Your chief risk officer prepares the quarterly board pack with the usual model performance charts. Accuracy sits at 91 percent and deployment count keeps rising, yet the document never records a single instance where a line manager overrode the output before it affected cash or regulatory filings.
The finance team runs an AI pricing engine that flags margin erosion on three product lines. A commercial manager manually adjusts the recommendations downward to protect a key customer. That adjustment never appears in the risk register because the override logging sits outside the approved platform.
Procurement now relies on an AI vendor scoring tool for mid-tier contracts. When the model downgrades a supplier with known delivery issues, the category lead reverses it to maintain supply continuity. The reversal saves the quarter but leaves the board blind to the actual decision chain.
Audit committees still treat model accuracy as the primary control. They never ask for the frequency or dollar impact of human overrides, so the real exposure stays off the heat map until a regulator or customer complaint surfaces the mismatch.
Melbourne-based firms with listed parents face growing pressure from insurers and ratings agencies on AI governance. Without override trails tied to P&L owners, directors cannot demonstrate they exercised reasonable oversight when a model-driven decision later triggers a claim or restatement.
The fix starts with forcing every production model to log both the raw output and the final human decision in a single immutable record. Risk teams then surface only the overrides above a defined threshold in the board pack, naming the accountable executive and the commercial rationale.
Until that logging becomes non-negotiable, boards will continue signing off on AI spend while the actual decision rights remain with whoever happens to catch the error in the queue.