When you hear "AI" these days, your mind probably jumps to ChatGPT writing a poem or a Midjourney image of an astronaut riding a horse. And that’s fair—generative AI has completely captured our imagination. It’s flashy, it’s accessible, and it’s changing how we do creative work.
But I want to let you in on a little secret. Some of the most important work in AI is happening far away from our keyboards, in places where a single mistake can have massive consequences. We’re talking about the heavy-duty, high-stakes world of industrial operations.
The energy sector, with its sprawling plants and constant flood of data, is a perfect window into this other side of AI. I recently got a look under the hood at Woodside Energy, a global energy company, and what they’re doing is a masterclass in how to apply AI with purpose, not just for hype.
It All Starts with the Data (Not the Dazzle)
For a company like Woodside, the AI journey didn't start a year or two ago with the launch of a chatbot. They’ve been at this for the better part of a decade. Why? Because they’ve always been swimming in data.
As Andrew Melouney, Woodside's VP for Digital, put it, “We’ve always had very large volumes of operational data coming from the equipment and the plants and the assets that we operate.”
Think about it. A single liquefied natural gas (LNG) plant is covered in thousands of sensors, all streaming information 24/7. For years, Woodside has been building systems to make sense of that data—using predictive analytics, machine learning, and optimization tools to get smarter about everything from exploration to plant maintenance.
This wasn't about building a cool new app. It was about solving real-world, high-value problems. How can we predict when a critical piece of equipment might fail? How can we make our operations safer and more efficient? These are the questions that drove their early AI adoption.
AI as a Copilot, Not a Replacement
This is where things get really interesting. Now that they have this rock-solid data foundation, Woodside is moving into more advanced AI, but their approach is all about augmenting their human experts, not replacing them.
A perfect example is their "Startup Advisor."
Starting up an LNG plant is an incredibly complex and delicate process. It’s not like flipping a switch. It requires deep expertise and intense focus from human operators. So, Woodside built an AI copilot that sits alongside these operators, acting like a seasoned veteran looking over their shoulder.
The Startup Advisor can play back previous startups, analyze how the current one is progressing, and offer insights to help the operator make better, faster decisions. Melouney explains their philosophy this way: “We’re really thinking about, how does it support the people in the organization in terms of empowering them to make better decisions?”
It’s not about taking the human out of the loop; it’s about giving them superpowers. It’s about making a junior operator as effective as someone with 20 years of experience.
From One-Off Projects to an Enterprise-Wide Playbook
This "copilot" approach is part of a bigger shift. For a long time, many companies treated AI like a series of science experiments—a cool project here, a small pilot there. Woodside realized that to get real value, they needed to move from these isolated solutions to a coordinated, enterprise-wide capability.
This is where Melouney’s motto comes in: “Think big, prototype small, and scale fast.”
Let’s break that down.
- Think Big: They identify a huge opportunity, like optimizing maintenance across all their assets or improving the startup process for every single one of their LNG plants.
- Prototype Small: They don’t try to boil the ocean. They start with a small, manageable piece of the puzzle—one subsystem, or a single part of one plant. They build, they test, they learn.
- Scale Fast: Once the prototype is proven and they’ve worked out the kinks, they use that learning to roll it out quickly and confidently across the entire organization.
This playbook is only possible because they’ve done the hard work of standardizing their platforms and creating repeatable patterns. They don’t want to build 50 AI agents in 50 different ways. They want a safe, reliable, and efficient way to deploy these tools so their teams can focus on solving problems, not reinventing the wheel.
The Power of Knowing What to Fix, and When
Another fantastic example of this in action is their "maintenance intelligence" system. Maintenance is a huge part of Woodside’s business. It’s critical for safety and reliability, but it’s also incredibly expensive. The age-old goal is simple: do the right work at the right time.
Easier said than done.
But by having all their data—historical maintenance records from SAP, real-time equipment performance data—in one well-governed place, they can work magic. Their AI analyzes all this information and recommends the optimal time for maintenance activities.
The results are pretty staggering. On one asset where they piloted this, they’re seeing the potential to reduce maintenance hours by up to 15% over five years. That’s a huge win, freeing up people and resources while improving the reliability of the plant.
You Can’t Scale Without Guardrails
As you can imagine, when you’re deploying AI in an environment where safety is everything, you can’t just wing it. Governance is absolutely critical.
Woodside has a structured process for this. Every single AI use case is assessed against a tough set of criteria for privacy, cybersecurity, ethics, and safety. A key question they ask is not just “Could we do this?” but “Should we do this?”
If there are any concerns, the proposal goes to an AI council of senior leaders who debate the risks and rewards. It ensures that every decision is deliberate and well-reasoned. This isn’t about slowing down innovation; it’s about earning the trust needed to go fast, safely. As Melouney says, having this strong governance is "critical for us to go fast."
The End Game: An Autonomous Enterprise?
So, where is all of this headed?
Melouney is pretty clear about the long-term vision. “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.”
That sounds like something out of science fiction, but it’s a logical next step. It’s about moving from AI tools that help with individual tasks to a connected system of agents that can manage entire workflows, freeing up humans to focus on the most complex, strategic challenges.
The goal isn't just efficiency for its own sake. It's about protecting people, safeguarding the environment, and ultimately, providing the energy the world needs more reliably and at a lower cost.
What’s happening at Woodside is a powerful reminder that while the public-facing AI tools are exciting, the real, foundational changes are often happening behind the scenes. It’s a story of patient, long-term investment in data, a deep respect for human expertise, and a clear-eyed vision for how technology can serve the business—not the other way around. It’s a different kind of AI revolution, and it’s already here.
