GoKawiilAndrew: Well, Megan, we've had a philosophy for a long time in Woodside from an innovation perspective, where we really want to think big, we want to prototype small, and we want to scale fast. We want to find big opportunities that we can go after, but we want to ensure that we look at how we deploy those on a small scale first, and then provide the right learning and insight that then can scale it everywhere. Something like maintenance intelligence is a good example of that, or our Startup Advisor, where we know that we've got multiple plants that we need to start up. We know that we've got multiple assets that need to do maintenance, so we have a big, bold ambition about how we can improve and optimize that. We start with a small prototype; it might be one subsystem, it might be just a part of an asset, and then we scale it out, we learn, and we scale faster.
I think from an AI learning perspective, one of the key things we've learned is really the transition from moving from isolated AI solutions to a more coordinated enterprise-wide capability. If you look back maybe 18 months, two years, in our generative AI journey, we rarely started by deploying AI as broadly as we could in the organization from a personal productivity perspective. And probably being quite open in terms of the problems that we will solve, the business problems that we'll solve with AI. That had a lot of benefits for us in terms of allowing our organization to get to know AI, get to know the capabilities, to build the trust in it.
What we've learned though is that we've needed to pivot from that to being a little bit tighter in terms of where we are going to invest our time and resources and more higher value solutions. How do we then enable and empower the rest of the organization so that they can actually effectively problem solve with technology in their domain or in their personal productivity without having to come to a central team?
When we think about that, think big, prototype small, scale fast, has been something really important for us. The transition from a more broader approach to use case development and solution development to now a narrower focus on the high value priorities. We've seen that paying dividends to us and allowing us to go after solutions and opportunities, things like Startup Advisor.
And so our Startup Advisor is a agentic AI solution that really aims to optimize and empower and better support our operators that sit in front of a panel and have to start up LNG plants, which are incredibly technical facilities and require really specialist skills to start up. And so our Startup Advisor is almost like a copilot that sits alongside those operators, and it gives them the ability to be able to play back previous startups. It gives them the ability to look at how the current startup is progressing, and it provides them better insights to optimize how they start up that facility. And again, starting up an LNG facility is incredibly complex.
Megan: I can imagine.
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