Artificial intelligence may have captured the public imagination through chatbots and image generators, but some of its most consequential use cases are unfolding far from consumer-facing tools. In industries where physical infrastructure, operational continuity, and safety are paramount, AI is becoming a core operating layer. With its sprawling industrial systems and constant stream of operational data, the energy sector offers a glimpse into what that future could look like.
At Woodside Energy, AI adoption did not begin with generative models or enterprise copilots. The company has spent years building predictive analytics, optimization systems, and machine learning tools across exploration, drilling, maintenance, and plant operations. “We’ve always had very large volumes of operational data coming from the equipment and the plants and the assets that we operate,” says the company’s vice president for digital Andrew Melouney.
“Those have created really clear, quite high-value use cases for us.” That long-term investment in infrastructure and governance is now enabling a broader shift toward agentic AI systems that can support complex industrial workflows. Rather than replace human operators, Woodside designs AI systems to augment expertise in high-stakes environments.
A prime example is its “Startup Advisor,” an AI copilot that helps 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’s approach reflects a wider evolution taking place across industrial AI: graduating from isolated experiments to enterprise-wide systems built on standardized platforms, governed data, and repeatable deployment patterns. That transition, Melouney argues, requires organizations to rethink both their technology stacks and how work itself 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.” Melouney’s motto has become: “Think big, prototype small, and scale fast.” As AI systems become more autonomous and interconnected, the companies poised to succeed may be those that spent years building the operational foundations beneath the hype.
“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,” says Melouney. This episode of Business Lab is produced in partnership with Infosys. Full Transcript: Megan Tatum: From MIT Technology Review, I’m Megan Tatum, and this is Business Lab, the show that helps business leaders make sense of new technologies coming out of the lab and into the marketplace.
This episode is produced in partnership with Infosys. Now, when people think about artificial intelligence, they often picture chatbots or productivity tools, but some of the most sophisticated and high impact uses of AI are actually happening far from consumer apps, inside complex industrial environments where safety, reliability, and physical systems matter.
The global energy sector is a prime example. Companies like Woodside Energy, a global energy producer headquartered in Western Australia, have been applying AI for more than a decade now, from advanced analytics and operations, to remote decision support, to smarter maintenance, and energy efficiency across large scale assets. Today, Woodside is scaling that experience, embedding AI more deeply across its operations and the enterprise with a strong focus on governance, data quality, and human accountability.
Two words for you: technological fuel. My guest today is Andrew Melouney, vice president for digital at Woodside Energy. Welcome, Andrew. Andrew Melouney: Thanks, Megan. It’s great to be here. Megan: Lovely to have you. Now, Andrew, as I said there, the energy sector has approached AI quite differently from technology or consumer businesses.
Early value has emerged in operational and industrial environments, rather than consumer-facing generative AI tools. Why is that? And what differentiates the energy sector’s AI journey? Andrew: Megan, I think it really comes down to the nature of the work we do. Energy operations and what Woodside does is very asset intensive, it’s very safety critical, and it’s highly physical.
And when you think about how Woodside operates, we operate across the full value chain. We do exploration through to drilling and subsurface work, to project development, all the way through to operating assets, which are often operated in harsh and remote locations, and then global energy portfolio marketing and trading as well.
We’ve always had very large volumes of operational data coming from the equipment and the plants and the assets that we operate, and those have created really clear, quite high-value use cases for us. When you think about reliability, when you think about safety and efficiency, those are really critical things for a company like Woodside.
We’ve been doing traditional AI for many years now. If you think about analytics, if you think about optimization, if you think about things like predictive models, those techniques we’ve been applying to our data sets and to our business since around 2015. And more recently with the advent of generative AI, we’ve really found that we’ve got a pretty strong and awesome foundation to build on top of and to really solve problems in the service of improving the business.
And again, whether that is keeping people safe, keeping the environments we operate in safe, or improving returns for the organization. Megan: Fantastic. I mean you touched on it there, but how has this reality shaped your own AI strategy at Woodside? Where did you start, and where did the technology prove most impactful in those early days? Andrew: Well, like I said, we’ve had a very long journey, in terms of understanding our operational data, recognizing the value of it, and collecting it at scale so that we can use it.
And we’ve been very deliberate in that approach, Megan. We’ve really thought about where the value is and where the risks were manageable. And we’ve started looking at, in today’s world from an agentic AI perspective, we’ve started looking at the problems that were solved with traditional AI and machine learning and data science in the past.
And we’ve started to think about, where can we then layer agentic AI over the top to provide an even better outcome? For our asset intensive industry and organization, we’re looking at areas such as maintenance optimization. We’re looking at areas such as, how do we ensure our LNG plants start up reliably, consistently, and safely? And we’re considering really our frontline workforce and making sure that we’re giving people on the frontline the tools required to do their jobs.
When we think about AI, 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? I think over time, this has just evolved from what has been traditional analytics to now artificial intelligence and generative AI. And we’ve learned along the way that the technology is important, but it’s about aligning people, processes, and the technology together.
We’ve spent a long time not only in collecting the data and having a well-curated data set that we can build on top of, but we’ve also spent a lot of time teaching people how to work in agile ways, how to do design thinking, how to problem solve, and how to really make sure that the technology that, say, my team can bring to bear to the organization is adopted effectively and purposefully.
And I think once we had that solid foundation in place from a technology perspective, from a data perspective, once we got strong trust built between our digital teams and the organization, we really saw quite a material uptick a
