One of our global beverage clients regularly brings GenAI into her team’s conversations, asking it questions about risk, compliance, and sustainability to gain new perspectives and challenge the group’s thinking. “This has been my best friend for the past six months,” the chief innovation officer said. “It helps me structure discussions on the spot.”

This leader isn’t alone. Our recent interviews with senior executives and managers signal that they’re increasingly using generative artificial intelligence as a real-time collaborative teammate and sparring partner — allowing their teams to fill in expertise and perspective gaps and advance their decision-making. This opens up all kinds of exciting possibilities, especially when they extend smarter collaboration practices to this new contributor — specifically, bringing together the right mix of expertise and viewpoints, at the right time, to tackle issues from many different angles.

As Heidi K. Gardner and I show in Smarter Collaboration: A New Approach to Breaking Down Barriers and Transforming Work, this way of working generates higher revenue and profit, faster innovation, lower risk, better talent engagement, and deeper customer and client relationships. GenAI can be a powerful new partner, bringing expertise and perspectives that may not exist on a given team.

Top Opportunities

Through our research, we’ve identified three kinds of gaps in a team where GenAI can play a remarkable role: functional, personality, and external perspectives. The functional role is perhaps the most obvious one, covering areas such as sales, marketing, product, client service, HR, risk, and compliance.

We see many examples of people using ChatGPT to bring functional expertise, including creating marketing content for their teams or quickly assessing the potential risks of a new campaign. When GenAI is applied to proprietary data sets, it can deliver highly specialized input, such as more precise answers to customer questions and better clinical decision support in the health-care arena. These virtual teammates are providing critical expertise needed for smarter collaboration and its associated outcomes.

To build trust that GenAI won’t replace them, employees must understand their unique value that it can’t rival.

GenAI can also round out personality traits on a team. Smarter collaboration requires engaging people with different personalities (for example, complex versus concrete thinkers) to innovatively brainstorm and carry out solutions. One team we observed used GenAI to role-play a person who is highly wary, to help refine approaches to experienced-hire integration. If your teammates share similar personality traits, you can bring in GenAI to supply the missing perspectives.

Perhaps the least common perspective on a team is the external one — from customers, partners, or competitors. But their contributions can be enormously valuable. If these stakeholders aren’t around, GenAI can simulate their viewpoints and personas, helping you understand how they could react to a new marketing message, product description, or process change. This approach can be a highly efficient way to strengthen decision-making.

Laying the Foundation for a GenAI Team Member

Our decade-plus of research with organizations across industries and geographies has quantified the top barriers to effective collaboration. These include a lack of interpersonal trust, a lack of competence trust, and a perceived lack of time. These barriers take on different flavors when GenAI is in the mix.

For example, to build trust that GenAI won’t replace them, employees must understand their unique value that it can’t rival. Senior leaders and managers are integral in helping to draw out these characteristics, fostering original thinking and creativity. As I recently explained in “How to Develop Talent in an AI World,” GenAI is fundamentally backward-looking, synthesizing existing data to develop its output — as opposed to developing truly original ideas.

As for competence trust, users need to know GenAI is capable. This can be shown through test pilots that showcase its successes and failures. Another approach is training workers on identifying (and improving upon) the 10 percent of GenAI results that are sub-optimal — that show, for example, evidence of bias, or deliver false or misleading information.

As with any new capability, an initial investment in learning and development is required. The added near-term time pressure can be alleviated by making sure colleagues understand when GenAI is likely to be effective and quantifying and communicating the time savings it achieves.

Looking Ahead

This article just scratches the surface on how GenAI can be used as a real-time collaborative team member, to provide crucial insights and perspectives that would otherwise be missing. My team at Smarter Collaboration International is knee-deep in research on this topic as well as on the effects of GenAI on professional development and capability-building as it takes over rudimentary tasks. As we advance our understanding in these areas, I look forward to sharing more practical insights and advice with the Wharton community.


Ivan Matviak C91 G98 WG98 is executive vice president at Clearwater Analytics, co-author of Smarter Collaboration: A New Approach to Breaking Down Barriers and Transforming Work, and co-founder of Smarter Collaboration Int’l.