Focus: AI governance
Who should own the thresholds Deciding what counts as routine versus high stakes is frequently handed to whoever implemented the system, either the software vendor or the IT team managing the integration. Neither group has the day-to-day context to make that call well. Vendors optimise for smooth deployment and broad applicability across customers; IT teams optimise for system stability and security. Operations staff are the ones who understand what a false escalation actually costs on a given line, and what a missed one actually risks. Setting escalation thresholds is closer to setting a quality
tolerance than configuring software. It requires the same plant-specific knowledge that a packaging plant operates very differently from an automotive facility, and that plants can differ meaningfully even within the same industry. Operations leaders should own the thresholds, with IT and vendors supporting implementation rather than setting policy.
Signals that governance is actually working A human-in-the-loop set-up can look functional while quietly providing false comfort. A short set of indicators separates real governance from theatre. The trend in escalation rate over time matters more than the raw number, since a rate that stays flat as a system handles more volume suggests the model is not learning the difference between routine and unusual
Only about one in fi ve
organisations have a mature governance model for autonomous AI agents
cases. Time to resolution on escalated items indicates whether people are engaging seriously or rubber stamping to clear a queue. And the accuracy of the system’s own uncertainty estimates, meaning whether the cases it flags as uncertain are actually the ones that turn out to need correction, is perhaps the clearest test of whether the escalation logic is calibrated at all. Governance maturity across the industry still has room to
grow. Broader enterprise research compiled this year found that only about one in five organisations have a mature governance model for autonomous AI agents, a gap that mirrors what is showing up on the manufacturing floor specifically. Closing it will depend less on better models and more on manufacturers building the organisational muscle to decide, deliberately and on a per-decision basis, when a system should act and when it should ask.
www.electronicsworld.co.uk November 2026 09
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