AI in production: What CIOs must change before scaling AI agents
“The mistake many organizations make is preparing AI for production without preparing production for AI.”
A pilot can succeed with limited users, narrow workflows and close supervision. Production is different. Production introduces volume, concurrency, exceptions, outages, retries, partial failures, support queues and business pressure. AI agents will encounter all of that, and they will do so at a speed that traditional operational processes may not be ready to absorb.
This is why CIOs should treat AI readiness as a production discipline. Before scaling AI-enabled workflows, teams should define what normal AI activity looks like, what abnormal behavior looks like and what evidence is required to troubleshoot the difference. They should know which systems an agent can touch, how agent traffic is labeled, how rate limits apply, how errors are escalated and how failed workflows are stopped.
