When Vivian Sun was an engineering student at Penn, she never imagined she would be working with artificial intelligence. There was an AI course that didn’t seem too popular at the time, so she didn’t take it.
Now decades into her career, Sun is at the forefront of the AI revolution at Jabil, an engineering, supply chain, and manufacturing company where she serves as senior director of Data & AI, Enterprise Architecture, and IT Transformation.
“I managed large teams. I managed big operations. But what always interested me is managing the change itself. Because we know the only thing that’s not going to change is the change itself. That’s how I started in AI,” she said.
Sun spoke to Wharton management professor Peter Cappelli for an episode of Where AI Works, a podcast series produced by Wharton in collaboration with Accenture. (Listen to the episode.) She said Jabil first embraced AI five years ago, making it an early adopter. She recalled going into a conference room with a group of colleagues to brainstorm ways to use the emerging technology. The goal wasn’t just to add value to their products, but also to improve the labor process for their employees.
Jabil is a manufacturing company that builds products for clients. Their initial AI efforts were in quality control, a crucial issue in manufacturing. The first effort used machine learning to make sure that the colors they were producing were an exact match with what their customers demanded.
Sun cautioned AI-assisted projects should be designed with scalability and long-term transformation in mind. That initial win led to a broader use of AI. The company used computer vision, which trains AI to analyze visual images quickly and accurately, to assess each product for visual defects, a process that took about 20 seconds per item. The repetitive nature of the task made workers prone to habituation, which often results in inattention to critical details.
“After we implemented the AI solution, we were able to remove some people from those repetitive and non-value [tasks]. They were able to concentrate on jobs that they’d like to do,” Sun said. “It improved our inspection qualities and saved time for our HR department. Now, they don’t have to look for as many inspectors.” But overall, jobs were not cut.
Beyond getting the right colors, Jabil was able to replicate the tacit knowledge of experienced workers who could tell why the colors were off in different contexts. This project was time-consuming, but it highlighted how AI can take over some tasks that relied on human experience developed over time.
“We use the data to analyze the weather information to be able to tell or suggest the correct amount of solutions to guide our workers, so we can get the exact color,” Sun said about refining the colors of some products.
“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.” — Vivian Sun
Sun shared other ways in which Jabil, which has 140,000 employees worldwide, is implementing AI. Following are some key takeaways from the conversation:
Start Small, Think Big
Sun emphasized the importance of beginning with small, manageable AI projects that demonstrate clear business value. She advised companies to anchor AI initiatives in solving real business problems rather than chasing trends.
“I think the No. 1 suggestion is to start from the business value instead of the technology,” she said. “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.”
AI Agents Are Becoming Digital Coworkers
Sun envisions an “explosion of AI agents” that are treated similarly to human employees, undergoing training and validation to align with company policies. These digital coworkers will take over repetitive tasks, make decisions, and interact with both internal and external stakeholders. This shift represents a major transformation in how organizations view and manage their workforce.
“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,” she said. “It’s going to be everywhere, in every single industry, in our work, in our life.”
Generative AI Adds a Layer of Accuracy, Validation
A very different use of AI at Jabil was to deal with the rapid changes in tariffs and other rules for international trade across their businesses. To improve accuracy in assigning international trade codes, Jabil combined machine learning with generative AI. The generative AI acts as a second opinion: First machine learning estimates which code applies to a particular production, then that estimate is validated by querying documents through a chatbot. This layered approach enhances reliability and reduces errors in critical business processes.
“If we get the same answer from the machine learning and also the chatbot, there is a higher chance that it’s a correct one,” Sun said.
AI Enhances Human Intelligence, Not Replaces It
Despite AI’s capabilities, Sun said its role is to augment human decision-making, not eliminate it. Especially in complex or variable scenarios, human judgment remains essential. This balanced perspective helps alleviate fears around job displacement and encourages collaborative human-AI workflows.
“AI is really to make people more intelligent. It’s assisting people, but it cannot replace people in many senses today,” she said.
This article was partially generated by AI and edited (with additional writing) by Knowledge at Wharton staff. Read our AI policy here.
