How Vision AI Scales Across a Manufacturing Network - with Jeff Witt

**Daniel Faggella** (0:12)
Welcome everyone to the Emerge AI in Business Podcast. Today's guest is Jeff Witt, a digital transformation leader and a Fortune 500 global leader in building materials and fiberglass composites. Jeff discusses why the majority of computer vision pilots in manufacturing stall before reaching production, and why the bottleneck is consistently organizational rather than technical. The conversation addresses the architectural decisions that allow vision data to integrate with existing manufacturing and enterprise systems, the role of business led ownerships in accelerating deployment across sites, and the practical threshold at which a model is production ready without being perfect. Today's episode is sponsored by Roboflow. Just a quick note for our audience that the views expressed by Jeff Witt are his own. According to Edison Research, 79% of Americans aged 12 and older listen to online audio monthly. That's an estimated 228 million people. For busy executive audiences, podcasts offer a rare opportunity to capture 20 or more minutes of the attention with VP and senior ranking leaders in America's largest enterprises. Emerge reaches 1 million listeners every year. To learn more about how recipes drive pipeline for other AI brands, download our media kit at emerge.com/addone. That's emerj.com/adnumberone.
Now the conversation with Jeff Witt.
Jeff, welcome to Emerge's AI in Business Podcast today.

**Jeff Witt** (1:59)
It's my pleasure to be here.

**Daniel Faggella** (2:00)
I always start off saying I'm excited to have this conversation, but this is one that I'm really excited to have because computer vision is not something that we get to talk about every day. I think there's still a lot of questions and a lot of confusion around it. But I want to start with a failure rate or the failure rate, because it's quite striking and not in a good way.
There's a widely cited figure circulating that around 77 percent of AI vision implementations in manufacturing specifically, never go behind the pilot phase. And this is mostly not because the technology didn't work. It's because they were treated as standard software projects instead of operational transformations. You've been inside a large scale manufacturing transformation for long enough to basically have seen this pattern play out over and over again. What's actually happening when a computer vision project stalls?

**Jeff Witt** (2:52)
I can validate that statistic. I would say we have a majority of our use cases are in pilot or POV status. The ones that make it out are the end to end solutions, like you talked about. It's more about the people and the process than it really is the technology, the AI, the computer vision, picking up the signals, generating the alerts. All of that is rather simple work. It's integrating it into day-to-day operations. So it's relying on some of our other pillars that we work with, workforce enablement, our TPM training, the teams to really ingrain the new processes into operations, into daily management of how they're doing the work on the shop floor.

**Daniel Faggella** (3:37)
It sounds like we do struggle with disconnected systems, disconnected teams, disconnected departments in this idea. At what point in a project does that disconnect usually surface? Is it during discovery, deployment, or somewhere after we go live?

**Jeff Witt** (3:52)
Disconnect is partly an architecture design problem in that the camera systems themselves that we have installed in our install base are diverse. They're usually on the manufacturing IT network, and they're separated from our other data pipelines and BI systems. So when we want to take that manufacturing level data from the vision systems and move it and combine it with other process data that's more in our enterprise systems, that's a barrier that needs to be planned and evaluated and handled early on in the architecture. So that's a big barrier that we faced, was getting that end-to-end integration. Now we have it. Now it's repeatable, it's scalable. It's something that we can deploy remotely. We don't have to go on site to facilitate. So as we continue to scale this across 100 sites, it's not something that needs to be so unique to every individual plant's vision systems.

**Daniel Faggella** (4:57)
Well, that's interesting. Once you have that recipe that works, it's integratable every way. And saying that you've now reached that point where you can go beyond pilot and it's actually working and the transformation has happened.
What is a question that you didn't ask at the beginning of this, that you now think, if I had to go back, I'll start with, I'll start with asking these questions.

**Jeff Witt** (5:18)
So our approach in the beginning was to meet the facilities where they were. So if most of our facilities had some sort of process camera system in place, they were in various levels of maturity with those systems, but most of them had infrastructure already in place. My goal was to take the computer vision, layer that on top of the existing infrastructure to make it more valuable, to unlock more value out of that existing infrastructure. So one of the questions that immediately comes up as soon as we start deploying this and people see and get excited about the possibilities is, great, now I need another camera. Now I need another location. Now I want to monitor this.