The Exchanges

Q So you have as good a window as anybody into how utilities are thinking about this stuff, and, um, as you look at who's best prepared to take advantage of new emerging technologies, um, how, how would you characterize those utilities? I assume that they are utilities who've already been working on this for a while.

A Yeah, and, and what they've been working on, Stephen, is the data. AI is only as good as your data. Uh, that, I mean, that, that's sort of just the, the foundational truth of all of this work, and so the, the utilities that have been on a journey with us that had, you know, especially when I see this in, in utilities that, not just in IT, because IT tends to, like, get it. They, they're experts in this space, and they, they, they, they get the value of the data, but when you see it from, you A CEO and a CFO and a CHRO and your COO that we've had this aha moment that we're going to be a digital company, and that the future of our business in power, wherever you sit, relies on your data. And once you've had that aha moment, if you can start to imbue that, not just in how you enable and empower your IT team, although go do that, they're really important, um, but in how you enable and empower All of your business units and, and that leadership that you provide out to them, we're going to become a digital company. And that, you know, this is happening. Um, but that, that's really the biggest difference that I see in these, in these companies. So the companies that have been on that journey for a while, they've been doing the data work. And so they have these big pools, uh, of, of data. They have well-organized data estates. Um, they're bringing in data from their business processes …

AI assessment note: “what they've been working on, Stephen, is the data.”

Q Hannah Green. I talked with her about how large language models and other forms of AI are making their way inside utilities, and why AI isn't as intimidating as it seems. Let's get into some specific use cases and partnerships, but very broadly, Microsoft has identified hundreds of potential applications, and some of that, some of those we'll talk about coming up. Um, what rises to the top, generally speaking?

A I, I think about it in terms of capabilities, um, of, of this technology, and so it is always important for me to set the foundation that Microsoft has been doing AI work for a long time. Time. We have decades of, of history, um, going back to the early days of the company where we've been working on AI and machine learning. So a lot of what we're going to be talking about today is actually generative AI and this next click in, in innovation around, around generative AI. And so there are four areas broadly where Azure open AI is really finely tuned and they are summarization, semantic search, code generation, and content generation. These really phenomenal images and things like that. That's that content generation machine. And then the way that we're, in addition to the Azure open AI work and co-innovation that we're doing with our customers, we're also pulling those generative AI capabilities into a number of our products, and we're calling those co-pilots. And that's, that's really their function. And what those co-pilots do is they show up as that trusted resource to you in your work to support how you're doing that work more, more effectively, you know, and it's a, it's a tool. At your disposal. And so the areas where I'm seeing customers, you know, sort of broadly apply those are in Predictive analytics. Forecasting. Um, a lot of really exciting forecasting, um, opportuni…

AI assessment note: “they are summarization, semantic search, code generation, and content generation”

Q And, and now in methane detection, there are a lot of people who physically go out in, in the field, and, um, I'm just curious, are, are the tools, are you seeing any benefit in terms of, uh, operational efficiency, or not having to send as many people out in the field, and then accuracy of results?

A Thank you. That, that's such an important point. Um, in many parts, Of the U.S. and even beyond the U.S. globally, the most common way that, that we, um, check for, for, um, just quality of our distribution and transmission and storage systems is still paper, clipboard, and a walk. Uh, and so anything that we can do that, that uses the sensor technology, um, there are, um, you know, a number of vendors today that use drones and fixed-wing aircraft to fly over the systems, but to really have real-time information system-wide to get to that, um, Prevent, uh, detect, remediate prevention space, you need as much of a holistic system-wide point of view as possible, and so, um, it's really going to be about layering those data inputs, and then using the AI and training the models to get out and, and do that preventative maintenance before the leak.

AI assessment note: “anything that we can do that, that uses the sensor technology”

Q How do our journalists get our hands on that tool?

A It's open to external stakeholders, so it's, it is, it's available, uh, if you're, if you're a member of a regulatory proceeding, it's available, too, for, for what they're, they're rolling out, and then, you know, just thinking about, well, what do we do next with it? They're looking at opportunities for it to provide some of that first-round drafting of things, like a GRC, that would otherwise take weeks or months, and boil that down, um, again, it's, it's, this is not the creativity, you And the expert part of the job, it's the task part of the job, so that your experts can do the expert and creative part of, of the job and, and of the highest value part of their work, and I just, we have so much information locked up in different parts of our organizations, and this is just such a powerful way for you to enable your teams and enable your colleagues to tap into that data and that information in a much more collective, transparent, and level way.

AI assessment note: “It's open to external stakeholders, so it's, it is, it's available”