Failures in complex operations are rarely the result of a single error. They emerge from multiple factors converging, frequently under abnormal operating conditions. What fails is not intent or effort, but assumptions — the unforeseen that was not anticipated.

Yet research into AI safety and long-tail risk shows that rare, high-consequence failures persist and are often difficult to detect at the point of use with “Yet long-tail failures persist, driven by epistemic risk, where AI systems produce confident outputs with no reliable basis for that confidence. The causes are many, but the result is the same: operators can be unwittingly blind to the risk.”

Artificial intelligence materially improves performance across planning, optimisation, and control. The opportunity is real. But in high-risk operations, that performance gain must be matched with proportionate governance.