Why Enterprise AI Fails: It's an Execution Problem, Not a Model One

Enterprise AI projects fail not due to model limitations—GPT-4o, Claude, and Llama 3.1 are all capable of handling enterprise workloads—but because teams apply traditional waterfall planning to inherently probabilistic systems. Understanding the recurring implementation mistakes, from over-scoping to building unnecessary infrastructure from scratch, is critical for any organization deploying GenAI at scale. Leaders who adopt rapid, focused proof-of-concept approaches can dramatically improve success rates and avoid costly, dead-ended investments.