Podcast: Creating better products faster with AI-driven design and development

Key takeaways:

AI can transform processes, but it requires more than just deploying new tools to create real value.
Training isn’t just about speed—it’s about making AI relevant and accessible to diverse roles.
Data quality often limits AI’s impact more than the algorithms themselves.
The pace of AI evolution challenges companies to remain flexible in how they apply it.

In this episode of Great Question: A Manufacturing Podcast, Laura Davis, editor-in-chief of New Equipment Digest, speaks with David Brown, vice president and CTO of Transportation Solutions at TE Connectivity, to unpack how TE is using AI to bring better products to market faster, 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 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 tech.

Below is an edited excerpt from the podcast:

NED: Pleasure to have you. So, let's start off with a big picture view of how AI is currently transforming product development and factory operations within the transportation sector.

DB: Yeah. Let me split that into in a two, Laura. I'm going to address them separately because we've got initiatives running in both parts of our business. So first of all, if I start with engineering, we've put our AI engineering initiatives under the banner of “Better Products Faster”. So we're really looking at how AI technology can help us get our product development lifecycle reduced and bring better performing products to our customers. And we came up with a strategy a couple of years ago, and we're well underway in the implementation of that. So just breaking down our engineering strategy, first of all, we broke it into three pieces.

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 company wide 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 of 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.

AI is two letters, but it encompasses so much. It’s so large. And I think we've got to be very focused on the outcomes for the individuals and the outcomes for the organization.
- David Brown

NED: Yeah, 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.

DB: 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.

NED: 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?

DB: 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.

Look, I think there are a couple of things here. Number one, as we've worked across our organization, I think where we've spotted challenges is when we're not focused on clear outcomes. AI is two letters, but it encompasses so much. It’s so large. And I think we've got to be very focused on the outcomes for the individuals and the outcomes for the organization. That's something that we've been spending more time working on recently. I think the second one is simply the wrong language, and what I mean by that is not only should we think about the outcome, but also depending on your target audience, how you use AI and where you use AI needs to be different. And I think there’s a little bit of a little bit of fear around “Is there something that's going to be accessible and relevant to me?” So I think getting it right and focusing on those outcomes is key to improving the effectiveness of training for AI.

NED: My first thought was that, possibly, it's moving so fast and if you don't know much about it, you're like, I don't know where to start. So they're like, I don't how to implement it or even how to train my workforce on it. I like that you said that it's two letters, but encompasses a lot more than that. Do you think there’s then a disconnect between leadership and engineering, or do you think they're likely on the same page? Or is engineering waiting on leadership to do something for them?

DB: Yeah, I don't know if there's necessarily a huge disconnect in that. You know, we've got a clear understanding from most executive leadership that I speak to, that AI is important and is going to make a difference. And I think most of the engineers I work with regularly would say the same thing. AI is important to the future of how we do our jobs here. So I think that part is aligned, but where I think there are some disconnects is understanding how much time is needed.

A lot of training is we're going to do this and in a week you're going to be trained. Or we're going to give you some self-paced training and in a day you’re going to be trained. And I think the gap here is just how long it takes to make effective use of AI. And I think that underestimate is probably worse on the leadership than on the engineering side, where there’s an assumption that this will be something quick, whereas what we really need to do is make consistent investments and to refresh that sort of education program to make sure that it remains relevant.

NED: Yeah, I mean, we use AI on our side and we're constantly learning and reading about it, and trying to find the best way to use it. So I can only imagine when you have applications and data and all of these machines, so that makes a lot of sense.

About the Podcast
Great Question: A Manufacturing Podcast offers news and information for the people who make, store and move things and those who manage and maintain the facilities where that work gets done. Manufacturers from chemical producers to automakers to machine shops can listen for critical insights into the technologies, economic conditions and best practices that can influence how to best run facilities to reach operational excellence.