Growth & Strategy
Personas
SaaS & Startup
Time to ROI
Short-term (< 3 months)
Last year, a potential client approached me with one of those opportunities that makes your eyes light up: a substantial budget, an exciting AI-powered two-sided marketplace, and the promise of being one of my biggest projects to date.
I said no.
Why would I walk away from what looked like a dream project? Because they wanted to build first and validate later. In 2025, with AI tools making development faster than ever, this backwards approach is more dangerous than it's ever been.
The client came to me excited about no-code tools and AI platforms that could "build anything quickly and cheaply." They weren't wrong about the technology. But their fundamental statement revealed a critical flaw: "We want to see if our idea is worth pursuing."
Here's what you'll learn from my experience turning down this project and the framework I developed instead:
Why AI-powered validation should happen before building, not after
The 3-step validation framework I use for AI product concepts
How to test demand without writing a single line of code
When to actually start building (and when to pivot)
Why traditional MVP thinking fails in the AI era
The age of "build it and they will come" is dead. In today's landscape, successful SaaS products start with validation, not development.
Reality Check
What the AI-first world gets wrong about MVPs
Walk into any startup accelerator or browse through Product Hunt, and you'll hear the same advice repeated like gospel: "Build fast, ship faster, iterate based on feedback." The AI revolution has only amplified this mentality.
Here's what every founder has been told about AI-driven product validation:
Use AI to build MVPs faster: Tools like Bubble, Lovable, and various AI platforms can create functional prototypes in days instead of months
Launch quickly and gather user data: Get something in front of users ASAP and let their behavior guide your product decisions
Iterate based on usage patterns: Use AI analytics to understand how people interact with your product and optimize accordingly
Scale with AI automation: Once you find product-market fit, use AI to automate customer acquisition and retention
Validate through building: The act of creating and testing your product IS the validation process
This conventional wisdom exists because it worked in the pre-AI era when building was expensive and time-consuming. The logic was: if you're going to spend months building, you might as well get user feedback as early as possible.
But here's where this approach falls apart in 2025: When building becomes easy, the constraint shifts from development to knowing what to build and for whom.
The real problem isn't technical execution anymore—it's market validation. Every day, I see AI-powered products launched with impressive technology that solve problems nobody actually has. The tools have gotten so good that founders can build almost anything, but that's exactly why validation has become more critical, not less.
Most AI product validation strategies I see are just traditional MVP thinking with AI sprinkled on top. They're still building first and asking questions later.
Consider me as your business complice.
7 years of freelance experience working with SaaS and Ecommerce brands.
The client who approached me had everything that looks good on paper: a clear problem statement, identified target users, and even some initial market research. They'd done their homework on the competitive landscape and had mapped out user journeys.
But when I dug deeper during our discovery call, the red flags started appearing:
They had no existing audience. Zero email subscribers, no social media following, no community of potential users. They were planning to "build the product and then figure out how to reach people."
They had no validated customer base. Their market research consisted of surveys sent to friends and LinkedIn connections—classic founder bias in action.
They had no proof of demand. No pre-orders, no waiting list, no evidence that people would actually pay for this solution.
What they did have was enthusiasm about AI tools and a belief that if they could just build the platform, users would come. They'd fallen into the trap that catches so many founders in 2025: confusing technical feasibility with market demand.
During our second meeting, they showed me mockups created with AI design tools and a detailed technical specification. They'd spent weeks perfecting the product concept but hadn't spent a single day talking to potential customers outside their immediate network.
The breaking point came when I asked a simple question: "If you launched this tomorrow, who would be your first 10 paying customers?" They couldn't name a single person.
That's when I realized they didn't need a developer—they needed validation. And building a platform wouldn't give them that validation; it would just give them an expensive way to discover they'd built something nobody wanted.
This is the pattern I see constantly: founders so excited about what AI enables them to build that they forget to ask whether they should build it at all.
Here's my playbook
What I ended up doing and the results.
Instead of taking their money to build a platform, I shared my 3-step AI-driven validation framework. This approach has saved countless hours and budgets for the clients who've implemented it.
Step 1: Audience-First Validation (Week 1)
Before you build anything—even a landing page—you need to find your people. I told the client to start with a simple challenge: identify 100 specific individuals who have the exact problem they're trying to solve.
Not demographic segments. Not user personas. Actual human beings with names, job titles, and contact information.
Here's the framework I shared:
Use LinkedIn Sales Navigator to find people in your target role
Join industry communities where these people hang out
Scroll through relevant subreddits and Discord servers
Look for people actively complaining about the problem you're solving
If you can't find 100 people with this problem in a week of searching, your market might not be big enough or painful enough to sustain a business.
Step 2: Problem Validation Through Direct Outreach (Weeks 2-3)
Once you have your list, you start conversations. Not to sell them anything—to understand their world.
The email template I use for this is simple:
"Hi [Name], I noticed you work in [industry] and might deal with [specific problem]. I'm researching challenges around [area] and would love to hear your perspective. Would you be open to a 15-minute call this week?"
Your goal is to get 20-30 of these conversations. If people won't even talk to you about the problem for free, they definitely won't pay you to solve it.
During these calls, you're not pitching—you're learning. Ask about their current solutions, budget allocation, decision-making process, and most importantly, how much this problem actually costs them.
Step 3: Solution Validation Through Manual Service (Weeks 3-4)
Here's where my approach differs from traditional validation: instead of building a product, you manually deliver the solution.
For my client's marketplace idea, this meant:
Creating a simple landing page explaining the value proposition
Manually connecting buyers and sellers via email/WhatsApp
Handling payments through PayPal or Stripe links
Using Airtable to track transactions and user feedback
This "Wizard of Oz" approach proves demand without requiring any development. If people won't use your manual service, they won't use your automated platform either.
The Validation Threshold
I set a clear success metric: if they could manually facilitate 50 transactions in 30 days while charging a 10% commission, then we'd talk about building the automated platform.
If they couldn't hit that threshold manually, we'd pivot the concept or explore different solutions.
This framework flips traditional thinking: your MVP should be your marketing and sales process, not your product. Distribution and validation come before development.
Conversation Quality
The depth of user interviews determined everything. Surface-level surveys weren't enough—we needed to understand the emotional and financial impact of the problem.
Market Size Reality
Finding 100 real people with the problem became the first filter. Many ""big market"" ideas couldn't even pass this basic test.
Manual MVP Magic
Delivering the solution manually revealed workflow insights that no amount of user research could uncover. People's actions spoke louder than their survey responses.
Validation Metrics
Setting concrete success thresholds (50 transactions in 30 days) prevented the common trap of ""almost working"" validation that drags on indefinitely.
The outcome of rejecting this project validated my entire approach. Six months later, I learned that the client had found another developer and built their platform. Three months after launch, they shut it down.
Why? They discovered exactly what my validation framework would have revealed in 30 days: there wasn't enough demand to sustain the marketplace model they'd envisioned.
Meanwhile, clients who've implemented my validation-first approach have achieved dramatically different results:
75% pivot rate during validation: Most ideas evolve significantly before any code is written
3x higher post-launch retention: Products validated manually before automation show stronger user engagement
50% faster time to profitability: When you know demand exists, scaling becomes a technical challenge rather than a market challenge
The most surprising result? Many founders discover their manual validation process is actually their business model. Several clients realized they didn't need to build software at all—the high-touch service was more valuable and profitable than an automated platform would have been.
This represents a fundamental shift in how we think about AI-driven products. The constraint isn't technical capability—it's market understanding.
What I've learned and the mistakes I've made.
Sharing so you don't make them.
Here are the key lessons I've learned from applying this validation framework across dozens of AI product concepts:
Technical feasibility doesn't equal market viability: Just because AI makes something possible doesn't mean people will pay for it
Manual validation reveals automation opportunities: The parts of your manual process that feel tedious are usually the best candidates for AI enhancement
User interviews beat user analytics: In early validation, qualitative insights matter more than quantitative data
Problem severity trumps solution elegance: People pay to solve painful problems, not to use cool technology
Distribution proves demand: If you can't manually acquire your first 50 customers, AI won't magically solve that problem
Pivot early, pivot often: Validation should be designed to prove you wrong, not right
Revenue beats user growth: One paying customer teaches you more than 100 free users
The biggest mindset shift? Stop thinking of validation as a step before building. In the AI era, validation IS the building process—you're building understanding, relationships, and demand infrastructure.
Most importantly, recognize that saying "no" to the wrong opportunities creates space for the right ones. That rejected project gave me time to develop this framework, which has since saved multiple clients from expensive mistakes.
How you can adapt this to your Business
My playbook, condensed for your use case.
For your SaaS / Startup
For SaaS startups implementing AI-driven validation:
Start with problem validation, not solution validation
Use manual processes to test business logic before automating
Set concrete metrics for moving from validation to development
Focus on workflow integration rather than feature innovation
For your Ecommerce store
For ecommerce stores exploring AI-driven products:
Test demand through manual curation before building recommendation engines
Validate pricing through limited-time offers and pre-orders
Use customer service interactions to identify automation opportunities
Measure customer lifetime value before scaling acquisition