Why AI is not delivering in high-risk industry

Most energy and resources companies are not yet getting measurable results from AI. In Bain & Company’s 2026 survey of 859 executives in oil and gas, utilities, chemicals, mining and agribusiness, fewer than one in five said their companies were scaling AI applications with measurable impact. The bottleneck cited most often was unclear outcomes, with no link to business value, ahead of shortages of technical expertise and poor data availability and governance (Bain & Company, AI in Energy: From Pilots to Payoff, September 2026).

In a high-hazard operation an AI system cannot reach production until management of change, risk assessment and barrier management can accept it; where they have no route for it, it stays a pilot.

Why is AI harder in high-hazard operations?

The US National Institute of Standards and Technology calls AI “a uniquely challenging technology to deploy and utilize” for any organisation (NIST AI 100-1, 2023). Its reasons: the data a model learns from can change over time, sometimes significantly and unexpectedly; AI systems and the settings they run in are complex, so failures are hard to detect and respond to; and AI is socio-technical, shaped by who uses it and how.

In an operation that manages major accident risk, each of these weighs more.

• Data. A process changes with feed quality, equipment wear, catalyst ageing and every modification. Upsets and rare events, the conditions that matter most, are the ones a model has seen least.
• Failure. A wrong output can look plausible. Where the process safety time is minutes, an error that is hard to detect may not be detected in time.
• People. Operators act on what the screen tells them. Reliance grows, manual skills fade, and the control room can stop questioning the system on the day it matters.
• Assurance. Process safety rests on systems that are specified, tested and proof-tested. NIST notes that testing standards for AI are underdeveloped, and the functional safety standards the industry applies, IEC 61508 and IEC 61511, contain no AI-specific guidance.

The United States’ AI Action Plan of July 2025 sets out to “Remove Red Tape and Onerous Regulation”, and still expects AI in safety-critical applications to be “instrumented to detect performance shifts” (The White House).

Elsewhere, an AI error usually means a poor decision or a cost. In a high-hazard operation it can weaken a barrier against a major accident. Many operators therefore keep AI at pilot stage, or deploy it without the controls their safety case assumes.

What does this mean for an operator?

AI delivers when it starts from the operator’s own aims and is built into the management system and processes that already run the plant: management of change, risk assessment, competence, procedures, barrier management, monitoring and records. That is how the M-OPS service range works.

If AI is not delivering in your operation, or you are not sure where to start, contact me.