What Failed AI Projects Taught Us
Everyone talks about successful AI products. Far fewer talk about the projects that never make it to production.
After working on AI initiatives across industries, we've found that most failures have little to do with the model itself. The bigger issues are unclear goals, poor data quality, lack of stakeholder buy-in, and treating AI as a one-time project instead of an evolving system.
A common pattern is teams rushing to build sophisticated solutions before validating the business problem. Another is launching a promising proof of concept without a plan for monitoring, maintenance, and adoption.
The lesson is simple: start with a clear business outcome, focus on data quality, keep the initial solution simple, and design for long-term use from day one.
The AI projects that succeed are rarely the most ambitious. They're the ones that solve a real problem and earn user trust.
In your experience, what's the best way to validate business problem before going deep into building? I see this theme commonly but find testing customer appetite challenging without a real MVP or refined product to present.
That's a challenge we've run into as well.
What has worked best for us is validating the pain before validating the solution. If people aren't already spending significant time, money, or effort on the problem, even a great MVP may struggle.
We usually start with conversations around their current workflow, what they're doing manually, and where the biggest frustrations are. If the same pain point keeps coming up across multiple discussions, that's often enough signal to build a focused proof of concept.
A polished MVP can help validate the solution, but we've found the problem itself should be validated first.