Artificial intelligence is rapidly changing industries far beyond the chatbots and image generators that have captured public attention. In sectors critical for physical infrastructure, operational continuity, and safety, AI is quickly becoming a foundational operating layer. The energy sector, with its vast industrial systems and constant flow of operational data, offers a compelling preview of this future.
For Woodside Energy, a global energy producer, the journey into AI wasn’t sparked by generative models or enterprise copilots. Instead, the company has dedicated years to building robust predictive analytics, optimization systems, and machine learning tools across its entire value chain, from exploration and drilling to maintenance and plant operations. Andrew Melouney, Woodside’s Vice President for Digital, emphasizes, “We’ve always had very large volumes of operational data coming from the equipment and the plants and the assets that we operate. Those have created really clear, quite high-value use cases for us.”
Building a Foundation for Autonomous Operations
This long-term commitment to infrastructure and strong data governance is now paving the way for a broader adoption of agentic AI systems that can support complex industrial workflows. Woodside’s strategy focuses on augmenting human expertise in high-stakes environments, rather than replacing it. A standout example is their “Startup Advisor,” an AI copilot designed to assist operators in the intricate 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. This approach highlights a significant evolution in industrial AI: moving from isolated experiments to integrated, enterprise-wide systems built on standardized platforms, well-governed data, and repeatable deployment patterns. This transition necessitates rethinking both technology stacks and operational processes. “We’re not just bolting AI onto an existing process,” Melouney states. “We’re deeply thinking about how that work needs to be reimagined.”
Melouney’s guiding principle, “Think big, prototype small, and scale fast,” encapsulates Woodside’s strategic approach. The companies best positioned for success as AI systems become more autonomous and interconnected will likely be those that have invested years in building solid operational foundations beneath the hype. Ultimately, Woodside’s ambition is clear: “Our ambition is really for an autonomous enterprise, where we have agents with agency that are able to really deeply interact with our core workflows.”
Why Energy’s AI Journey Differs
The energy sector’s unique approach to AI, distinct from technology or consumer businesses, is deeply rooted in the nature of its work. Energy operations are intensely asset-intensive, safety-critical, and highly physical, spanning exploration, drilling, project development, asset operations in harsh remote locations, and global energy portfolio management. This environment has always generated massive volumes of operational data from equipment and plants, presenting clear and high-value use cases for AI.
Andrew Melouney elaborates that for Woodside, critical aspects like reliability, safety, and efficiency are paramount. The company has been applying traditional AI techniques, including analytics, optimization, and predictive models, to its data sets since around 2015. This extensive experience with foundational AI has provided a robust platform for integrating newer generative AI capabilities, enabling them to solve problems that enhance safety, environmental protection, and organizational returns.
Data as a Core Asset: Powering Predictive Maintenance
Data is the absolute bedrock of Woodside’s AI strategy, enabling innovation and operational excellence. The company views its data as an invaluable asset, essential for powering solutions in a sector where real-time decisions, driven by continuous data streams from countless sensors, are crucial. Woodside has made a conscious, multi-year investment in an enterprise-scale data platform, ensuring it is secure, well-structured, and governed with precision. This meticulous approach fosters trust in the data, ensuring that when it’s utilized in AI agents or data science applications, the outcomes are reliable and responsible.
Woodside’s platforms continuously ingest high-frequency data from assets and enterprise systems, enabling the correlation of diverse data sets. A prime example of this is their Maintenance Intelligence solution. This tool analyzes historical maintenance records alongside equipment performance, drawing data from sources like SAP and real-time operational data lakes. By recommending optimal timings for maintenance activities, Maintenance Intelligence helps Woodside “do the right work at the right time,” aiming to reduce maintenance hours by up to 15% over five years on pilot assets.
This decision-support capability empowers asset and operational teams, providing them with the best possible data to exercise their judgment and experience. The goal isn’t to remove human accountability but to enhance it, ensuring better, faster, and more informed decisions. The ongoing evolution from traditional analytics to sophisticated AI, supported by well-curated data and a strong culture of adoption, has allowed Woodside to scale technology effectively across its entire enterprise.
Source: MIT Tech Review – AI
