Over the past several months, I have had the opportunity to speak with leaders across a range of sectors about artificial intelligence. These conversations have taken place in boardrooms, universities, professional development seminars, and informal gatherings following presentations. The contexts vary and the industries differ, however a common pattern has begun to emerge.
The organizations I encounter are not dismissive of AI. Quite the opposite. Most are experimenting with generative tools, reviewing internal processes, or considering policy development. Many have established working groups. Some have launched pilot projects. Others are waiting for clearer regulatory direction before moving further. At first glance, the tone is thoughtful and measured.
Beneath that surface, however, a more subtle but significant governance issue is taking shape.
In this column, I want to discuss three of the most common responses I am hearing when from senior managers, lawyers and executives when discussions turn to responsibility for AI risk. The responses to questions around responsibility for AI initiatives or risk management often take a familiar form: “We have a committee.” “IT says it’s fine.” “We trust our people to use it responsibly.” Each of these statements is reasonable in isolation and signals that attention is being paid. Taken together, however, they reveal a more concerning pattern, namely the diffusion of responsibility across structures, departments, and organizational culture.
AI governance is uniquely prone to this problem. Unlike traditional technology deployments, AI systems sit at the intersection of technical infrastructure, professional judgment, regulatory exposure, and institutional strategy. When accountability is distributed thinly across committees, delegated entirely to technical teams, or left to individual discretion, no single actor retains clear ownership of the risk.
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AI and the Diffusion of Responsibility: Dispatches From the Road
