Growth & Strategy
Personas
SaaS & Startup
Time to ROI
Medium-term (3-6 months)
Last year, I watched a potential client pitch me their "revolutionary AI-powered platform" that would "transform how businesses operate." The budget was substantial—enough to be one of my biggest projects. But fifteen minutes into their presentation, I knew I had to turn it down.
Here's why: they had no existing audience, no validated customer base, and zero proof of demand. Just an idea, enthusiasm, and the belief that AI automatically equals market success. This is the trap I see 90% of AI startups falling into—they're building solutions without understanding if there's actually a market problem worth solving.
The harsh reality? AI doesn't create market demand. It amplifies existing demand—but only when there's proper solution–market alignment. Through working with multiple AI startups and watching countless projects fail, I've learned that the technology is rarely the problem. The alignment is.
In this playbook, you'll discover:
Why traditional product-market fit frameworks fail for AI projects
The 3-phase validation process I use before building any AI solution
How to identify genuine AI use cases vs. AI-washing opportunities
Real examples of solution–market misalignment and how to avoid them
A practical framework for testing AI solutions before expensive development
Let's dive into why most AI projects are solving the wrong problems—and how to find the right ones. Check out our AI playbooks for more strategic insights.
Industry Reality
What the AI hype machine won't tell you
The AI industry has created a dangerous narrative that goes something like this: "AI is transformational, so any AI product will find its market." This thinking has led to countless failed projects and burned venture capital.
Here's what every AI founder typically hears:
"AI is the future—build it and they will come" - The assumption that AI inherently creates demand
"Focus on the technology first, market second" - Prioritizing impressive AI capabilities over real user problems
"AI can optimize anything" - The belief that every process needs AI enhancement
"Users will adapt to AI workflows" - Expecting people to change their behavior for your technology
"More data equals better solutions" - Assuming computational power solves market alignment issues
This conventional wisdom exists because it's easier to focus on building impressive technology than validating actual market demand. AI demos look sexy in investor presentations. Complex algorithms sound revolutionary. But none of that matters if you're solving a problem that doesn't exist or that people aren't willing to pay to solve.
The truth is, AI amplifies existing market dynamics—it doesn't create new ones. If there's no underlying demand for what you're building, adding AI won't magically generate that demand. In fact, it often makes the misalignment worse by adding complexity to an already questionable value proposition.
Most AI startups fail not because their technology is bad, but because they never established that anyone actually wants what they're building. They fall in love with the solution before understanding the problem.
Consider me as your business complice.
7 years of freelance experience working with SaaS and Ecommerce brands.
Six months ago, a potential client approached me with what seemed like a perfect project. They wanted to build a two-sided marketplace platform powered by AI that would "revolutionize how businesses connect with service providers." The technical scope was interesting, the budget was substantial, and they'd heard about AI tools that could build complex platforms quickly.
But during our initial consultation, they made a statement that immediately raised red flags: "We want to see if our idea is worth pursuing."
They had no existing audience, no validated customer base, and no proof of demand. Just excitement about AI capabilities and a vague belief that their marketplace concept would find its market once built. They were essentially asking me to build a solution to test if a market existed.
This scenario perfectly illustrates the fundamental misunderstanding most AI projects have about solution–market alignment. They're treating AI like a magic wand that will create demand where none exists. The reality is far different.
Through my work with various AI startups and after analyzing dozens of failed AI projects, I've observed a consistent pattern: AI projects fail at solution–market alignment because they start with the technology instead of the market need. They build sophisticated solutions for problems that either don't exist or aren't painful enough for people to pay to solve.
The most common failure modes I've seen include AI solutions that automate processes people don't mind doing manually, platforms that require users to completely change their workflows, and tools that solve problems only the founders think are important. The technology might be impressive, but if there's no genuine market pull, even the most advanced AI will fail.
This is why I told that marketplace client something that shocked them: "If you're truly testing market demand, your first step shouldn't take three months to build—it should take one day."
Here's my playbook
What I ended up doing and the results.
Instead of building that AI-powered marketplace, I recommended what I call the "Manual MVP Approach" for AI solution–market alignment. This approach tests whether there's genuine demand before building any technology.
Here's the exact framework I developed after watching too many AI projects fail:
Phase 1: Market Validation (Week 1)
Rather than starting with AI capabilities, I start with market research. I create simple landing pages or Notion docs that explain the value proposition in plain language—no mention of AI, just the end result. For the marketplace client, this meant describing "faster connections between businesses and service providers" rather than "AI-powered matching algorithms."
Phase 2: Manual Testing (Weeks 2-4)
This is where most AI founders resist, but it's the most crucial step. I manually perform what the AI would eventually automate. For marketplaces, this means manually matching supply and demand via email or messaging. For automation tools, this means doing the automation tasks by hand. If people won't engage with the manual version, they definitely won't engage with the AI version.
Phase 3: Technology Integration (Month 2+)
Only after proving manual demand do I consider building automation. And here's the key insight: the AI should enhance an already-working process, not create a new process.
I also developed what I call the "AI Reality Check Questions":
Would people pay for this solution if it took humans 10x longer to deliver?
Are we solving a vitamin problem (nice-to-have) or a painkiller problem (must-have)?
Does our solution require users to change their existing workflows significantly?
Can we explain the value without mentioning AI or any technical capabilities?
For SaaS AI tools specifically, I focus on the "10x Better Rule"—the AI solution needs to be dramatically better than existing alternatives, not just incrementally better. Users won't switch from familiar tools for marginal improvements, regardless of how impressive the AI is.
The breakthrough came when I realized that successful AI alignment happens when technology serves proven demand, not when we hope demand will discover our technology. This insight completely changed how I approach AI project validation.
Key Insight
AI doesn't create demand—it amplifies existing demand. Validate the manual version first.
Validation Framework
The 3-phase approach: Market research, manual testing, then technology integration.
Reality Check Questions
Four critical questions to ask before building any AI solution.
Manual MVP Success
If people won't engage with the manual version they won't engage with the AI version.
The results of applying this framework have been eye-opening. Of the AI projects I've consulted on using this approach, about 70% discover their original idea has no real market demand during the manual testing phase. This sounds like failure, but it's actually success—we're failing fast and cheap instead of failing slow and expensive.
The 30% that pass all three phases tend to have much stronger solution–market alignment. They enter development with validated demand, clear user workflows, and realistic expectations about what AI can and cannot do.
One particularly telling example: an AI content optimization tool that seemed promising in theory completely failed the manual test. When we tried manually optimizing content for potential users, we discovered they were perfectly happy with their existing tools and workflows. The "problem" we thought we were solving wasn't actually painful enough to motivate behavior change.
Conversely, an AI-powered customer service routing system passed all phases because the manual version revealed genuine frustration with existing solutions and clear willingness to pay for improvements. The AI enhanced something that was already working manually.
The timeline difference is also significant: projects using this framework typically validate or invalidate their core assumptions within 4-6 weeks, compared to the 6-12 months it usually takes to realize a built AI solution has no market fit.
What I've learned and the mistakes I've made.
Sharing so you don't make them.
Here are the seven critical lessons I've learned about AI solution–market alignment:
Technology capabilities ≠ Market demand - Just because AI can do something doesn't mean people want it done differently
AI should enhance existing workflows, not replace them - Users resist dramatic workflow changes, regardless of AI sophistication
Manual validation is non-negotiable - If the manual process has no demand, the automated version will fail
Painkiller problems beat vitamin problems - AI solutions need to solve urgent, expensive problems, not nice-to-have optimizations
User education costs are often underestimated - Complex AI solutions require significant user education investments
Distribution challenges intensify with AI - AI products are harder to explain and demonstrate than simple tools
The "AI-washing" trap is real - Adding AI to existing solutions without clear value addition confuses rather than attracts users
What I'd do differently: I'd be even more ruthless about the manual testing phase. Many founders try to shortcut this step, but it's where the most valuable insights emerge. The discomfort of manual processes often reveals the real user needs that AI should address.
When this approach works best: For B2B AI tools, enterprise automation, and any AI solution targeting existing markets. When it doesn't work: For entirely new market categories or consumer AI products where behavior patterns are still emerging.
How you can adapt this to your Business
My playbook, condensed for your use case.
For your SaaS / Startup
For SaaS startups building AI solutions:
Start with manual processes to validate core value propositions
Focus on 10x improvements over existing solutions, not incremental gains
Test willingness to pay before building AI capabilities
Validate that your AI enhances rather than replaces proven workflows
For your Ecommerce store
For ecommerce businesses considering AI integrations:
Identify customer pain points that manual processes could solve first
Test personalization and recommendation features manually before automation
Ensure AI solutions integrate with existing customer journeys
Measure engagement with manual versions before investing in AI