Traditional operational risk is well-understood: human error, system failures, process breakdowns. The risk frameworks that organisations have built over decades address these categories effectively.
AI introduces a different category of risk. A language model can produce confident, plausible, and entirely incorrect outputs. An agent can take actions that are technically within its defined scope but contextually inappropriate. These failure modes are harder to anticipate and harder to detect.
This does not mean AI should not be deployed. It means that risk frameworks need to evolve. Testing must include adversarial scenarios. Monitoring must track not just system uptime but output quality. And the organisation must develop the capability to evaluate AI decisions — not just trust them.
Written by
The Orryx advisory team
Orryx is an advisory practice for AI and operational transformation. We work outcome-first and keep a human in the loop — our perspectives come from designing and governing automation in production, not from theory.