Artificial Intelligence in Modern Corporate Teamwork

Artificial intelligence agents like chatbots have become significant components of businesses’ customer service strategy, creating more efficient interactions for the organization and consumer. But what happens when companies introduce AI into their internal teams’ workflow?

Stevens School of Business assistant professor Bei Yan, who teaches business analytics and social network analytics courses that are part of the business intelligence & analytics and information systems programs, is studying this phenomenon, investigating how to use new technologies like artificial intelligence to support information sharing and decision-making.

“We have this technology, which is a mechanical agent that works like a human and can solve cognitive tasks,” Yan said. “We're adding it to human collaboration to support it, but maybe it interferes with those collaborations. One thing about team research is that when you have a lot of people working together, the social processes in people's interactions always interfere with the outcomes. There is always something unexpected. So, a new technology, such as an AI meant to support human collaboration, may interfere with the naturally occurring team processes.”

Yan’s exploration into how this rapidly changing technology affects real-world business interactions is just one example of the extraordinary artificial intelligence work women do every day. Giving this work higher visibility is the purpose of the first National Women in AI Month, a joint effort by National Day Calendar and the Cadence Giving Foundation to "showcase women as role models, promote their success in the AI and tech sectors, and encourage women to pursue careers in artificial intelligence technology."

How did you become interested in this line of research?

I have always been interested in technology since I was a kid. In my graduate research, I looked at information technologies in general before I began investigating collaborative, online work enabled by Web 2.0, self-created content and the new information technology that connects people at different locations. In addition to this technological aspect of my work, there has always been a cognitive part. I like to look into how people share information, discuss information, integrate information, create knowledge and reach decisions together. So, there's both this technology and cognitive part, and AI is particularly interesting because it includes both.

Why is understanding AI’s effects important?

AI can affect the collaboration process because people may stop interacting with each other and asking each other for information. For example, teams may gradually develop a shared mental model called a transaction memory system. In naturally interacting teams, they know who has what information, and they can coordinate that information when they do a task together. By relying on AI when looking for information, they may stop developing those naturally occurring team processes that would occur without AI. It actually interferes with the process because people may stop interacting with each other if they rely on AI when trying to seek information. The implication is that when we design AI technologies to implement in teams, we need to be aware of the potential collaborative process interference, which may lead to unintended consequences.

What are some of the practical implications of your research?

When companies try to design AI for teams, the organization’s teams have likely already interacted for a while, so certain norms they’ve already formed prevent them from effectively using AI for better outcomes. Designing training and interventions to help make the AI more effective is necessary, so we are testing different ways to learn how to alter the team’s behavior and have better outcomes, even if the AI is introduced later in their collaboration stage. We're also trying to test it using ChatGPT because Microsoft is already trying to add these generative AI tools in their Teams chat so they can just summarize discussions, point out what they haven't discussed, and then assign tasks to the team members. So, this is a real issue that we're trying to study using new forms of technology.

Why is focusing on women’s contributions to artificial intelligence important?

It is important to have women in this field because women are still underrepresented, which should not be the case. The technologies we're studying aren’t about women or men. It's everyone. Everyone should be able to participate and understand it. Women would bring a different perspective in many ways because of our background and life experience. We can identify some biases or potential caveats in the AI application design.For example, most of the intelligent assistants have women’s voices. It’s strange if you think about it, but they are implying women are assistants. It's not very comfortable. I don't feel comfortable hearing that. Women probably aren’t designing it, and the implicit bias is there.

How do you mentor and support students and colleagues?

When we accept students, gender is not what I take into consideration. What we take into consideration is they're interested and qualified. However, I understand the challenges of being a woman in academia and doing research, so I try to offer more personal and customized support for women who want to pursue this career. I try to encourage them and offer as much support as possible, whether financial advice or tips about work-life balance. I'm an assistant professor on tenure track, so there's a lot of stress to produce. At the same time, I have a one-year-old, so I need to balance many things. My husband is very supportive, but a mother is irreplaceable in many aspects. When I have Ph.D. students who want to have a baby, or they're having a baby and struggling, I understand a lot of the mental stress. The support is not just for students but also among women faculty members in these fields. We try to offer support for each other by applying for grants together or having support groups and workshops.