Topic
3 essays in this series
AI cannot become a reliable organizational actor while context stays trapped in chats, files, applications, and individual heads. Enterprise memory, decisions, assumptions, evidence, and outcomes, is the missing layer between pilots and AI-native operation.
Most data governance programs were designed to manage data at the pace of policy-making, not the pace of operations. In environments where AI systems are consuming and generating data continuously, that gap is becoming critical.
Every AI initiative runs into the same obstacle around six to eight weeks in, with the same expression of surprise. The models do not work the way the demos suggested — not because the technology is deficient, but because the data is not what the organization assumed it was.