Dr. Molly Sands, head of the Atlassian Teamwork Lab, told VentureBeat senior technology contributor Sam Witteveen during a fireside chat at VB Transform 2026 that many companies are approaching AI adoption backwards — optimizing how individuals use AI instead of redesigning how teams collaborate and how work flows.
Sands leads a team of behavioral scientists and psychologists who study how AI is reshaping teamwork, and they apply those findings to help organizations change how work gets done.
"We don't just study it, we also actively go in and change it," she said. Her teams teach new ways of working and remap workflows across companies, a task many organizations still find difficult.
Activity is up, measurable ROI is rare
Atlassian’s annual State of Teams Report surveyed 12,000 global knowledge workers and interviewed roughly 200 Fortune 1000 executives. The report finds a large gap between AI activity and measurable value: almost everyone is using AI, but few can locate where it pays off.
According to Sands, 89% of those executives said individuals in their companies are speeding up, while only 6% said they could point to specific examples of clear ROI. At the team level, about 14% had translated AI usage into real value — meaning a company can contain a handful of high-performing teams while most teams see no return.
What successful teams do differently
Teams that have turned AI into real advantage share three characteristics: shared context, end-to-end workflows, and a culture of experimentation.
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Context: leading teams build what Atlassian calls a "context graph" by capturing goals, decisions, and organizational knowledge in shared digital records rather than leaving them in individual memory. Across products such as Jira and Confluence, the graph links work items, goals and the people doing them, giving AI access to organizational context.
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Workflows: winning teams redesign whole end-to-end processes rather than merely accelerating isolated tasks. Otherwise, speeding up individuals who are pointed in slightly different directions quickly causes them to "crash into each other," as Sands put it.
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Culture: the fastest-moving teams operated under leaders who explicitly encouraged learning and experimentation and made it clear some experiments would fail.
Turning individual hacks into team advantage
Sands said experimentation and deliberate constraints are the fastest route to learning. Teams seeing the biggest gains intentionally imposed constraints on how they worked — from breaking every task into the smallest practical unit (a single story point) to 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.
She also argued that a key obstacle is that employees are discovering AI on their own. Every worker develops different prompts, agents and assumptions, which creates layers of tacit knowledge within teams that rarely translate into organizational performance.
To address that, Atlassian experimented with AI working agreements at the start of projects: teams decided not only what they would use AI for, but what they would deliberately avoid, which agents they'd share, and what common skills would keep everyone working from the same context. Teams that adopted these agreements used AI more, moved faster, made better decisions and produced higher-quality work.
Final takeaways
Sands said AI is less about creating new management problems than about exposing old ones. Teams have always struggled with hidden assumptions and differing mental models; AI simply makes those gaps more consequential, increasing the importance of shared context and explicit ways of working.
Note: this article was presented by Atlassian as sponsored content in VentureBeat.
