Introducing Atlassian
Many companies are approaching getting AI back by improving how people use AI instead of how teams work together, said Dr. Molly Sands, head of the Teamwork Lab at Atlassian, during a fireside chat with VentureBeat technology contributor Sam Witteveen at VB Transform 2026.
Sands leads a team of behavioral scientists and psychologists studying how AI is reshaping the way people work, using the findings to help organizations redesign the way work is done.
"We don’t just read it, we go in and change it," he explained. His teams teach new ways of working and reorganize how work flows in companies, which is a challenge that many organizations are still struggling with, he said.
Why AI speed doesn’t translate to ROI
The annual Atlassian State of Teams Report, which this year surveyed 12,000 global information workers and interviewed nearly 200 Fortune 1000 executives, found a huge disconnect between work and value, showing that everyone is using AI, while very few may be able to find where it pays.
"89% of those executives told us that people are accelerating in their companies, and only 6% of them said they could point to specific examples of clear ROI," Sands said.
But only about 14% of teams have translated the use of AI into real value – meaning that one organization can contain a handful of high-performing teams surrounded by others that don’t see anything at all.
Those leading teams shared three characteristics: context, workflow and culture. Lean teams were building what Atlassian calls a context graph by capturing the organization’s goals, decisions, and knowledge in shared digital records rather than leaving them in individual memory. Across products like Jira and Confluence, a graph connects work items, goals and the people who make them, giving AI access to the organizational context it needs.
In workflow, winning teams redesign entire processes end-to-end rather than simply speeding up individual tasks. If not, speeding up people who are identified in different ways makes them “quick to collide,” as Sands says.
Culturally, the fastest teams worked under leaders who openly encouraged learning and experimentation, while making it clear that some experiments would fail.
How leaders can move AI from individual hacking to team benefit
Experiments and challenges are the fastest way to learn, says Sands. The teams that saw the greatest benefits were deliberately putting constraints on how they worked, from breaking every task down into a very small piece of work (a single story point) to committing to writing code by hand for a week.
"Most of it is not stable to do forever, but it is a quick way to learn," he said.
Sands argued that another obstacle is not the technology itself but the fact that workers are discovering AI on their own. Every employee develops different information, agents and assumptions, creating another layer of unspoken knowledge within groups that rarely translates into organizational performance.
To counter that, Atlassian has experimented with AI performance agreements at the start of projects, asking teams to decide not only what they will use AI for, but what they will deliberately avoid using it for, what agents they will share and what common skills will keep everyone working in the same context. Teams that have embraced this trend use AI more, move faster, make better decisions and ultimately produce higher quality work.
The broader lesson, says Sands, is that AI doesn’t create new management problems so much as it exposes old ones. Teams have been struggling with hidden assumptions and different mental models of their work. AI simply makes those spaces more relevant, increasing the value of shared context and clear ways of working.
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