Building Engineering Teams for Production AI

AI initiatives often stall after proof of concept because organizations lack the engineering capabilities required to operate AI reliably in production. The article explains why AI-ready teams need balanced strength across data engineering, model engineering, platform engineering and application integration. It also highlights recurring failure patterns: weak data foundations, delayed governance, isolated AI teams, limited observability and success metrics focused on demos rather than adoption. Technology leaders should assess AI readiness as a team, platform and operating-model capability, not only as access to AI tools.