AI-Powered Teams: How the Usage of Generative AI Tools Enhances Knowledge Transfer and Knowledge Application in Knowledge-Intensive Teams
Olivia Bruhin, Luc Bumann, Philipp Ebel
This study investigates the role of Generative AI (GenAI) tools, such as ChatGPT and GitHub Copilot, in software development teams. Through an empirical study with 80 software developers, the research examines how GenAI usage influences key knowledge management processes—knowledge transfer and application—and the subsequent effect on team performance.
Problem
While the individual productivity gains from GenAI tools are increasingly recognized, their broader impact on team-level knowledge management and performance remains poorly understood. This gap poses a risk for businesses, as adopting these technologies without understanding their collaborative effects could lead to unintended consequences like reduced knowledge retention or impaired team dynamics.
Outcome
- The use of Generative AI (GenAI) tools significantly enhances both knowledge transfer (sharing) and knowledge application within software development teams. - GenAI usage has a direct positive impact on overall team performance. - The performance improvement is primarily driven by the team's improved ability to apply knowledge, rather than just the transfer of knowledge alone. - The findings highlight GenAI's role as a catalyst for innovation, but stress that knowledge gained via AI must be actively and contextually applied to boost team performance effectively.
Host: Welcome to A.I.S. Insights — powered by Living Knowledge. I’m your host, Anna Ivy Summers. Today, we're diving into a fascinating new study titled "AI-Powered Teams: How the Usage of Generative AI Tools Enhances Knowledge Transfer and Knowledge Application in Knowledge-Intensive Teams".
Host: It explores how tools we're all hearing about, like ChatGPT and GitHub Copilot, are changing the game for software development teams. Specifically, it looks at how these tools affect the way teams share and use knowledge to get work done. To help us unpack this, we have our expert analyst, Alex Ian Sutherland. Welcome, Alex.
Expert: Thanks for having me, Anna.
Host: Alex, we all know GenAI tools can make individuals more productive. But this study looks at the bigger picture, right? The team level. What’s the core problem they're trying to solve here?
Expert: Exactly. While we see headlines about individual productivity skyrocketing, there's a big question mark over what happens when you put these tools into a collaborative team environment. The concern is that businesses are adopting this tech without fully understanding the team-level impacts.
Host: What kind of impacts are we talking about?
Expert: Well, the study points to some serious potential risks. Things like the erosion of unique human expertise, reduced knowledge retention within the team, or even impaired decision-making. Just because an individual can write code faster doesn't automatically mean the team as a whole becomes more innovative or performs better. There was a real gap in our understanding of that connection.
Host: So, how did the researchers investigate this? What was their approach?
Expert: They conducted an empirical study with 80 software developers who are active, regular users of Generative AI in their jobs. They used a structured survey to measure how the use of these tools influenced two key areas: first, "knowledge transfer," which is basically sharing information and expertise, and second, "knowledge application," which is the team's ability to actually use that knowledge to solve new problems. Then they linked those factors to overall team performance.
Host: A direct look at the people on the front lines. So, what were the key findings? What did the data reveal?
Expert: The results were quite clear on a few things. First, using GenAI tools significantly boosts both knowledge transfer and knowledge application. Teams found it easier to share information and easier to put that information to work.
Host: Okay, so it helps on both fronts. Did one matter more than the other when it came to the team’s actual success?
Expert: That's the most interesting part. Yes, one mattered much more. The study found that the biggest driver of improved team performance was knowledge *application*. Just sharing information more efficiently wasn't the magic bullet. The real value came when teams used the AI to help them apply knowledge and actively solve problems.
Host: So it’s not about having the answers, it's about using them. That makes sense. Let's get to the bottom line, Alex. What does this mean for business leaders, for the managers listening to our show?
Expert: This is the crucial takeaway. It's not enough to just give your teams a subscription to an AI tool and expect results. The focus needs to be on integration. Leaders should be asking: How can we create an environment where these tools help our teams *apply* knowledge? This means fostering a culture of active problem-solving and experimentation, using AI as a collaborator.
Host: So, it’s a tool to be wielded, not a replacement for team thinking.
Expert: Precisely. The study emphasizes that GenAI should complement human expertise, not replace it. Over-reliance can be dangerous and may reduce the interpersonal learning that’s so critical for innovation. The goal is balanced usage, where AI handles routine tasks, freeing up humans to focus on complex, collaborative problem-solving. Think of GenAI as a catalyst, but your team is still the engine.
Host: That’s a powerful distinction. So, to recap: this research shows that GenAI can be a fantastic asset for teams, boosting performance by helping them not just share information, but more importantly, *apply* it effectively. The key, however, is thoughtful integration—using AI to augment human collaboration, not automate it away.
Host: Alex, thank you for breaking that down for us with such clarity.
Expert: My pleasure, Anna.
Host: And thank you for tuning into A.I.S. Insights, powered by Living Knowledge.
Human-AI Collaboration, AI in Knowledge Work, Collaboration, Generative AI, Software Development, Team Performance, Knowledge Management
