How To Build Agentic AI That Ships

Enterprise AI initiatives often fail due to integration challenges with people and processes, not technology itself. Common pitfalls include using generic models, unrealistic expectations, pursuing flashy projects, and underestimating coordination complexities. Success is more likely when focusing on practical back-office improvements addressing specific inefficiencies. For instance, using AI to analyze customer survey data could save managers time with minimal risk, compared to ambitious system overhauls with higher failure potential.