Podcast: The Impact of AI, Cybersecurity, and Sustainability on Industrial Asset Management

Inderpreet Shoker is the director of research as ARC Advisory Group. As a member of the asset performance management team, Inderpreet leads research initiatives related to APM, asset integrity management, plant asset management, and asset reliability. She has authored and co-authored several Worldwide Market Research reports, as well as researching augmented reality and other extended reality technologies. Inderpreet met up with Anna Townsend, managing editor of Plant Services, at the 2024 ARC Industry Forum in Orlando, Florida, to talk about the changing landscape of asset management and what you can do to get and stay ahead of these disruptions.

Below is the transcript of the podcast:

PS: So the theme for this year's conference is Accelerate Transformation in the Age of AI, Cybersecurity and Sustainability. I’d like to break that down a little bit in terms of what each of those means for asset management and plant maintenance. That's our core readership. So how would you describe the maturity level of AI technologies for asset management? How many companies are ready for them, and can you make an overall assessment for how the industry is doing as a whole?

IS: Absolutely, I think and not only at this forum, I think, in general, we are seeing that to be the theme, AI, sustainability, cybersecurity, to be the theme in industrial manufacturing. And I would say in the last few years as well, that theme continues to go on itself and improve. And the reason is, 10 years ago, if any end user wanted to adopt artificial intelligence, it was hard to do that because you would basically have to be starting from scratch. You would work with an AI company, and then, you would need to hire a bunch of data scientists and build your application from scratch.

I think what has changed in the past few years is that a lot of industrial software providers, they are adding AI capability to their solutions. So what this has done, this has made the adoption really easy for end users, because now they don't have to do anything. That's the industrial software provider that is working to incorporate AI into their existing solutions. And this increases the value proposition of their software solution and simplifies things for end user. I would give an example of field service management solution. FSM software has been around for many years. And if an end user have a lot of equipment, or like a lot of assets spread throughout a geographic location, they would use field service management software to tackle the work orders, assignments, dispatch, and all those things, and it helps them. Over the years, we have seen that FSM providers, now they are incorporating AI into their solutions, so they are including features like route optimization, skills matching, and all these features are now available to end users. So it simplifies adoption for them. For suppliers, it adds value to their solutions. It's like a win-win situation and for both of them. So I think that has really changed how end users look at AI. And that has really helped pick up the adoption of AI for end users in various different industries as well.

PS: Yeah, we heard a lot yesterday in the opening session about the next steps for AI, particularly generative AI and how that's going to reshape industrial automation. But a lot of that still feels so way off in the future. So it's nice to hear those case studies of what people are actually able to do right now. 

But I do want to dive a little bit into the future and the ways that we hope AI will shape us in the future. I do want to cut through a little bit of the hype around artificial intelligence. It's one of those buzzwords you hear everywhere you go, but really using algorithms and data analysis to improve operations or for predictive maintenance, is not new. Operators have been doing that for a long time. We talk about the keys to AI next’s transformation is really using machine learning generative AI applications. So a lot of people that I've talked with about this even before I came describe that transformation as being our copilot. I think we even heard that that term used yesterday. So how does that translate to industrial equipment and asset management? And where do you see AI making the biggest impact in the future?

IS: Absolutely. Anna, what you're hearing is absolutely what I am hearing, as well as what I'm seeing as well. AI was a buzzword. I absolutely agree, but slowly because we have started to see a lot of use cases that take it to the next level. And it's not just a buzzword anymore. And I think in the industry, one of the biggest challenge we have is skills gap. A lot of boomers are retiring, and as they're retiring, they're taking all their knowledge away from the industry. And we need to fill that skills gap, and technology and tools are helping us bridge that skills gap. 

We have tried to capture the data from various different devices for a very long time, and that is going on well, but that only solves a part of the problem. You are capturing the data but how do we retrieve that data? How do we access that data? That still remains a problem. And I think this is the area where generative AI, or you can say copilot or AI-enhanced assistance, which really helps. Because when a field worker is out there, although they have a lot of data available, how do they access that right data at the right amount of time? I think that's where GenAI is going to really help push and solve the skills gap problem from both ends.

Anna Townshend | Managing Editor