In this episode of Great Question: A Manufacturing Podcast, Laura Davis, editor-in-chief of New Equipment Digest, speaks with David Brown, VP and CTO of transportation solutions at TE Connectivity, to unpack how TE is using AI to bring better products to market faster.
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Davis and Brown also chat about how to optimize factory operations with predictive maintenance and robotics and why closing the AI skills gap is critical for the transportation industry's future.
The conversation also explores TE’s approach to building in-house AI tools, practical training programs, and a culture that encourages experimentation, plus why clear goals and good data matter more than shiny new technology.
Below is an edited excerpt from the podcast:
First of all, the establishment of what we call an AI hub, which is a bunch of AI specialists who we've set up, who are serving all of our engineering organization with their expertise. Secondly, we've developed and deployed a companywide AI training program.
So, that's available to all of our engineers. And then lastly, we've rolled out our generative AI solution, which uses our proprietary TE technology, and we call that product “Tell Me.” So, that's now available to all our engineering community.
So really, on the engineering side, there are three very separate pillars which work very nicely together to bring AI capability to our wider engineering organization.
If I flip over to the manufacturing side of our business, that's under the banner of “Better Customer Service.” And what we've been focused on here for a little bit longer than engineering is really, again, how can we bring AI to life across our manufacturing footprint? And similar to our engineering, we've got three major pillars here.
The first one is active analytics. How can programs like predictive maintenance make a difference to our operations? With predictive maintenance, for example, we're targeting a 25% reduction in unplanned maintenance.
Secondly, augmented process automation. So, for us that covers everything from cobots across our manufacturing footprint to AMRs (autonomous mobile robots), which are used for material handling across our various manufacturing locations.
And then thirdly, and similar to our engineering strategy, we have a generative AI component to our operations and manufacturing strategy, again looking at how Generative AI can do things like improve decision making, improve our inventory handling, improve logistics, etc. So, two different but connected initiatives across those two different parts of our company.
Davis: That's a very robust initiative you guys have. I don't hear of many companies having quite a thought-out plan around AI. So that's very interesting.
Brown: It's something which we have to be agile and willing to change. Certainly one thing that's really different about AI compared to other initiatives is the end goal is typically very clear when we start an initiative in the company. With AI—week by week, month by month, the capability of AI is changing. So it's taught us that as we look at AI, we need to be nimble and we need to be very agile.
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Davis: Yeah, it's rapidly changing across any industry. So you do have to be very flexible. I want to touch on a survey you guys recently did: The 2025 Industrial Technology Index. You guys found that 42% of organizations globally lack formal AI training, though 71% of engineers who responded say they're interested in said training. Why do you think there's a gap that persists despite that high interest from engineers?
Brown: Yeah, this comes from our Industrial Technology Index, which is incredibly insightful and we get some really great information from our respondents. Something else that came back from our respondents was that 81% of the engineers who responded said that they think that AI can help them solve complex problems that they face in their jobs every day.
So, there's certainly a very keen appetite to use AI and to understand how to use AI. But yes, going back to the start of the question, 42% of organizations do lack that formal AI training.
