Why AI Projects Fail — and How to Make Yours Succeed

AI projects fail for predictable reasons: unclear success metrics, poor data foundations, and no path to production. In this post we share the delivery framework we use — starting with the business metric, not the model. We cover data readiness assessment, the importance of a thin slice through the stack, and how to build MLOps from week one. The goal is simple: an AI system that keeps delivering value long after the pilot ends.