Why Most AI Deployments Stall After the Demo

**Executive summary:** This article explains why AI demos often fail to translate into production in security and IT environments, highlighting practical challenges—messy and fragmented data, latency, edge cases, integration limits, and governance—and provides a concise checklist (use real workflows and data, measure accuracy/latency/reliability, assess integration depth, and define governance) to help teams evaluate and operationalize AI tools successfully.