Most AI products don't fail because of technical limitations. They fail because they solve problems that don't exist, or worse—they solve the wrong problems entirely. This is the uncomfortable truth that the AI industry refuses to acknowledge.
Teams fall in love with their AI capabilities and then search for problems to apply them to. This is backwards. The technology becomes the hammer, and suddenly every business challenge looks like a nail. The result is elegant solutions to problems nobody actually has.
Teams interview stakeholders, collect requirements, and build what was asked for—without understanding why it was asked for. They solve the stated problem without questioning whether it's the real problem. This leads to technically perfect solutions that miss the mark entirely.
Products are designed to impress in 15-minute demos rather than solve real problems in messy production environments. They work beautifully with clean data and controlled scenarios but collapse when confronted with the chaos of actual operations.
Engineers prove they can build something without validating whether anyone will use it or pay for it. The focus is on overcoming technical challenges rather than understanding business constraints, economics, and adoption barriers.
Technical teams build AI products without understanding the domain deeply enough to recognize what matters. They optimize for metrics that sound important but don't drive actual value. They miss the subtle constraints and workflows that determine whether a solution will actually be adopted.
According to Gartner research, 87% of AI projects never make it to production. The primary reason isn't technical—it's problem definition.
Billions are spent annually on AI initiatives that fail to deliver business value because they solve the wrong problems or solve them in the wrong way.
Most AI projects that fail do so within 18 months, after significant investment in development but before achieving meaningful adoption.
The AI industry has convinced itself that implementation is the hard part. It's not. Finding real problems that are worth solving, understanding them deeply enough to solve them correctly, and structuring them in ways that AI can actually address—that's the hard part. Most teams skip this work entirely.
You cannot understand a problem space by reading documentation or conducting user interviews. Real domain expertise comes from years of experience navigating the constraints, politics, economics, and workflows of an industry. Without this expertise, you're building blind.
Consultants and advisors don't have skin in the game. They can afford to be wrong because they're paid regardless of outcomes. Real problem definition requires ownership—the kind that comes from equity participation and long-term commitment to success.
"We spent six months building a perfect recommendation engine before realizing that our users didn't want recommendations—they wanted better search. We solved the wrong problem beautifully."
"Every AI vendor promised to solve our problems. None of them took the time to understand what our problems actually were. They just wanted to sell us their solution."
The solution isn't more sophisticated AI models or better development tools. The solution is starting with real problems from people who understand them deeply—domain experts with years of experience navigating the constraints and workflows that AI must address.
This requires a fundamental shift in how AI products are built. Instead of technical teams searching for problems to solve, we need domain experts defining problems and technical teams implementing solutions. Instead of consultants advising from the sidelines, we need equity-based partnerships where everyone has skin in the game.
This is what Incuba.ai exists to enable. We don't sell ideas. We don't provide advisory services. We create structured use cases from domain experts and match them with technical teams who have the skills to execute. Everyone owns equity. Everyone is accountable for outcomes.
Because ideation is harder than implementation. And AI will amplify whoever owns the problem definition.
Our methodology starts with real problems from domain experts who understand the constraints, economics, and workflows that AI must navigate. No demos. No proofs of concept. Just ownership.