6 ways to make AI accountability stick
As intelligent systems move into production environments and begin taking actions, organizations quickly discover that accountability becomes much harder. Unlike traditional enterprise software, these tools can produce unpredictable outcomes as they interact dynamically with data, APIs, and business workflows. “When something goes wrong with AI, it is generally assigned to whoever was closest to the pain point,” says David DuChene, manager of data and AI pre-sales at SHI International, which works with enterprises on AI deployments and governance. As these systems shift from advisor to actor within workflows, accountability becomes harder to enforce through policies alone. IT leaders must build it directly into the fabric of their operations through clear ownership, continuous observability, defined escalation paths, and infrastructure designed to make responsibility visible when things go wrong. Here are six ways to make AI accountability enforceable in production. 1. Assign direct ow