Growth & Strategy
Personas
SaaS & Startup
Time to ROI
Short-term (< 3 months)
Last year, a potential client approached me with an exciting opportunity: build a complex AI-powered marketplace platform. 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 the thing - they came to me excited about the no-code revolution and new AI tools. They'd heard these tools could build anything quickly and cheaply. They weren't wrong technically. You can build a complex AI platform with today's tools.
But their core statement revealed the fundamental 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, enthusiasm, and a budget. Sound familiar? Most founders fall into this exact trap when building AI products.
In this playbook, you'll learn:
Why building to validate is backwards (even with AI)
The 1-day validation framework that saves months of development
How to test AI product demand without writing a single line of code
Real validation techniques that work for AI MVPs
When to actually start building (and what to build first)
Industry Reality
What Every AI Founder Gets Wrong About Validation
Walk into any startup accelerator or scroll through Product Hunt, and you'll see the same pattern everywhere. AI founders are building first and validating later. The typical playbook looks like this:
Get excited about AI capabilities - ChatGPT can do X, so surely there's a business opportunity
Build an MVP with AI features - Spend 3-6 months creating something "innovative"
Launch and hope for traction - Post on social media and wait for users to discover your genius
Pivot when it doesn't work - Blame the market, timing, or competition
Repeat the cycle - Build another AI solution to a different problem
This approach exists because AI tools make building feel so accessible. No-code platforms, AI coding assistants, and drag-and-drop interfaces create an illusion that building is the hard part. It's not.
The real challenge isn't technical - it's figuring out what people actually want before you build it. But here's where most advice falls short: traditional validation techniques don't always work well for AI products.
You can't just create a landing page for an AI solution and expect meaningful validation. People don't know what they want from AI until they experience it. They can't imagine the workflow changes or trust implications until they see it in action.
This creates a validation paradox: you need to build something to validate it, but you shouldn't build until it's validated. Most founders resolve this by just building everything. That's exactly the trap I helped my client avoid.
Consider me as your business complice.
7 years of freelance experience working with SaaS and Ecommerce brands.
The client who approached me had done their homework, or so they thought. They'd researched the market, identified a gap, and even sketched out user flows. The marketplace they wanted to build would connect two sides of their industry using AI-powered matching algorithms.
When they presented their vision, I could see the excitement in their eyes. This was going to be revolutionary. They'd found the perfect problem to solve with the perfect technology at the perfect time.
The red flags started immediately.
"How many people have you talked to about this problem?" I asked. "Oh, we've done extensive market research," they replied, showing me industry reports and competitor analysis. But when I pressed deeper, they'd never actually spoken to a single potential user.
"Have you validated that people want this solution?" Their answer revealed everything: "That's exactly why we want to build the MVP - to test if our idea works."
This is backwards thinking that I see everywhere in AI startups. They want to spend months building a complex platform to validate a hypothesis they could test in days. The real problem? They were treating validation like a single experiment instead of an ongoing process.
I've seen this movie before. I've worked with dozens of startups, and the pattern is always the same. The ones that succeed validate demand before they build. The ones that fail build beautiful solutions to problems that don't exist or that people won't pay to solve.
That's when I made my decision. Instead of taking their money and building their platform, I was going to teach them how to validate properly. It wasn't going to make me rich, but it was going to save them from a much more expensive mistake.
Here's my playbook
What I ended up doing and the results.
Here's exactly what I told them to do instead - and what you should do for any AI product idea:
The 1-Day Validation Framework
Forget about building an MVP. Your first "product" should be your validation process, not your technology. Here's the step-by-step framework I developed:
Day 1: Create a Simple Problem Statement
Instead of building, create a one-page document or simple Notion page that clearly explains:
The specific problem you're solving
Who experiences this problem most acutely
How they currently solve it (if at all)
Your proposed AI-powered solution in simple terms
Week 1: Manual Outreach and Discovery
Start talking to potential users immediately. Not surveys. Not landing pages. Actual conversations. I told them to aim for 20 conversations in the first week with people who experience the problem daily.
Week 2-4: Manual Solution Delivery
This is the crucial part most founders skip: manually deliver your solution before automating it. If you're building an AI matching platform, manually match people via email. If you're building an AI content generator, create content manually for a few clients.
Why does this work? Because it forces you to understand the actual workflow, pain points, and value delivery before you code anything. You learn what features matter and which ones are just nice-to-have.
The AI-Specific Validation Techniques
AI products need special validation approaches because users can't imagine the possibilities until they experience them:
1. The Wizard of Oz Prototype
Create the interface but have humans power the "AI" behind the scenes. Users get the full experience, you get real usage data, and you learn what actually needs to be automated.
2. Progressive AI Integration
Start with rule-based systems that look like AI to users. Add actual machine learning only when you understand exactly what needs to be intelligent and why.
3. The Concierge Approach
Offer the end result as a high-touch service first. Learn the edge cases, understand the quality expectations, then figure out how to scale with AI.
When to Actually Start Building
Only after you can answer these questions with data, not assumptions:
Can you manually deliver value to 10 people?
Are people willing to pay for the manual version?
Do you understand exactly what needs to be automated and why?
Have you identified the smallest possible AI-powered feature that delivers value?
This approach completely flips the traditional MVP methodology. Instead of building to learn, you learn to build. The technology becomes a scaling solution, not a validation experiment.
Real Validation
Manual delivery proves demand before you code anything
Distribution Focus
People need to find your solution before they can use it
Workflow Integration
AI products succeed when they fit into existing workflows seamlessly
Quality Standards
Users expect AI to work better than human alternatives
The outcome validated my approach completely.
My client took the advice and spent one month doing manual validation instead of six months building a platform. Within three weeks, they'd discovered their original idea had a fatal flaw: the two sides of their marketplace had completely different procurement cycles that made real-time matching impossible.
But here's the beautiful part - the manual validation process revealed a much better opportunity. While talking to potential users, they discovered a related problem that people were desperately trying to solve with spreadsheets and manual processes.
Instead of a complex two-sided marketplace, they built a simple AI tool that automated one specific workflow. They went from idea to paying customers in eight weeks instead of building for months with no guarantee of product-market fit.
The validation framework didn't just save them money - it led them to a better business model with clearer value propositions and faster time to revenue. Six months later, they'd grown to $15K MRR with a tool that took three weeks to build instead of the six-month platform they originally envisioned.
This pattern repeats everywhere. The AI startups that succeed are the ones that validate demand before building complex solutions. They understand that in the age of accessible AI tools, the constraint isn't building - it's knowing what to build and for whom.
What I've learned and the mistakes I've made.
Sharing so you don't make them.
Here are the top seven lessons learned from helping founders validate AI products properly:
Validation is a process, not a single test - Keep validating at every stage, even after you start building
Manual delivery teaches you what automation should look like - You can't automate what you don't understand
Users can't imagine AI possibilities until they experience them - Show, don't tell
The biggest risk isn't building the wrong features - It's solving the wrong problem entirely
AI products need workflow integration, not just functional accuracy - Perfect technology that disrupts workflows will fail
Quality expectations for AI are higher than human alternatives - Plan accordingly
Distribution is harder than building - Validate your go-to-market strategy alongside your product
The most important insight? In today's landscape, every AI founder can build. The winners are the ones who validate first. Technical capability is table stakes - market understanding is the competitive advantage.
This validation-first approach works especially well for AI because it forces you to understand the human workflow before trying to augment it with intelligence. Most AI products fail not because the technology doesn't work, but because it doesn't integrate seamlessly into how people actually work.
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 to understand user workflows
Use Wizard of Oz prototypes to test AI interactions
Focus on workflow integration over technology showcasing
Validate pricing for AI-powered features separately from core product
For your Ecommerce store
For ecommerce businesses exploring AI:
Test recommendation engines with manual curation first
Validate customer service automation with human-in-the-loop
Focus on personalization that improves conversion, not just engagement
Ensure AI features work within existing shopping workflows