Growth & Strategy
Personas
SaaS & Startup
Time to ROI
Short-term (< 3 months)
Last month, a potential client approached me with an exciting opportunity: build a two-sided marketplace platform with AI features. 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 collecting feedback on AI prototypes in 2025. Most founders I work with get trapped in the same cycle: they spend months building "perfect" AI prototypes, obsessing over model accuracy and feature completeness, only to discover their users don't actually want what they built.
The uncomfortable truth? If you're truly testing market demand, your MVP should take one day to build — not three months. Even with AI and no-code tools, building functional AI prototypes takes significant time. But collecting meaningful feedback doesn't require a finished product.
Through working with multiple AI startups and testing this approach myself, here's what you'll learn:
Why your first AI "prototype" shouldn't be a product at all
The 3-step validation framework I use before writing any code
How to collect 100+ quality insights in your first week
The feedback formats that predict actual user behavior
When to build vs. when to fake your AI features
This isn't about avoiding development — it's about building the right thing. Let me show you how to validate your AI concept before investing months in the wrong solution.
Conventional Wisdom
What every AI founder thinks they need to do
Walk into any startup accelerator or browse through Product Hunt, and you'll see the same pattern everywhere: founders obsessing over their AI prototype's technical sophistication before they've talked to a single user.
The conventional wisdom sounds logical enough:
Build a functional prototype first — "Users can't give feedback on something that doesn't work"
Perfect your AI model accuracy — "If the AI isn't good enough, users won't trust it"
Create a polished demo — "First impressions matter, so make it look professional"
Launch on beta testing platforms — "Get it in front of as many users as possible"
Measure engagement metrics — "Track everything and optimize based on data"
This approach exists because it feels safe and measurable. You can point to your sophisticated neural network, your beautiful interface, your impressive metrics. VCs love demos that "just work." Your team feels productive building features.
But here's what nobody tells you: building first and asking questions later is the most expensive way to collect user feedback. By the time users interact with your "finished" prototype, you've already made dozens of critical decisions about user experience, feature prioritization, and core functionality.
The real problem isn't that your AI isn't sophisticated enough — it's that you're optimizing for the wrong success metrics. User engagement with a prototype doesn't predict real-world adoption. Demo feedback doesn't reveal actual purchase intent. And technical sophistication often masks fundamental product-market fit issues.
Most AI prototypes fail not because the technology isn't ready, but because founders never validated whether users actually wanted the solution they built.
Consider me as your business complice.
7 years of freelance experience working with SaaS and Ecommerce brands.
That potential client I mentioned? They came to me excited about the no-code revolution and AI tools like Lovable. They'd heard these tools could build anything quickly and cheaply. They weren't wrong — technically, you can build a complex platform with these tools.
But their core statement revealed the problem: "We want to see if our idea is worth pursuing."
They had no existing audience, no validated customer base, no proof of demand. Just an idea and enthusiasm. Sound familiar?
This is when I realized I'd been making the same mistake with my own AI experiments. I'd spent weeks building sophisticated prototypes on Bubble, perfecting machine learning workflows, creating beautiful interfaces — all before talking to a single potential user.
The wake-up call came from a simple question I asked this client: "If I could prove your idea works without building anything, would that be more valuable than spending three months on a platform that nobody wants?"
That conversation changed everything. Instead of taking their money to build something impressive but potentially useless, I walked them through what I now call the "validation-first" approach to AI prototype feedback.
The outcome? Within two weeks, they'd collected feedback from 200+ potential users, identified three critical flaws in their original concept, and pivoted to a much simpler solution that people actually requested. They saved months of development time and thousands in budget.
But more importantly for me, this experience revealed a pattern I'd been missing: the best AI prototypes I'd worked on weren't the most technically sophisticated — they were the ones that solved real problems users had already articulated.
Here's my playbook
What I ended up doing and the results.
After that project, I developed a systematic approach to collecting AI prototype feedback that frontloads validation instead of building first and hoping for the best. This isn't about avoiding development — it's about ensuring every line of code serves a validated user need.
Step 1: Problem Documentation, Not Solution Building
Instead of building an AI prototype, I start by creating a simple problem documentation system. This usually takes one day maximum and involves:
A one-page description of the problem you think AI can solve
3-5 user scenarios where this problem creates friction
A mockup or wireframe showing the intended solution (no actual AI required)
A feedback collection method (Google Form, Typeform, or simple email)
The key insight: users can give excellent feedback on problems they understand, even if they can't envision the technical solution. I've collected more actionable insights from showing people a problem description and asking "Does this resonate?" than from any technical demo.
Step 2: Manual Validation Before Automation
Here's where most AI founders get it wrong: they try to automate solutions before proving they work manually. My approach flips this completely.
Instead of building AI that automatically categorizes support tickets, I manually categorize 100 support tickets and document the process. Instead of creating an AI writing assistant, I manually help 10 people write better content and track what actually helps them.
This manual validation phase typically involves:
Direct outreach to 20-50 potential users via LinkedIn or email
15-minute conversations focused on their current process (not your solution)
Manual execution of your proposed AI workflow for willing participants
Documentation of what works, what doesn't, and what they actually need
The magic happens when you can prove your solution works manually before automating it with AI. This approach has helped me identify critical user needs that no amount of technical sophistication could address.
Step 3: Progressive Fidelity Testing
Only after manual validation do I move toward technical prototypes — but I do it progressively. Instead of building the full AI system, I create increasingly sophisticated "fake" versions that simulate the final experience.
This progression typically looks like:
Wizard of Oz testing: I manually provide AI-like responses while users think they're interacting with automation
Static simulation: Pre-written responses that simulate AI intelligence for common use cases
Hybrid system: Simple automation for easy cases, human intervention for complex ones
Full AI implementation: Only after proving each previous level works effectively
The breakthrough insight from this approach: users care about outcomes, not the underlying technology. A "dumb" system that solves their problem consistently beats a "smart" system that works 80% of the time.
Through this framework, I've helped multiple AI startups collect hundreds of user insights before writing their first line of production code. The feedback quality is dramatically better because users interact with the solution concept rather than getting distracted by technical limitations or interface bugs.
Problem First
Document problems users actually have, not solutions you think they need
Solution Faking
Simulate AI behavior manually before building complex automation systems
Progressive Testing
Build sophistication gradually based on validated user needs and feedback
Outcome Focus
Users judge success by results delivered, not by technical complexity behind scenes
The results from this validation-first approach have been consistently impressive across multiple projects. Most recently, I worked with an AI startup that was planning to spend six months building a complex natural language processing system for customer support.
Using this framework, we validated their concept in three weeks:
Week 1: Problem documentation and initial outreach resulted in 150+ responses from potential users
Week 2: Manual validation with 25 companies revealed that 60% needed a completely different solution than originally planned
Week 3: Progressive fidelity testing with 5 pilot companies showed the simplified version achieved 90% of desired outcomes
The most valuable discovery wasn't about their AI technology — it was that users didn't need "intelligent" responses. They needed consistent responses. This insight saved them months of complex machine learning development and led to a much simpler, more profitable solution.
Similar patterns emerged across other AI projects I've consulted on. Companies that used this approach typically collected 3-5x more actionable feedback in their first month compared to those who led with technical prototypes. More importantly, their eventual products had significantly higher adoption rates because they were built around validated user needs rather than assumed requirements.
What I've learned and the mistakes I've made.
Sharing so you don't make them.
After implementing this framework across multiple AI projects, several critical lessons emerged that challenge conventional wisdom about prototype development:
Technical sophistication often masks fundamental problems. The most impressive AI demos often solve problems users didn't know they had.
Manual validation scales better than you think. Insights from 20 manual interactions often predict behavior patterns across thousands of users.
Users judge AI by consistency, not intelligence. A simple system that works reliably beats a sophisticated system that fails 20% of the time.
Feedback quality inversely correlates with technical complexity. The more "finished" your prototype, the less honest feedback users provide.
Problem articulation beats solution demonstration. Users can describe their problems much better than they can evaluate your solutions.
Progressive fidelity prevents feature bloat. Building complexity gradually ensures every feature serves a validated need.
The best AI prototypes often aren't AI-first. They're problem-first solutions that happen to use AI as an implementation detail.
What I'd do differently: I'd start with even less technical complexity and spend more time understanding the context around user problems. The richest feedback comes from understanding not just what users want, but why they want it and what success looks like in their specific situation.
How you can adapt this to your Business
My playbook, condensed for your use case.
For your SaaS / Startup
For SaaS startups developing AI features:
Start with workflow documentation before building automation
Test AI concepts within existing user journeys
Focus on improving current user success metrics
Use progressive rollout to validate AI impact
For your Ecommerce store
For ecommerce stores exploring AI implementation:
Validate personalization needs through customer interviews
Test recommendation logic manually before automation
Focus AI on proven conversion bottlenecks
Measure business impact, not technical metrics