6 proven lessons from the AI projects that broke before they scaled

Companies hate to admit it, but the road to production-level AI deployment is littered with proof of concepts (PoCs) that go nowhere, or failed projects that never deliver on their goals. In certain domains, there’s little tolerance for iteration, especially in something like life sciences, when the AI application is facilitating new treatments to markets or diagnosing diseases. Even slightly inaccurate analyses and assumptions early on can create sizable downstream drift in ways that can be concerning.In analyzing dozens of AI PoCs that sailed on through to full production use — or didn’t — six common pitfalls emerge. Interestingly, it’s not usually the quality of the technology but misaligned goals, poor planning or unrealistic expectations that caused failure.

Here’s a summary of what went wrong in real-world examples and practical guidance on how to get it right.Lesson 1: A vague vision spells disasterEvery AI project needs a clear, measurable goal. Without it, developers are buil