Implementation of AI can be tricky when multiple stakeholders are involved. Wharton George W. Taylor Professor of Management Peter Cappelli spoke with Vivian Sun ENG99 W99 GEN01, senior director for data and AI, enterprise architecture, and IT transformation at electronics manufacturing company Jabil, on Knowledge at Wharton’s Where AI Works podcast about how she transformed her workplace through AI and the unique challenges it poses for manufacturing. This interview has been edited and condensed.
Peter Cappelli: How do changes when we introduce AI really work? What are the effects on employees? What we’ve been learning is, increasingly, that’s where the action is. It’s not the AI per se; it’s this intersection. Did you start out thinking that you would be at this intersection?
Vivian Sun: I never imagined I would be working with artificial intelligence, because back in engineering school [at Penn], there was actually a course called Artificial Intelligence, but we didn’t know much about it.
What always has interested me is managing the change, because we know the only thing that’s not going to change is the change itself. So that’s how I started utilizing AI about five years ago, from a first use case until now, where we are scaling AI solutions for Jabil.
PC: Let’s talk about Jabil for a bit: 140,000 employees, and it’s U.S.-based but all over the world now. You’re a company that makes stuff and helps other companies manufacture stuff. So you’re at the heart of this AI discussion in a company that’s at the heart of the manufacturing conversation. What was the transformation around AI that began this path at Jabil?
VS: As a group, we sat down in the conference room to try to define our strategy. Yes, we want to embrace AI, but where should we start? We identified three different AI technologies we should be concentrating on. The first one is AI computer vision; it’s very widely utilized in the manufacturing environment for inspections, and it’s more deterministic than other AI technologies. The second is machine learning, where we want to understand what the data will bring to us — help us transform, help us conduct preventive maintenance. The third area is generative AI.
PC: The first one was computer vision, optical scanning. What do we mean by that?
VS: It is optical, but it’s using AI models to improve the optical technologies. For example, understanding the color of a product. A traditional optical technology might understand blue versus yellow, but there are subtle differences between more blue or less blue. Human eyes can recognize that, but maybe not traditional optical technology.
We also utilize it in inspecting cosmetic errors. With the products that we make, we hire many inspectors to see if there are scratches, dirt, or dents on the product. Traditionally, we’ve used humans to do that. Now we are utilizing AI in many cases.
PC: One of the interesting things about manufacturing is, unlike other areas trying to use AI, you know the right answer. You know the color that you want. You know what the image should look like, and that makes it conceptually simpler, but you’ve also got to be right.
VS: Exactly.
PC: Walk us through the first execution of this at Jabil when you started with computer vision. What did it look like before, and what did it look like after?
VS: In manufacturing, the visual inspection is a crucial step. Every product has to go through many inspections. Some are testing the capabilities of the machine, making sure it’s functional. And we need to make sure the product cosmetically is up to the quality standard. We make sure the right labels are on the product in the right arrangement. All of those were done previously by people. Although it’s very important, it’s very tedious and tiring. Because of that, it’s difficult to hire people to come here and inspect those products. After we implemented the AI solution, they were able to concentrate on jobs that they’d like to do, and it also improves our inspection qualities and saves time for our HR department.
PC: The psychological process of doing repetitive and boring tasks is called habituation, and that means you just start to tune it out when you see the same thing over and over. It’s not, as you say, something that humans are very good at. So when you introduced this, it was kind of an entryway into AI. Was this because the leadership and your stakeholders could see the benefits of it?
VS: Yes, people can see the result immediately, so they trust us in continuing the pursuing of AI technology into other manufacturing or other core functional teams.
PC: It’s a great point that this is an organizational challenge. It’s not just a technical challenge. You have to persuade the finance department to give you the money for this. You have to persuade the investors that it’s going to be worth the money. But if they can start seeing examples, you’re able to do other things.
VS: AI is really to make people more intelligent. It’s assisting people, but it cannot replace people. So we need to understand where we should apply the technology and where we should not.
PC: What are you working on now in the company that you think within a year or two, we’re going to see a different application, a different innovation?
VS: I think it’s the explosion of AI agents. There are going to be digital employees working next to us. Workday, which is a SaaS software, has actually started to identify AI agents as virtual employees, because just like human beings, the agents that come in to work for a company need to understand the specific terms and the policies. So by identifying that agent in Workday, we’re trying to make them go through the same validation, same training, or same management as a human employee. I believe that’s going to happen, because if you look at how agentic AI is transforming the world, it’s going to start to take over the decision-making. And it’s going to be everywhere — in every single industry, in our work, in our life.
PC: When we’re talking about agents, we’re not talking about robots sitting at desks. We’re talking about large language tools or generative AI tools that do specific things that we can plug into the work stream. It doesn’t necessarily even take the employee out of the chair they’re in now, but it takes over some of those tasks. If you had advice for other companies that are just getting started on this, what would you tell them, from your own experience?
VS: I think number one, start from the business value instead of the technology. We’re trying to use the technology to solve a problem. We’re not trying to build a use case because we want to use the technology. Start small, but think big. Prioritize lower-hanging fruits so we can prove the value to the organization, our employees, and then the customers, but don’t always stay there.
We had to design how the traffic light works and design the traffic laws. So don’t only concentrate on the technology, but look at this whole initiative — whole revolution — as a transformation.
Published as “Start Small, Think Big” in the Spring/Summer 2026 issue of Wharton Magazine.
