ARC Industry Forum 2024: Industrial AI potential is here, now

Industrial artificial intelligence (AI) insights

• The industrial application of AI, as discussed at the ARC Industry Leadership Forum, highlights the need for a thoughtful approach. While AI can enhance automation, its success relies on a specific plan and contextual understanding, akin to the challenges faced in previous manufacturing concepts.
• The emphasis on expedience, efficiency, and leveraging technology underscores the importance of realistic solutions and collaboration with end-users, ensuring that AI enhances productivity and efficiency in manufacturing.

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Industrial artificial intelligence (AI) was discussed a lot at the ARC Industry Leadership Forum in Orlando. While AI is not a new concept, its rise in the public consciousness over the past year thanks to large language models (LLMs) such as ChatGPT have many wondering what AI can do in an industrial setting.

Industrial AI, as defined by ARC, refers to the application of AI technologies (including generative AI) that augment the workforce to improve growth, profitability, sustainability, production processes and business outcomes.

Sid Snitkin, vice president of ARC Advisory Group, reminded attendees in his presentation “Managing digitalization and AI cybersecurity challenges” that AI is nothing new. At least, not in terms of the fundamentals.

“Many of the things people are saying about AI were in place years ago. There have been tremendous advances in the computing capabilities to do AI,” he said.

In his keynote address, Ray Maxwell, vice president of engineering, ExxonMobil, said, “AI is foundational to the technology suite and is well-positioned to accelerate improvements for manufacturers.”

While there’s concerns about what AI will do to jobs in the same way it was when robots became a stronger presence in facilities, Maxwell said, “In reality, we’re going to need all our engineers and scientists as we continue to liberate our people from spreadsheets and data wrangling.”

Wielding industrial AI to make operations, systems better

While improving automation is nothing new, the process of how to do this has taken a new form with the rise of AI. Like a robot, AI can take the dull jobs (not really dangerous or dirty) and give humans the ability to do the more complex tasks they’re good at.

While that’s important, broad applications of industrial AI won’t work realize their potential without a specific plan and context behind it to make the processes work. Much like preventive maintenance or the industrial Internet of Things (IIoT) the tools are only as good as the persons or company wielding it.

That was true for IIoT, preventive maintenance, digital transformation and every other major manufacturing concept or trend that has come along the last few decades as companies try to improve productivity and efficiency.

During a panel discussion, “Empowering tomorrow: A perspective on AI and cyber resilience,” Joe Bastone, director of Experion product marketing at Honeywell Process Solutions, said, “It is critically important any AI application is explainable. Incorporate all the disparate elements into actionable intelligence. How can we make the system better?”

Expediency and efficiency are critical when it comes to leveraging technology, said Jason Pennington, director of digital solutions at Endress+Hauser. “Can you get the data provided in two clicks or less? If so, what next? For the end user, what do they need to be empowered?”

This is easier said than done, however. Pennington said Endress+Hauser works closely with customers to find realistic solutions, saying, “What’s deployable and possible are two different things.”

During the demo of the Endress+Hauser engineering platform, Pennington said AI can help engineers parse through and make sense of all the data coming in, which can be paralyzing. AI, wielded properly, can help people make sense of what they’re seeing and come to usable conclusions more quickly.

“A lot of the stuff will work if you want it to,” he said.

Industrial AI to get useful answers more quickly

Corey Rinehimer, director of product marketing for Seeq, has integrated generative AI to improve user engagement. The Seeq ecosystem, he said, is designed to accelerate digital transformation and improve connectivity through context.

“We need to process the information in such a way and make things faster. What’s the impact?”

That impact can be improved by capturing tribal knowledge from the engineers already working and feeding the program with the insights and knowledge they have for the workforce of the future.

Through that knowledge, AI can help engineers find insights with an AI assistant tool to find solutions from within their database to help deliver the right solutions. And like anything else, the system gets better the more it is used and as more information is delivered into the system. See an example below.

Making systems more secure

The rise of AI also makes systems more vulnerable to cybersecurity attacks, which was a major concern during the event, as well.

Ted Kerkam, senior director of product strategy for Kepware, said, “Security and connectivity go hand in hand so we use best practices to ensure the connectivity provided can secure connections between operations and enterprise networks.”

Kerkam added the challenge with this is trying to balance that connectivity while making sure manufacturing operations stay efficient and don’t impede operations and, Kerkam said, give customers the freedom from having to worry.

Matt Wiseman, senior product manager for Opswat, discussed process-oriented cybersecurity during a booth visit, saying, “We want to enable operations to be more secure and give access to the files they need while also enabling the supply chain and keeping things running.”

Three things industrial AI applications should do

In his presentation, Mike Carroll, vice president of innovation of Georgia-Pacific, explained how AI should be able to do three things.

1. The ability to learn and the capacity to be taught by others and itself like humans do.

2. The ability to predict outcomes and discover counterfactuals based on acquired knowledge.

3. The ability to reason and make decisions based on that reasoning for a specific purpose.

In a way, manufacturers are already on this path and have been for a while, but the stakes have been raised along with public consciousness. Manufacturers and end users will have to meet the moment. There was a lot of discussion of AI during the ARC Industry Forum, but how they apply industrial AI technologies beyond this event is an open question.

Chris Vavra, web content manager, CFE Media and Technology, cvavra@cfemedia.com.

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