The most consequential artificial intelligence deployments are not happening inside chatbots or image generators. They are unfolding in industrial environments where operational continuity and safety depend on data, infrastructure and human expertise. Woodside Energy, a global energy producer headquartered in Western Australia, offers a clear example of that shift.

What You Need to Know

Woodside Energy has been applying traditional AI since 2015 for predictive analytics, optimization and machine learning across exploration, drilling and plant operations. The company is now moving toward agentic AI systems that can autonomously support complex industrial workflows. A key example is its Startup Advisor, an AI copilot that helps operators start liquefied natural gas (LNG) plants. The approach emphasizes human augmentation rather than replacement.

Long-Term Investment in Data and Governance

Andrew Melouney, vice president for digital at Woodside, describes the company's AI journey as one built on operational data collected over years from equipment, plants and assets. That data created clear high-value use cases for reliability, safety and efficiency. The company standardized its technology stack and data governance before deploying generative AI more broadly.

This is not a story of bolting AI onto existing processes. Melouney says Woodside is rethinking how work itself gets done. Systems are designed to empower operators to make better, faster decisions without removing human accountability. The goal is a deeply integrated autonomous enterprise.

Why This Matters

Woodside's approach signals a wider evolution in industrial AI: graduating from isolated experiments to enterprise-wide systems built on repeatable deployment patterns. For companies in asset-intensive sectors, the lesson is clear. Without strong data foundations and governance, agentic AI will struggle to deliver value. The energy sector's focus on safety and reliability makes it a proving ground for autonomous systems that could eventually spread to other industries such as manufacturing, logistics and utilities. Organizations that invested early in data infrastructure are best positioned to lead this shift.

The Startup Advisor and Agentic AI

One of Woodside's most advanced systems is the Startup Advisor, an AI copilot designed to help operators navigate the complex process of starting LNG plants. That process involves coordinating dozens of interdependent steps where errors can be costly or dangerous. The Startup Advisor draws on historical data, real-time sensor readings and operator expertise to provide recommendations.

Melouney describes the system as a prime example of agentic AI, where software agents have agency to interact deeply with core workflows. This is not about replacing humans, he explains, but about augmenting them with context-aware intelligence. The same approach is being applied across other operational domains.

Scaling From Prototypes to Enterprise

A notable insight from Woodside's journey is its emphasis on prototyping quickly and scaling fast. Melouney summarizes the company's motto as: think big, prototype small and scale fast. That strategy allows the company to test AI systems in low-risk settings before deploying them across the enterprise. It also helps manage the cultural shift required when work is reimagined around AI.

The company sees its experience as a model for other organizations. Building the foundation for an autonomous enterprise takes time, but the payoff in operational efficiency and decision speed is significant.

  • Predictive analytics: Used for equipment reliability and maintenance.
  • Optimization systems: Applied to drilling and plant operations.
  • Machine learning models: Trained on decades of operational data.

This episode of Business Lab, hosted by Megan Tatum and produced in partnership with Infosys, explored these themes in depth. Melouney and Tatum discussed how industrial AI differs from consumer AI, why governance matters and what the autonomous enterprise will look like. That conversation underscores a broader truth: the companies that succeed with AI are not the ones chasing hype, but the ones building the operational foundations beneath it.