Why do some teams consistently deliver high performance while other, seemingly identical teams struggle? Led by Sandy Pentland, researchers at MIT’s Human Dynamics Laboratory set out to solve that puzzle. Hoping to decode the “It factor” that made groups click, they equipped teams from a broad variety of projects and industries (comprising 2,500 individuals in total) with wearable electronic sensors that collected data on their social behavior for weeks at a time. With remarkable consistency, the data showed that the most important predictor of a team’s success was its communication patterns. Those patterns were as significant as all other factors—intelligence, personality, talent—combined. In fact, the researchers could foretell which teams would outperform simply by looking at the data on their communication, without even meeting their members. In this article Pentland shares the secrets of his findings and shows how anyone can engineer a great team. He has identified three key communication dynamics that affect performance: energy, engagement, and exploration. Drawing from the data, he has precisely quantified the ideal team patterns for each. Even more significant, he has seen that when teams map their own communication behavior over time and then make adjustments that move it closer to the ideal, they can dramatically improve their performance.
The Social Physics of Conversation: Why Communication Patterns Matter
Discover how communication patterns shape team performance, innovation, and collective intelligence. Learn why idea flow matters more than talent alone.
The Task Space: An Integrative Framework for Team Research
Research on teams spans many contexts, but integrating knowledge from heterogeneous sources is challenging because studies typically examine different tasks that cannot be directly compared. Most investigations involve teams working on just one or a handful of tasks, and researchers lack principled ways to quantify how similar or different these tasks are from one another. We address this challenge by introducing the “Task Space,” a multidimensional space in which tasks—and the distances between them—can be represented formally, and use it to create a “Task Map” of 102 crowd-annotated tasks from the published experimental literature. We then demonstrate the Task Space’s utility by performing an integrative experiment that addresses a fundamental question in team research: when do interacting groups outperform individuals? Our experiment samples 20 diverse tasks from the Task Map at three complexity levels and recruits 1,231 participants to work either individually or in groups of three or six (180 experimental conditions). We find striking heterogeneity in group advantage, with groups performing anywhere from three times worse to 60% better than the best individual working alone, depending on the task context. Critically, the Task Space makes this heterogeneity predictable: it significantly outperforms traditional typologies in predicting group advantage on unseen tasks. Our models also reveal theoretically meaningful interactions between task features; for example, group advantage on creative tasks depends on whether the answers are objectively verifiable. We conclude by arguing that the Task Space enables researchers to integrate findings across different experiments, thereby building cumulative knowledge about team performance. This paper was accepted by Sameer Srivastava, organizations. Funding: The authors thank the Alfred P. Sloan Foundation [Grant #202-13924] and the MIT Wade Fund for their generous support of this research. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2023.03544 .
Habits of High-Functioning Teams
I often struggle to explain what it means to be part of a high-functioning software team. Sure, there are mountains of literature, and an entire genre of LinkedIn thought leadership that professes all kinds of guidelines and heuristics about what makes teams work, but in my experience, it’s hard to internalize these ideas and follow someone else’s model if you’ve never seen what good looks like.
Is Your Team Caught in the Solution Fixation Trap?
In the face of escalating complexities, organizations are increasingly relying on teams to tackle these intricate issues and drive pivotal organizational decisions. To extract maximum value from team dynamics, leaders must remain mindful of potential biases that may impact team dynamics and to actively steer and fine-tune team processes. Specifically, the authors urge leaders to address a decision-making bias they call the solution fixation trap, which emerges when a team rushes into discussing possible solutions before fully understanding the problem at hand. The authors discuss the study that helped them uncover this bias, explain what the solution fixation trap looks like, and discuss what leaders can do to protect their teams from it.
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The Illusion of Decisiveness
Why product teams spin in chaos despite a "clear" strategy
Reaching Peak Meeting Efficiency
Meetings are undeniably the most important tool for build a high-performance team with shared values
Kanjun 🐙 on Twitter / X
I've always felt it weird to treat agents as team members. To me, computing is a creative medium where each step expands what I imagine to be possible. Something about the "team member" frame feels creativity-dampening. Not sure why, can't put my finger on it 🤔 https://t.co/CWT3QFVh3x— Kanjun 🐙 (@kanjun) October 1, 2026
A Guide to Measuring Engineering Team Performance
“You can’t manage what you can’t measure.” While software development practices constantly change, there will always be a tier of truly top engineering teams who stand above…
Opinion | Why Some Teams Are Smarter Than Others (Published 2015)
It’s about listening, empathy and having more women.
Redundancy Protects Human Collaborations from Failure
In the name of efficiency, organizations and governments around the world are increasingly trimming redundancy---personnel or overlapping capabilities beyond what routine operations require. Drawing on distributed computing and cognitive science, we challenge this view. Using queueing simulations and a preregistered multiplayer experiment (N = 832 in 187 teams of 3–6), we show that redundancy improves performance through two distinct routes. Adding collaborators to a team improves resilience, with larger teams completing more tasks when members became temporarily unable to contribute. Giving team members overlapping capabilities also improved efficiency, with teams in which collaborators could perform multiple roles completing substantially more work. These findings suggest that efforts to improve short-term efficiency by reducing redundancy may come at a cost, quietly eroding a team’s capacity to withstand unexpected failures.
What Google Learned From Its Quest to Build the Perfect Team (Published 2016)
New research reveals surprising truths about why some work groups thrive and others falter.
What Google Learned From Its Quest to Build the Perfect Team (Published 2016)
New research reveals surprising truths about why some work groups thrive and others falter.
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Research, expert perspectives, and product announcements from Atlassian on how teams can work smarter with AI. Subscribe for monthly insights on the future of teamwork.
