Artificial intelligence is finding increasingly strategic applications far from chatbots and image generators. In the energy sector, where physical infrastructure, operational continuity, and safety are paramount, AI is becoming a core operating layer. A prime example is Woodside Energy, a global energy producer headquartered in Western Australia, which has invested over a decade in predictive analytics, optimization, and machine learning to improve its operations.
An AI strategy built on operational data and solid governance
According to Andrew Melouney, vice president for digital at Woodside Energy, AI adoption did not start with generative models or enterprise copilots. The company spent years building data infrastructure and predictive analytics systems for exploration, drilling, maintenance, and plant management. "We have always had large volumes of operational data coming from the equipment and plants we operate," Melouney explains. "These have created clear, high-value use cases for us." This foundation now enables a shift toward agentic AI systems that can support complex industrial workflows.
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Startup Advisor: an AI copilot for LNG plant startup
A concrete example is the "Startup Advisor," an AI copilot that helps operators manage the complex process of starting liquefied natural gas plants. The goal is not to replace humans but to augment their expertise in high-stakes environments. "We think about how to support people in the organization to make better, faster decisions," says Melouney. This approach reflects a wider evolution in industrial AI: moving from isolated experiments to enterprise systems built on standardized platforms and governed data. This requires rethinking both technology and work processes. "We are not just bolting AI onto an existing process," he stresses. "We are deeply reimagining how that work needs to be done."
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Melouney's philosophy is: "Think big, prototype small, and scale fast." As AI systems become more autonomous and interconnected, companies that invested in operational foundations are poised to succeed. "Our ambition is an autonomous enterprise, with agents that can deeply interact with our core workflows," Melouney concludes. This approach could reduce emissions and improve efficiency, issues already covered in related articles about AI's environmental impact. For further reading, check the Wikipedia entry on AI in industry.
Source: https://www.technologyreview.com/2026/07/02/1138433/teaching-ai-to-run-with-the-turbines
