Provided by Atlassian
During an insightful fireside chat with VentureBeat Senior Technology Contributor Sam Witteveen at VB Transform 2026, Dr. Molly Sands, head of Atlassian’s Teamwork Lab, discussed a crucial misstep in AI adoption. Companies frequently focus on optimizing individual AI usage rather than enhancing team collaboration.
Sands leads a team of behavioral scientists and psychologists dedicated to exploring how AI influences teamwork. Their research aims to help organizations redesign and improve their collaborative dynamics.
“We are not just researchers; we actively implement changes,” she emphasized. As her team introduces innovative work methodologies and redefines workflows, many organizations continue to wrestle with these challenges.
Why AI Speed Fails to Deliver ROI
This year, Atlassian’s annual State of Teams report, which surveyed 12,000 knowledge workers globally and interviewed nearly 200 Fortune 1000 executives, highlighted a troubling disconnect between activity levels and tangible value. While AI adoption is widespread, many organizations struggle to pinpoint its true benefits.
“89% of executives reported increased speed within their organizations, yet only 6% could cite specific examples of clear ROI,” Sands stated.
Remarkably, about 14% of teams were able to translate their AI usage into genuine value. This suggests that while some high-performing teams thrive, many others remain unproductive.
These successful teams exhibit three key traits: context, workflow, and culture. They create what Atlassian terms a context graph, capturing goals, decisions, and organizational knowledge in a shared digital record rather than relying on individual memory. Tools like Jira and Confluence facilitate these graphs, linking work items, goals, and team members to provide crucial organizational context for AI.
Regarding workflow, the most effective teams focus on redesigning the complete end-to-end process instead of merely speeding up individual tasks. Sands remarked that if team members are progressing in direction, they may inadvertently collide with each other.
In terms of culture, high-velocity teams operate under leaders who actively promote learning and experimentation, acknowledging that failure is an integral part of the process.
Empowering Leaders to Leverage AI for Team Success
Sands highlighted that experimentation combined with constraints is essential for accelerated learning. The most successful teams often break down their tasks into manageable units and might even agree to refrain from manual coding for a week.
“While these measures may not be permanent, they are effective routes for rapid learning,” she noted.
Another significant barrier is that many employees are figuring out AI independently. This results in varied prompts and assumptions, contributing to a layer of tacit knowledge that is seldom reflected in organizational performance.
To address this, Atlassian initiated AI working agreements at project commencement, encouraging teams to define not only how they would utilize AI but also what they would consciously avoid using it for. They delineate which agents to share and identify common skills, fostering a unified context among team members. Teams employing this practice reported increased AI use, faster decision-making, and ultimately, superior work quality.
The overarching takeaway, according to Sands, is that AI does not introduce entirely new business challenges but rather amplifies existing ones. Teams have historically grappled with hidden assumptions and varying mental models of their work. AI simply accentuates these gaps, underscoring the significance of shared context and clearly defined working methods.
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Source: venturebeat.com