The Real‑World Conditions That Shape Industrial AI - with Jeff Witt of GE Vernova

Daniel Faggella (0:12)

Welcome everyone to the Emerge AI in Business Podcast. Today's guest is Jeff Witt, Global Process Digital Authority at GE Vernova. Jeff discusses why industrial AI can't be measured the same way enterprise AI is, since the margin for error on a factory floor involves physical safety, not just redone work. He explains how context determines where the frontline workers actually trust and adopt an AI tool and lays out a practical framework for proving a process is ready to automate before scaling it across multiple sites. Today's episode is sponsored by Poka. A quick note for our audience that the views and opinions expressed by Jeff on today's program are his own and do not reflect those of GE Vernova or its leadership. In this episode, we cover what separates industrial AI from enterprise AI. To go deeper on this topic and learn how to evaluate AI vendors by assessing leadership expertise and why funding benchmarks can signal product maturity and stability, download our free PDF report Five Ways to Select the Right AI Vendor at emerge.com/aiv1.

That's emerj.com/aiv1 to download your copy. Now the conversation with Jeff.

Jeff, welcome back to the Emerge AI in Business Studio.

Jeff Witt (1:41)

Thank you for having me. I'm glad to be here.

Daniel Faggella (1:43)

Great conversation waiting for us. So if our listeners remember correctly, the last time we had you on the show, we spoke about computer vision, and this time a very interesting conversation about how we should be thinking about AI in the industrial environment. So when I have conversations about AI outside of this environment, it feels as though most people think that AI is AI, regardless of which direction you're pointing it. So that's where I want to start today, is what is genuinely different about applying AI in an industrial environment versus an office one?

Jeff Witt (2:15)

I think there's two components to this that make it significantly different. So first would be consequences. So if I'm using generative AI or an agent to analyze some sales figures, the worst possible case is that I have to redo it. I have to correct the data. I have to redo some work. If I'm using AI on the shop floor to make recommendations, it potentially could tear down a piece of equipment. If I do something incorrectly, worse than that, I could maybe put a technician right next to an operating piece of equipment, a spindle that's still running.

So there's a safety component, there's a equipment safety impact component that just doesn't exist in the enterprise AI environment. So it is a little bit different. It changes what good looks like, right? So for enterprise AI, you might expect 80% accuracy and be perfectly happy with that.

In the OT space, I can't have a fork truck not hit a pedestrian 80% of the time. That's not good enough, right? So the way it's judged is completely different. So that just adds a whole different layer of rigor to an AI project on the shop floor. Absolutely.

Daniel Faggella (3:34)

So what's the one thing that a software team building AI capabilities or AI programs for offices always gets wrong when they try to apply to a factory? Is there a general assumption?

Jeff Witt (3:46)

I think there's constraints on the architecture that we have in the OT space that are definitely not present in the SaaS subscription space. So I can securely access data in my cloud environments. All I need is the proper SSO login credentials, and I can get access to that data to run a model or to analyze the data.

There's a rather complex architecture on the shop floor that some of our levels of equipment are air-gapped intentionally so that people can't maliciously get into our equipment. So running models in the cloud against systems on the shop floor that might be a control loop that are actually controlling the equipment or controlling a process, we are not going to round trip something to the cloud to analyze or run a model against that sort of data. So just the architecture itself has to be different in the IoT space than it is in the enterprise space. Bringing the model down to the edge and running it locally is usually the solve for that. So instead of round tripping to the cloud, we run edge compute locally to bring that model down a little bit closer to the edge where we have more control over the security.

Daniel Faggella (5:15)

That makes a lot of sense. And I think it's basically the same as building a house for domestic use versus building a big commercial building. It's the same, both AI, but you have to treat it differently, both houses or both buildings, but the safety requirements will also be completely different. So Jeff, this is the part where I try to get more practical, and I'm going to read from my notes here, because a very interesting concept that jumped out to me was that the PwC and Manufacturing Institute study from the spring found that only about a quarter of frontline workers come into AI feeling excited about it. Most of them are very skeptical about it. And over half of leaders aren't confident that their frontline teams are ready to lead through this change and adoption.