🧩 The AI Use Case Inventory: The Smallest Governance Move With the Biggest Payoff
Most organizations do not struggle with AI because they lack tools. They struggle because they lack visibility. When we cannot clearly see where AI is being used, what data it touches, and what decisions it influences, we cannot scale adoption confidently. We either freeze, or we let shadow usage spread until trust breaks. An AI use case inventory sounds unglamorous, but it is one of the highest leverage moves we can make. It turns AI from scattered experimentation into a managed capability. ------------- Context: Why AI Gets Messy Fast ------------- AI adoption often begins with good intentions. A team tests a tool for summarizing meetings. Another team uses AI to draft marketing copy. A leader asks for faster reporting. Someone finds an AI feature in an existing platform and switches it on. None of this feels risky in isolation. Then, a few months later, the organization is surprised. People cannot answer basic questions. Which teams are using AI. What tools are in play. Are we putting customer data into third-party systems. Are we relying on AI outputs in decisions that affect customers. Which workflows are automated. Who owns them. The problem is not that AI is uniquely chaotic. The problem is that AI is easy to adopt without coordination. It spreads through convenience. It hides inside everyday tools. It slips into workflows because it saves time, and then it becomes normal before anyone has defined standards. When that happens, leadership tends to react in one of two ways. We either clamp down and restrict everything, which kills momentum and creates resentment, or we ignore it and hope for the best, which creates silent risk. An inventory is the middle path. It does not require perfect policy. It requires honesty. It starts with one simple act: seeing reality clearly. ------------- Insight 1: You Cannot Govern What You Cannot See ------------- Governance often fails because it is built on assumptions. We write rules based on what we think is happening, not what is actually happening. AI makes this worse because usage is distributed and often informal.