Podcast: Best practices for utilizing AI agents in industrial workflows

Thomas Wilk: You’ve got sweeping responsibilities at IBM, especially with Maximo, and I’m so glad you're here. We could talk for 10 podcast episodes, I’m sure, but today we’re going to focus specifically on data-driven asset management—especially AI.

So, let me ask you the first question: When it comes to data-driven approaches, what do you consider best practice for the kind of people we’re speaking to today—the plant managers, operators, especially those in the reliability function?

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Christine Nishimoto: You know, I would say it all comes down to data. As you just mentioned, data is a huge, huge topic—and a huge challenge. As we start to talk more about AI in this conversation, you’ll see that data is at the core of everything that’s happening today.

But it is a huge challenge. I think in the world of manufacturing—and just in general—we’re collecting more and more data and information, whether it's around assets, vibrations, defects, temperatures, or the work people are doing with work orders. There's so much information out there. Some of it is structured, some of it is unstructured—like notes or other formats that are difficult to reach.

I kind of see it as three challenges, and they all start with the letter A. First is accessibility—sometimes it’s just not easy to get to the data. Second is actionable—you might have all this data, but what do you do with it? What does it mean for you? How does it provide any value to make it useful? And third is accuracy—we see a lot of issues there, whether it's someone entering incorrect data because they misunderstood something, or just not bothering to enter it at all.

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We hear about this all the time when talking with customers—you have these work orders where someone just selects a generic dropdown, like "N/A," and it’s just not super helpful when you're trying to find patterns, or some trending thing with an asset, and you can’t see that information because somebody forgot to put that data in there.

So, yes, there are a lot of data challenges with data as a whole. Having the right product—obviously, the right software product—having the right process, the right tools absolutely helps. Having the right people in place to make sure it’s really happening helps.

We believe that you get a lot of value as well tracking from beginning to end, so within my world, we talk a lot about asset and asset management, but we’ve really transitioned the conversation to focus on the entire lifecycle of the asset.

From the moment you’re planning what you need and the financials around that, to receiving the assets, tracking the assets, seeing what’s happening around them, the work around them, everything all the way through to disposal—each one of those steps gives you additional insight.

We’ve started to transform our conversations to be more holistic, as opposed to just managing assets. And we feel that leads to a good foundation for AI, because AI is going to be heavily dependent on having good data to work with.

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TW: There are certain sectors, like power generation, that require five-nines reliability. I’m curious—from your perspective, how are you seeing this approach to asset management permeate other market sectors, like pharma, food, or utilities? Are they all taking cues from power gen, or is it moving through sectors at different rates?

CN: I feel like the impact of AI is hitting all industries. The challenges with data—and the regulations around data—is hitting everywhere. We’re all realizing there’s so much untapped value.

There are opportunities for optimization, for efficiencies, for safety. And there’s a lot of opportunities for things like optimizing power consumption. There’s so much untapped opportunity that we feel it's transforming every aspect of life that we’re looking at, every kind of business.

A couple of examples of that would be to combine power generation and health care, where you look at the facility management. There’s a lot of questions around, can we do maintenance better? Can we do compliance better? Are there ways to optimize energy usage? How do we look at new ways to improve occupancy and repairs?

It’s all rooted in data, in looking at patterns, at making insights actionable. A lot of that is going on regardless of what industry you’re looking at, and there’s so much opportunity based on what’s happening from a technical standpoint—with AI helping as a tool to get there.