Energy giant Woodside Energy describes its shift from predictive analytics to agentic AI systems

Artificial intelligence is reshaping industries where physical infrastructure, operational continuity, and safety are paramount, with the energy sector serving as a leading example of consequential AI use cases beyond consumer-facing tools.

Woodside Energy, a global energy producer, reports that its AI journey began with predictive analytics, optimization systems, and machine learning tools across exploration, drilling, maintenance, and plant operations, rather than with generative models or enterprise copilots.

Vice president for digital Andrew Melouney says the company has long relied on large volumes of operational data from equipment and assets to identify high-value use cases, with early AI efforts dating to around 2015.

Melouney describes a deliberate strategy focused on where value is highest and risks are manageable, leading to the development of systems that augment frontline expertise rather than replace human operators.

A concrete example is Woodside’s “Startup Advisor,” an AI copilot designed to support operators managing the complex process of starting liquefied natural gas plants, enabling faster and better-informed decisions.

The company frames its current phase as a transition from traditional analytics to artificial intelligence and generative AI, with agentic systems layered atop existing predictive models to improve reliability, safety, and efficiency.

Melouney emphasizes that scaling autonomous enterprise workflows requires rethinking both technology stacks and work processes, with governance, data quality, and human accountability as foundational elements.

He summarizes the approach as “Think big, prototype small, and scale fast,” reflecting a focus on incremental, governed deployment patterns across enterprise systems.