The scenarios below are illustrative composites built from publicly reported patterns of AI-related incidents — not accounts of a specific named organisation — used to demonstrate how certified competence changes the outcome. As real, attributable case studies are developed with certified professionals and partner organisations, they will be published here.
Illustrative scenario: automation complacency in hazard detection
A facility deploys a computer-vision system to flag safety-gear non-compliance on a production floor. Over several months, supervisors increasingly defer to the system's judgment, and manual spot-checks decline. A gradual camera-angle drift degrades detection accuracy without triggering any alert. The gap is only caught during a routine audit — the kind of drift a professional trained to the AISP competency framework is specifically taught to watch for, because the framework treats "the model still runs" and "the model is still accurate" as two different questions.
Illustrative scenario: investigating an AI-assisted near-miss
An AI-based routing system for warehouse vehicles contributes to a near-miss between an autonomous cart and a worker. A investigation needs to reconstruct not just what the vehicle did, but why the model made that specific routing decision, what data it was trained and evaluated on, and whether the human oversight process had a realistic opportunity to intervene. This is precisely the skill set the AIIP certification validates: investigating AI-related incidents with the same rigour applied to any other safety incident, rather than treating "the algorithm decided" as an unexaminable black box.
Have a real case study to share?
If your organisation has a de-identified example of AI safety governance in practice — success or failure — we'd welcome the opportunity to feature it. Contact us.