Artificial intelligence is moving beyond chatbots and image generators into the physical world of industrial operations. At Woodside Energy, a global energy producer based in Western Australia, AI has become a core operating layer for managing complex infrastructure, from exploration to liquefied natural gas (LNG) plant startups. The company's vice president for digital, Andrew Melouney, describes a long-term strategy that prioritizes data governance, predictive analytics, and human oversight over flashy consumer tools.

What You Need to Know

Woodside Energy has been applying machine learning and optimization models since 2015, building a foundation for today's agentic AI systems. The company's Startup Advisor copilot helps operators manage LNG plant startups, a high-stakes process where errors can be costly. This approach reflects a broader industrial shift from isolated experiments to enterprise-wide AI platforms governed by strict data quality standards. The goal is an autonomous enterprise where AI agents interact deeply with core workflows while humans remain accountable for decisions.

Building on a Decade of Data

Woodside's AI journey did not begin with generative models. The company has spent years developing predictive analytics, optimization systems, and machine learning tools across exploration, drilling, maintenance, and plant operations. Melouney notes that the energy sector's asset-intensive nature creates clear, high-value use cases for AI. Large volumes of operational data from equipment and plants in harsh, remote locations have driven the need for smarter reliability and efficiency tools.

That long-term investment in infrastructure and governance now enables a broader shift toward agentic AI systems that support complex industrial workflows. Rather than replacing human operators, Woodside designs AI to augment expertise in high-stakes environments. The company's Startup Advisor is a prime example: an AI copilot that helps operators manage the intricate process of starting LNG plants.

Reimagining Workflows, Not Just Bolting On AI

Melouney emphasizes that successful industrial AI requires rethinking 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.” This philosophy has led to a motto: “Think big, prototype small, and scale fast.”

The company's approach reflects a wider evolution across industrial AI: graduating from isolated experiments to enterprise-wide systems built on standardized platforms, governed data, and repeatable deployment patterns. Melouney argues that organizations must rethink both their technology stacks and operational workflows to succeed.

  • Predictive analytics: Woodside uses machine learning models to forecast equipment failures and optimize maintenance schedules.
  • Agentic systems: AI agents interact with core workflows to support decision-making in real time.
  • Human accountability: Operators remain in control, with AI providing recommendations rather than autonomous actions.

Why This Matters

The energy sector's AI adoption offers a blueprint for other industries where physical infrastructure and safety are paramount. As AI systems become more autonomous and interconnected, companies that have invested in operational foundations will be better positioned to scale. For Woodside, the ambition is an autonomous enterprise where AI agents deeply interact with core workflows while humans retain oversight. This model could reduce operational risks, improve efficiency, and lower costs across industrial sectors. The shift from isolated experiments to enterprise-wide AI platforms also signals a maturation of the technology, moving beyond hype into measurable business value.

This episode of Business Lab is produced in partnership with Infosys.