Woodside Energy has been running AI systems since 2015.
Andrew Melouney, the company’s vice president for digital, says this early focus on traditional analytics and optimization has created a foundation for newer agentic systems. He notes that the firm has always handled large volumes of operational data from equipment and plants, which has generated clear, high-value use cases.
From chatbots to turbines
While the public often focuses on consumer-facing chatbots, Melouney argues that energy operations require a different approach. The work is asset intensive, safety critical, and highly physical. Woodside operates across the full value chain, from exploration and drilling to operating assets in harsh, remote locations.
“We’ve always had very large volumes of operational data coming from the equipment and the plants and the assets that we operate,” Melouney says. “Those have created really clear, quite high-value use cases for us.”
This history means the company could build on a strong base when generative AI arrived. The goal is not to replace human operators but to support them in high-stakes environments.
Startup Advisor
A key example is the “Startup Advisor,” an AI copilot designed to help operators manage the complex process of starting liquefied natural gas (LNG) plants.
“We’re really thinking about, how does it support the people in the organization in terms of empowering them to make better decisions, to make faster decisions,” Melouney explains.
The company is moving from isolated experiments to enterprise-wide systems built on standardized platforms and governed data. Melouney insists that organizations must rethink both their technology stacks and how work gets done.
“We’re not just bolting AI onto an existing process,” he says. “We’re deeply thinking about how that work needs to be reimagined.”
His strategy follows the motto: “Think big, prototype small, and scale fast.”
What it means
Workers in industrial settings now have tools to make faster decisions without losing accountability. The “Startup Advisor” does not take over the job; it provides decision support so operators can keep their roles while handling more complex workflows. The shift is from manual analysis to systems that recommend optimal timing for maintenance and plant startups, reducing hours spent on tasks by up to 15% over five years on pilot assets.
Success depends on treating data as an asset. Woodside has invested for years in a secure, enterprise-scale data platform that ingests high-frequency data from sensors. This allows the company to correlate records from systems like SAP with real-time performance data from time series data lakes.
Agentic AI is now layered over these models to provide better insights. The ambition is an autonomous enterprise where agents interact deeply with core workflows while humans remain responsible for final decisions.
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