Dr. Molly Sands, head of the Teamwork Lab at Atlassian, said at VB Transform 2026 that most companies are approaching AI adoption backwards by optimising how individuals use AI instead of redesigning team workflows. Sands discussed her lab’s research and practical work in a fireside chat with VentureBeat senior technology contributor Sam Witteveen.
Who is studying this and what they do
Sands leads a team of behavioral scientists and psychologists who examine how AI is changing the way people collaborate. The Teamwork Lab not only studies these changes but also actively intervenes, teaching new ways of working and remapping work flows inside organisations so that technology creates real value.
"We don't just study it, we also actively go in and change it," Sands summarized.
Why AI speed isn’t translating into ROI
Atlassian’s annual State of Teams Report this year surveyed about 12,000 global knowledge workers and interviewed roughly 200 Fortune 1000 executives. The findings show a large gap between activity and value: AI use is widespread, but clear return on investment is rare.
Sands reported that 89% of executives told them individuals are speeding up at their companies, yet only 6% could point to specific examples of clear ROI. Overall, roughly 14% of teams have translated AI usage into real value.
That means a single organisation may contain a handful of high-performing teams while other teams see no return at all.
What the successful teams do differently
Teams that converted AI use into value shared three characteristics:
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Context: Successful teams build what Atlassian calls a "context graph" — goals, decisions and organisational knowledge are captured in shared digital records rather than staying in individual memories. Across products such as Jira and Confluence, this graph links work items, goals and the people doing the work, giving AI access to the organisational context it needs.
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Workflows: Leading teams redesigned entire end-to-end processes rather than merely accelerating isolated tasks. Speeding up individuals who are pointed in slightly different directions quickly leads to clashes.
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Culture: The fastest-moving teams had leaders who explicitly encouraged learning and experimentation, while making clear that some experiments would fail.
How leaders can move AI from individual hack to team advantage
Sands said experimentation and constraints are the fastest route to learning. The teams seeing the biggest gains deliberately imposed constraints on how they worked—for example, breaking every task into the smallest practical unit (a single story point) or committing to write no code by hand for a week.
"Most of it is not sustainable to do forever, but it is a really, really fast way to learn," she said.
Another obstacle is that employees are figuring out AI on their own. Every worker develops different prompts, agents and assumptions, creating an additional layer of unspoken knowledge within teams that rarely translates into organisational performance.
To address this, Atlassian experimented with AI working agreements at the start of projects: teams decided not only what they would use AI for but also what they would deliberately avoid using it for, which agents they would share and what common skills would keep everyone operating from the same context. Teams that adopted these agreements used AI more, moved faster, made better decisions and ultimately produced higher-quality work.
Conclusion
Sands argued that AI does not necessarily create entirely new management problems so much as expose and amplify existing ones. Hidden assumptions and differing mental models have always challenged teams; AI simply makes those gaps more consequential, increasing the importance of shared context and explicit ways of working.
This article is based on sponsored content presented by Atlassian and published by VentureBeat.
