $40,000 in development costs and three months of engineering time. More importantly, it prevented them from shipping AI features that would have confused their user base.
When we eventually built the production version, we implemented only the categorization AI – but with much better user controls and explanation systems based on our Bubble learnings. User adoption of the real feature hit 65% within the first month of launch.
The timeline? From concept to validated AI strategy took less than a month. From there to production took another six weeks. Compare this to the typical "build everything and hope" approach that often takes 4-6 months with uncertain outcomes.
Perhaps most valuable was learning how users wanted to interact with AI suggestions. The Bubble testing revealed that users didn't want AI to make decisions for them – they wanted AI to provide better information for their own decisions.
What I've learned and the mistakes I've made.
Sharing so you don't make them.
Here are the key insights I've gathered from testing AI functionality in Bubble across multiple client projects:
Users can't predict their own AI preferences - What people say they want in surveys bears little resemblance to how they actually behave with AI features
AI errors are features, not bugs - How gracefully users can correct AI mistakes matters more than initial accuracy rates
Context matters more than capability - The same AI feature can be brilliant in one workflow and useless in another
Explainability beats accuracy - Users prefer slightly less accurate AI that they can understand and control
Progressive disclosure works - Introduce AI features gradually rather than all at once
Manual simulation reveals real needs - "Wizard of Oz" prototyping uncovers insights that real AI implementations miss
Behavioral metrics predict success - Feature engagement and return usage matter more than technical performance
The biggest mistake I see? Skipping the testing phase because it feels like "extra work." In reality, Bubble testing prevents much more expensive mistakes later. The few weeks spent in validation saves months in development and years in product-market fit struggles.
This approach works best when you have clear hypotheses about user needs but uncertainty about AI implementation. It's less useful if you're building AI-first products where the entire value proposition depends on AI capabilities.
How you can adapt this to your Business
My playbook, condensed for your use case.
For your SaaS / Startup
For SaaS startups implementing this approach:
Start with one AI feature that solves a clear user pain point
Test with existing users before building for new acquisition
Focus on features that improve existing workflows rather than replacing them
Measure engagement metrics alongside traditional SaaS metrics like retention and expansion
For your Ecommerce store
For Ecommerce businesses exploring AI features:
Test AI recommendations with your actual product catalog
Validate customer service AI with real support scenarios
Focus on AI that drives conversion or increases average order value
Test mobile AI interactions separately from desktop experiences