AI has to be dull before it can be sexy

Enterprise AI adoption is uneven not because of model access, but because of engineering capability gaps. The real challenge is building production-grade AI systems that handle messy enterprise data through proper RAG implementation, evaluation loops, permissions, observability, and memory management. Teams treating AI as a prompt-driven demo will fail in production. The path to successful enterprise AI runs through 'boring' disciplines: data modeling, retrieval quality, chunking strategy, tool constraints, and feedback loops — not flashy multi-agent architectures.