Building a safer path to autonomous industrial AI

Industrial AI is entering a new phase. After decades of predictive analytics and other specialized applications, advances in foundation models, physical AI, and agentic AI are making it possible to automate more complex tasks across industrial environments. But unlike AI that operates purely in the digital world, industrial AI can interact directly with physical systems, where an unexpected decision can have consequences for safety, reliability, and critical infrastructure.

That makes responsible deployment central to the next wave of industrial automation. “How do we leverage these technologies while maintaining safety, while maintaining reliable operations, while still being able to deliver on the promises of the new capabilities?” asks Arti Garg, chief technologist at AVEVA. The challenge is particularly acute as newer AI systems become more capable but also harder to predict and explain.

One foundation for making that transition work is data. Industrial systems often contain information across telemetry, service logs, engineering documents, and other disparate sources. Newer technologies can help connect and correlate that information more quickly, giving operators real-time support when diagnosing problems. AI-powered robots could take that a step further by gathering information in hazardous environments without requiring workers to enter them.

But greater autonomy also requires new approaches to governance. AVEVA’s framework for responsible AI emphasizes security, efficiency, and human safety and oversight. Garg argues that AI should augment rather than replace people in critical decision loops, with guardrails determining where automated systems can act and where human supervisors remain responsible.

Sustainability is another part of that equation. AI can help manage complex power systems as renewable generation grows, while organizations also need better ways to understand AI’s own environmental footprint. Garg is involved in an IEEE working group developing a standard methodology for measuring that impact across electricity, energy, resources, water, and carbon.

The next phase could bring industrial AI further into the physical world, from autonomous robots and drones to AI-assisted coding that allows domain experts to build new applications. But realizing that potential will require more than deploying new technology, says Garg. Organizations will need to rethink business processes, establish appropriate safeguards, and give experienced workers new ways to apply their expertise, creating a model of automation that is not only more autonomous, but safer, more efficient, and more sustainable.

“Autonomous systems, whether they're robots or drones, are really going to change the way that we work in plants, in power systems, on mining sites,” says Garg. "In a way, that will make these types of operations more efficient, much safer for the human beings involved and more productive.”

This episode of Business Lab is produced in partnership with AVEVA.

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.

Industrial AI may not be new, but it is developing at a breakneck pace. From modern predictive analytics that can preempt equipment failures, to autonomous robots tasked with inspecting high risk machinery, emerging applications have the potential to transform operational efficiencies. But often used in high consequence environments with minimal margin for error, industrial AI deployed without clarity, security, or accountability could also have catastrophic consequences for critical infrastructure over time. As industries rush to embrace the latest technologies, the goal should always be responsible AI designed to support, rather than replace that all important human judgment.

Two words for you: sustainable automation.

My guest today is Arti Garg, chief technologist at AVEVA.

This podcast is produced in partnership with AVEVA.

Welcome, Arti.

Arti Garg: Thank you, Megan. It's great to be here.

Megan: Thank you so much for joining us. And just to start, if we could set some context for our discussion, can you share a bit about the current state of industrial AI and what AVEVA is working on at the moment as well?

Arti: Yeah, more than happy to. And I want to go back to what you already stated, which is that in some ways, industrial AI is not new. It's something that, even here at AVEVA, we've been working on for more than 20 years, really thinking about how AI can enhance applications in the industrial sector.

But what has changed quite substantially in maybe the last few years is the type of AI. We've really moved to these general purpose foundation models, which enable a lot more people to leverage advanced AI technologies. In addition to that, in just the last couple of years, there's been an explosion in what's known as physical AI and the types of AI that can really accelerate the capabilities of robotics and autonomous systems. And also agentic AI, which also allows for automation at more of the software level.

And what we're seeing is that while in the past there has been some hesitancy to adopt models that don't totally have predictable outcomes, there's been a rapid acceleration in adoption of AI even in the industrial sector. One study suggests maybe that's almost a 78% increase over the past just two years within the industrial sector. So yes, that's an inflection point, but it's almost like a step function if you think about it with respect to AI adoption.

And what we're really thinking about now at AVEVA is, how do we think about this? How do we think about incorporating these new and very powerful AI technologies in environments where customers have mission-critical operations, human safety is a significant factor? Those are some of the primary concerns that our customers have when they think about adopting any new technology. We're doing that in narrow where these newer types of AI aren't necessarily predictable, in terms of how they behave. That's one of the areas we're really thinking about is, how do we leverage these technologies while maintaining safety, while maintaining reliable operations, while still being able to deliver on the promises of the new capabilities?

Megan: There's so many things to consider at once, isn't it? And you outlined there some of the huge developments we've seen in this space in recent years. What makes this such a pivotal moment for industrial AI specifically? What has changed to make some of those perhaps more out there hypotheticals much more plausible?

Arti: Well, I think one of the big things, one of the big challenges within industrial AI, and this is something that as AVEVA, it's in our blood, it's in our core DNA, is that to really leverage any kind of digital technology in the industrial space, one of the first important things you often need to do is gather and correlate data from disparate systems, so that maybe I have telemetry coming off of a pump or a mixer, and I want to be able to correlate that with service logs so I know maybe the last time that someone had done some maintenance on that pump or mixer. And maybe I also want to be able to correlate that with the original design documentation and some of the original documentation on how to maintain that, so that if something is happening, I can do a better job diagnosing what's going wrong and how to address it. This has always been a core challenge in the industrial space.

I think that what's happening now is that with newer technologies, even things like graph databases and being able to leverage AI to really match disparate data sets much faster, we're able to get to the point where if something goes wrong, instead of an operator maybe needing to spend a little bit of time doing this research to really understand how do these different components fit together of information, they may have just an iPad with an AI that can very quickly go and fetch that information, correlate it and help in real time, almost, diagnose what is happening.