Presented by Atlassian
Most companies are approaching AI adoption backwards, optimizing how individuals use AI rather than how teams work together, said Dr. Molly Sands, director of Atlassian’s Teamwork Lab, during a fireside chat with VentureBeat senior technology contributor Sam Witteveen on VB Transformation 2026.
Sands leads a team of psychologists and behavioral scientists studying how AI is reshaping the way people work together, using those findings to help organizations redesign the way work is done.
"We not only study it, we also actively go in and change it," she explained. His teams teach new ways of working and redefine how work flows between companies, a challenge many organizations are still struggling with, he said.
Why AI speed doesn’t translate into ROI
Atlassian’s annual State of Teams Report, which this year surveyed 12,000 global knowledge workers and interviewed approximately 200 Fortune 1000 executives, found a significant disconnect between activity and value, showing that everyone is using AI, while very few can still identify where it pays off.
"89% of those executives told us that people are picking up the pace at their companies, and only 6% of them said they could point to specific examples of clear ROI." Sands said.
But about 14% of teams had translated AI use into real value, meaning a single organization could contain a handful of high-performing teams surrounded by others that see no return.
Those leading teams shared three characteristics: context, workflows, and culture. Teams moving forward were building what Atlassian calls a context graph by capturing goals, decisions, and organizational knowledge in shared digital records rather than leaving them in individual memory. In products like Jira and Confluence, the graph connects work items, goals, and the people who perform them, giving AI access to the organizational context it needs.
In terms of workflows, the winning teams redesigned entire end-to-end processes rather than simply accelerating isolated tasks. Otherwise, accelerating individuals pointing in slightly different directions only causes them to “very quickly start crashing into each other,” as Sands puts it.
In terms of culture, the teams that advanced the most quickly worked under leaders who explicitly encouraged learning and experimentation, while making it clear that some experiments would fail.
How leaders can take AI from an individual gimmick to a team advantage
Experimentation and limitations are the quickest route to learning, Sands said. The teams that saw the biggest gains deliberately imposed restrictions on how they worked, from breaking each task down into the smallest practical unit of work (a single story point) to committing to not writing code by hand for a week.
"Most of it isn’t sustainable forever, but it’s a really quick way to learn." she said.
Sands argued that another obstacle is not the technology itself, but the fact that employees are discovering AI for themselves. Each worker develops different cues, agents, and assumptions, creating another layer of tacit knowledge within teams that rarely translates into organizational performance.
To counter this, Atlassian experimented with AI working agreements at the start of projects, asking teams to decide not only what they would use AI for, but also what they would deliberately avoid using it for, what agents they would share, and what common skills would keep everyone working in the same context. Teams that adopted this practice used AI more, moved faster, made better decisions, and ultimately produced higher quality work.
The broader lesson, Sands said, is that AI is not creating entirely new management problems but exposing old ones. Teams have always struggled with hidden assumptions and different mental models of their work. AI simply makes those gaps more consequential, increasing the importance of shared context and explicit ways of working.
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