Energy giant Woodside Energy scales agentic AI systems to augment high-stakes operations

Woodside Energy, a global energy producer based in Western Australia, has spent more than a decade building predictive analytics, optimization systems, and machine learning tools across exploration, drilling, maintenance, and plant operations. The company’s long-term investment in operational data and infrastructure has enabled a transition from traditional AI techniques to agentic systems designed to support complex industrial workflows.

According to Andrew Melouney, Woodside’s vice president for digital, the company’s AI adoption began with high-value use cases in reliability, safety, and efficiency—areas where operational data from equipment and assets provided clear opportunities for improvement. Melouney notes that Woodside has applied analytics, optimization, and predictive models to its datasets since around 2015, establishing a foundation for more recent advances in generative and agentic AI.

The company’s current focus includes deploying agentic AI systems that augment human expertise rather than replace operators. A key example is Woodside’s “Startup Advisor,” an AI copilot designed to assist operators in managing the complex process of starting liquefied natural gas (LNG) plants. Melouney emphasizes that these systems are intended to empower frontline workers to make better and faster decisions in safety-critical environments.

Melouney describes Woodside’s approach as a shift from isolated experiments to enterprise-wide systems built on standardized platforms, governed data, and repeatable deployment patterns. This transition requires rethinking both technology stacks and workflows, with a motto of “Think big, prototype small, and scale fast.” The company’s ambition is to develop an “autonomous enterprise” where AI agents with agency deeply interact with core workflows, though Melouney stresses that human accountability remains central to this vision.

The energy sector’s AI journey contrasts with consumer-facing generative AI tools, focusing instead on operational continuity, safety, and physical infrastructure. Melouney attributes this difference to the asset-intensive, safety-critical nature of energy operations, which demand robust governance and trusted data to ensure reliable and safe outcomes.