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 with a substantial budget. 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 the real purpose of AI MVPs in 2025. The client came excited about the no-code revolution and new AI tools, thinking they could build anything quickly and cheaply. They weren't wrong technically, but their core statement revealed the problem: "We want to see if our idea works."
They had no existing audience, no validated customer base, no proof of demand—just an idea and enthusiasm. Sound familiar?
After six months of deep AI experimentation and working with multiple clients on AI validation, I've learned something counterintuitive: the number of users you need to validate AI isn't what matters. It's what you validate first that determines success or failure.
In this playbook, you'll discover:
Why the "100 users minimum" rule is killing AI startups
My 1-day AI validation framework that saved clients thousands
The hidden validation sequence most founders skip
When to build vs. when to validate manually
Real examples from AI implementation projects that worked
Industry Reality
What the startup world preaches about AI validation
Walk into any startup accelerator or scroll through founder Twitter, and you'll hear the same advice about AI validation: "You need at least 100 users to validate your AI product."
The conventional wisdom goes something like this:
Build a functional AI MVP with core features
Launch to 100+ beta users for meaningful data
Measure engagement metrics like retention and usage
Iterate based on user feedback and behavioral data
Scale when you hit product-market fit indicators
This approach exists because it mirrors traditional software validation. VCs love seeing "traction" in the form of user numbers. Accelerators teach frameworks built for consumer apps. Everyone treats AI like it's just another SaaS tool.
But here's where this falls short in practice: AI products aren't like traditional software. The value isn't in the interface—it's in the intelligence. And intelligence can be validated much faster and cheaper than most founders realize.
The real problem? While you're spending months building and finding 100 users, your competitors are validating core assumptions in days and moving faster to market. The market doesn't wait for your perfect validation process.
Consider me as your business complice.
7 years of freelance experience working with SaaS and Ecommerce brands.
The project that changed my perspective wasn't about user numbers at all. It was about a fundamental misunderstanding of what validation actually means in the AI context.
The client was a B2B startup wanting to build an AI-powered customer service platform. They'd read all the growth hacking blogs, talked to advisors, and were convinced they needed to get their AI model in front of 100+ customer service teams to "properly validate" the concept.
Their plan was methodical: build the AI model, create the interface, recruit beta customers, measure everything. Timeline? Six months minimum. Budget? Substantial. Risk? Enormous.
But when I dug deeper into their assumptions, red flags appeared everywhere. They'd never actually manually solved the customer service problems their AI was supposed to automate. They didn't know if businesses would change their workflows for AI. They hadn't even confirmed that the data quality they needed was available from their target customers.
This wasn't about building better AI—it was about validating completely unproven assumptions. They were treating AI validation like product validation, when what they really needed was business model validation first.
The scariest part? This wasn't unusual. After working with dozens of AI startups, I realized most founders were making the same mistake: optimizing for user quantity instead of learning quality. They were so focused on getting to "enough users" that they never stopped to ask what they were actually trying to learn.
Here's my playbook
What I ended up doing and the results.
Instead of the six-month plan, I proposed something that made my client uncomfortable: What if we validated your core assumptions in one day without building any AI at all?
Here's the exact framework I developed after testing it across multiple AI projects:
Day 1: The Manual Magic Test
Before building any AI, we manually delivered the proposed solution to 3 potential customers. Not 100—just 3. We took their customer service tickets and manually provided the insights their AI was supposed to generate. No algorithms, no models, just human intelligence doing what the AI would eventually do.
Result? 2 out of 3 customers couldn't integrate the insights into their existing workflow without major process changes they weren't willing to make. This took one day to discover, not six months.
The Assumption Stack Method
I realized that AI validation isn't about user volume—it's about systematically testing your stack of assumptions from most critical to least critical:
Value Assumption: Will people pay for this outcome? (Test manually first)
Workflow Assumption: Can they integrate this into existing processes?
Data Assumption: Is the required data quality available and accessible?
AI Assumption: Can AI deliver the outcome better than alternatives?
Scale Assumption: Will this work across multiple customer segments?
Most founders jump straight to assumption #4 or #5. The magic happens when you start with #1 and only move forward when each layer is validated. This approach saved my clients months of development time and thousands in wasted resources.
We tested the first three assumptions with just 5 target customers total—not 100. But the insights were crystal clear because we were testing the right things in the right order. When assumption #2 failed, we pivoted the entire business model before writing a single line of AI code.
Smart Validation
Test assumptions, not interfaces. Start with manual delivery to 3-5 customers before building any AI functionality.
Assumption Stack
Validate value → workflow → data → AI capability → scale, in that exact order. Don't skip levels.
Speed Advantage
Competitors waste months on wrong assumptions while you iterate on the right problem with real users.
Manual First
Your AI's value comes from outcomes, not technology. Prove outcomes manually before automating with AI.
The results speak for themselves. Using this approach across 12 different AI validation projects, here's what actually happened:
Time to validation: Average of 3 days instead of 3-6 months. The fastest took 6 hours.
Success rate: 8 out of 12 projects pivoted or killed their original AI concept after manual validation. Those that continued had much higher success rates because they'd validated the fundamentals first.
Resource savings: Clients saved an average of $50,000-$100,000 in development costs by identifying fatal flaws early. One client saved 8 months of development time.
Market speed: The 4 projects that validated successfully got to market 3x faster than traditional approaches because they'd already proven product-market fit manually.
The most surprising outcome? The projects with the smallest initial user groups (3-5 customers) often provided the clearest validation signals. Larger groups created noise and false positives that delayed real learning.
One client went from idea to paying customers in 6 weeks using this approach, while their competitor spent 8 months building an AI solution for a problem customers didn't actually want solved.
What I've learned and the mistakes I've made.
Sharing so you don't make them.
Here are the top 7 lessons I learned from applying this validation approach across different AI projects:
Manual validation reveals workflow friction that no amount of beta testing will catch. Do the work yourself first.
3 engaged customers > 100 passive users for early validation. Quality of feedback matters more than quantity.
Most AI failures happen at the integration level, not the technology level. Test how your solution fits their process.
Assumption order matters. Validating AI capability before value destroys startups.
Speed is your biggest advantage over well-funded competitors. Use it.
If you can't deliver the outcome manually, AI won't magically make it work.
The best AI MVPs start as manual processes that prove value before automation.
What I'd do differently: I'd focus even more on the workflow integration testing in the manual phase. Some of our pivots could have been identified even faster with better process mapping.
When this approach works best: B2B AI solutions where integration complexity is high and the cost of building wrong is expensive. When it doesn't work: Consumer AI where user behavior is unpredictable and emotional factors dominate.
How you can adapt this to your Business
My playbook, condensed for your use case.
For your SaaS / Startup
Start with 3-5 target customers for manual validation
Test value assumption before building any AI
Map workflow integration early
Validate data quality and access
For your Ecommerce store
Manually deliver AI outcomes to test demand
Focus on process integration over features
Test with existing customer workflows
Scale validation before scaling AI