PERTH — The hardest part of building an AI for heavy industry is not writing the code. It is convincing the machine to trust the data, and convincing the humans to trust the machine. Woodside Energy has spent years on both.
Andrew Melouney, the company’s vice president for digital, describes a long slog through data architecture and governance before any flashy tools appeared. The payoff is now visible in systems like the “Startup Advisor,” an AI copilot that helps operators restart liquefied natural gas plants.
That process is delicate. One wrong valve sequence can mean hours of delay or a safety incident. The AI does not take over.
It advises. This is the pattern emerging across the energy sector.
Not a robot takeover, but a slow, cautious embedding of machine intelligence into the bones of industrial operations. The public sees generative AI and chatbots. Inside companies like Woodside, the real work has been predictive analytics, optimisation engines, and machine learning models trained on years of sensor readings from drills, pipelines, and turbines.
“We’ve always had very large volumes of operational data coming from the equipment and the plants and the assets that we operate,” Melouney said. “Those have created really clear, quite high-value use cases for us.” The energy industry is not Silicon Valley. It runs on physical infrastructure that must stay online 24 hours a day.
A hospital depends on steady power. A family running a home ventilator depends on it.
The margin for error is zero. That context shapes every decision about AI adoption. Woodside is now moving toward what Melouney calls agentic AI systems.
These are not chatbots that answer questions. They are programs designed to support complex industrial workflows — managing multiple variables, recommending actions, flagging anomalies before they become failures.
The goal is augmentation, not replacement. That distinction matters because the stakes are high. An AI that misidentifies a pattern in drilling data could waste millions.
An AI that gives bad advice during a plant restart could cause an explosion. So the company has focused on systems that work alongside experienced operators, not above them. The Startup Advisor is a concrete example.
Starting a liquefied natural gas plant involves hundreds of steps, each with its own pressure and temperature thresholds. Operators have checklists, but the AI can process real-time data and suggest adjustments faster than a human can.
The operator still makes the final call. This approach reflects a broader trend across heavy industries. Manufacturing, mining, and energy companies are all experimenting with AI, but they are doing it on their own terms.
They cannot afford the hype cycles that drive consumer tech. Their timelines are measured in years, not quarters.
Their failures are measured in injuries, not lost market share. The data infrastructure Woodside built over the past decade is now the foundation for this shift. Without clean, labeled, accessible data, no AI works.
The company invested in that long before anyone was talking about large language models. That investment is now paying off as the technology matures. Melouney described the work as quiet.
It does not make headlines. But for the people who depend on reliable power — the families, the patients, the hospitals — it matters more than most people realise.
The turbines keep spinning. The AI learns to run with them.
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