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 two-sided marketplace platform powered by AI. The budget was substantial, the technical challenge was interesting, and it would have been one of my biggest projects to date.
I said no.
Not because I couldn't deliver. Not because the client seemed unreliable. I turned it down because they wanted to "test if their idea worked" by building a complex platform with AI features. This is exactly backwards.
Here's what shocked them: I told them if they're truly testing market demand, their MVP should take one day to build—not three months. The problem wasn't their enthusiasm or their budget. The problem was they had zero validation that anyone actually wanted what they were planning to build.
In this playbook, you'll learn:
Why building before validating kills most AI startups
My simple framework for validating AI MVPs without code
How to test demand manually before investing in development
When (and how) to transition from validation to building
Real examples of what happened when founders skipped validation
Check out our AI strategy playbooks for more insights on building AI products that actually work.
The Reality
What founders get wrong about AI MVPs
The startup world is obsessed with AI right now, and every founder thinks they need an AI MVP to stay competitive. The conventional wisdom sounds logical: build a minimum viable product, get it in front of users, iterate based on feedback.
Here's what most AI startup guides recommend:
Start with a simple AI feature - Pick one core AI capability and build around it
Use no-code tools like Bubble - Leverage platforms to build faster and cheaper
Launch quickly and iterate - Get to market in 2-3 months maximum
Focus on the technology first - Make sure the AI works before worrying about users
Build, measure, learn - Follow the lean startup methodology religiously
This advice exists because it worked for traditional software products. Build something, see if people use it, improve based on data. The problem? AI products are fundamentally different.
Here's where this conventional wisdom falls apart: Even with no-code tools, building an AI-powered platform takes significant time and resources. You're not just building a simple app—you're integrating machine learning models, handling data processing, managing AI inference costs, and dealing with the inherent unpredictability of AI outputs.
More importantly, AI products have a much higher bar for user adoption. People don't just need to like your product—they need to trust it enough to change their workflow around it. That's a massive psychological hurdle that most founders never validate before building.
The result? Beautifully built AI MVPs that nobody uses, sitting in digital graveyards alongside thousands of other "innovative" solutions that solved problems nobody actually had.
Consider me as your business complice.
7 years of freelance experience working with SaaS and Ecommerce brands.
When this client approached me, they had all the classic symptoms of pre-validation enthusiasm. They'd identified a real problem in their industry—inefficient matching between suppliers and buyers in B2B marketplaces. They'd done market research showing the pain point existed. They even had conversations with potential users who said "yes, we'd use something like this."
But here's what they hadn't done: proven that people would actually change their behavior to use their solution.
Their plan was ambitious. They wanted to build a platform where suppliers could upload product catalogs, buyers could describe their needs in natural language, and AI would intelligently match them based on complex criteria including price, location, timing, and past performance. The AI would also predict optimal pricing and suggest negotiation strategies.
"We want to see if our idea is worth pursuing," they told me. That single sentence revealed the fundamental flaw in their approach.
I explained something that initially shocked them: if you're truly testing market demand, your MVP should take one day to build, not three months. Even with AI tools and no-code platforms, what they were describing would require weeks of development, API integrations, data modeling, and testing.
They had no existing audience, no validated customer base, no proof that people would actually use an AI-powered solution instead of their current manual processes. They just had an idea and enthusiasm.
This is the classic startup mistake: confusing validation with building. They wanted to spend months creating something to test whether their idea worked, when they should have been spending days testing whether anyone actually wanted the solution.
Here's my playbook
What I ended up doing and the results.
Instead of taking their money and building what they requested, I walked them through my validation-first framework. This approach has saved multiple clients from expensive failures and helped others find much better product-market fit.
Day 1: Create a Landing Page Test
I had them build a simple landing page explaining their value proposition. Not the AI features—just the core promise: "We connect B2B suppliers with qualified buyers automatically." They used this to test if people understood and wanted the outcome, regardless of how it was delivered.
Week 1: Manual Outreach and Validation
Instead of building algorithms, they manually reached out to potential suppliers and buyers in their network. The goal wasn't to sell anything—it was to understand their current processes, pain points, and willingness to try something new.
Here's the key insight: they discovered most buyers preferred working with known suppliers. The "discovery" problem they thought they were solving wasn't the real bottleneck. The actual problem was inefficient communication and project management between existing relationships.
Weeks 2-3: Manual Matching Process
Rather than building an AI matching system, they manually connected suppliers and buyers via email and WhatsApp. This taught them exactly what information mattered in real matches, what questions buyers actually asked, and where the process broke down.
The manual process revealed something crucial: successful matches required 3-4 back-and-forth conversations to clarify requirements. No AI system could have captured this nuance without first understanding the human workflow.
Month 2: Automated Communication, Not Matching
Only after proving demand did we discuss building automation. But instead of an AI matching platform, we focused on automating the communication workflow they'd validated manually. Much simpler to build, much higher chance of adoption.
This approach follows a simple principle: your MVP should be your marketing and sales process, not your product. Prove people want the outcome before you automate the delivery.
Manual First
Validate demand through human processes before building automation. If you can't make it work manually, AI won't save you.
Outcome Focus
Test whether people want the result, not whether they want your specific AI-powered approach to delivering it.
Workflow Discovery
Manual processes reveal the real complexity that your AI will need to handle—complexity you can't predict in advance.
Constraint Benefits
Building constraints (manual processes) force you to focus on the most valuable features instead of getting lost in technical possibilities.
The outcome validated my approach completely. After two months of manual validation, the client pivoted to a much simpler solution focused on communication workflow automation rather than AI-powered matching.
What they learned through manual validation:
The "discovery" problem they wanted to solve wasn't the real pain point
Buyers preferred working with known suppliers, not finding new ones
The real friction was in project communication and requirement clarification
Successful matches required human judgment that couldn't be easily automated
Instead of building a complex AI platform, they launched a simple communication tool that automated the back-and-forth between buyers and suppliers. Time to first customer: 6 weeks instead of 6 months.
Most importantly, they built something people actually wanted instead of something that sounded innovative. The manual validation process saved them months of development time and revealed a much better product opportunity.
What I've learned and the mistakes I've made.
Sharing so you don't make them.
This experience taught me several crucial lessons about AI MVP validation:
Manual validation reveals real complexity - You can't predict how users will actually interact with your solution until you walk through it manually
AI adds complexity, not simplicity - Every AI feature introduces new variables, edge cases, and user education requirements
People buy outcomes, not technology - Users don't care about your AI unless it clearly improves their current process
Distribution beats product quality - A simple solution that people can easily adopt will always outperform a complex one that requires behavior change
Constraints force focus - Manual processes force you to identify the truly essential features
Validation prevents feature creep - When you validate manually first, you can't hide behind "the AI will handle it" thinking
Time-to-insight beats time-to-product - Learning what users actually want is more valuable than quickly building what you think they want
The biggest lesson? In the age of AI and no-code, the constraint isn't building—it's knowing what to build and for whom. The easier it becomes to build technology, the more important validation becomes.
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 workflows, then automate what works
Test AI value props with simple landing pages first
Validate user behavior change willingness before building
Focus on workflow integration over feature innovation
For your Ecommerce store
For ecommerce businesses considering AI tools:
Test personalization assumptions with manual customer segmentation
Validate chatbot value through live chat data analysis
Prove recommendation engine value with manual product suggestions
Start with simple automation before complex AI features