Industrial AI Success Demands More Than Intelligence

Jim Chappell, Global Head of AI at AVEVA.

Coming from nuclear engineering and the nuclear Navy, I learned early on that in critical systems, one small mistake can become a serious problem, and industrial AI requires the same discipline. After an outage, a power plant operator unintentionally entered the wrong set point for extraction steam on a steam turbine. The plant continued operating within acceptable limits, so its conventional control system didn’t raise any alarms. This error could have gone unnoticed for months. It would have increased the plant’s carbon footprint and cost them millions.​

AI often creates the most value when it solves a defined operating problem and delivers a measurable outcome. For the plant operator, predictive analytics detected the change in the operating pattern and pinpointed the problem before months of fuel and water were wasted.​

Customer-Specific Intelligence Is Key

Although AI has improved its outputs at a much faster pace than anticipated, some AI models can hallucinate. They may not know how a specific production line behaves under changing supply chain or consumer demand conditions, or why experienced engineers make quick-turn decisions based on years of operational knowledge.​

This is an issue for industries operating safety-critical environments that leverage this type of generative AI. For plant operators, responding with “the model told me” isn’t an acceptable explanation for an operating decision. Teams need to know what evidence produced a recommendation, who reviewed it and who remains accountable for acting on it.​

Back in the 1990s and 2000s, I wrote a lot of C++ code for simulation and expert systems. Some of those systems are still running today because they were built to solve specific operating problems and incorporated the knowledge of the engineers who understood them. The technology has evolved, but the need to combine software solutions with operational context and domain expertise hasn’t.​