Atlassian: Research shows organizations should approach AI at the team level, not the individual level, to achieve true ROI

Atlassian researchers argue that companies are getting AI adoption wrong. Organizations focus on individual productivity gains when they should redesign how teams collaborate with AI tools.

Dr. Molly Sands, head of Atlassian's Teamwork Lab, presented this finding at VB Transform 2026. Her team of behavioral scientists and psychologists studies how AI reshapes teamwork and helps companies restructure workflows accordingly.

The distinction matters. Individual AI optimization, like an engineer using ChatGPT to write code faster, misses the bigger payoff. Team-level adoption captures how AI changes handoffs, decision-making, and collaboration across departments.

Sands' research suggests that ROI compounds when organizations redesign processes around AI capabilities rather than bolting AI onto existing workflows. This means mapping how information flows between teams, redefining approval chains, and restructuring how work moves from one person to the next.

Many enterprises struggle with this shift. They deploy tools like Copilot or Claude at scale without rethinking underlying processes. Engineers get faster, but marketing still waits on engineering. Sales gets AI-powered forecasting, but still follows outdated approval procedures for contracts.

Atlassian's approach goes beyond research. The Teamwork Lab actively works with clients to redesign how work flows. This involves teaching new collaboration patterns, identifying bottlenecks that AI can eliminate, and restructuring team interactions.

The message aligns with broader industry observations about AI adoption. Companies that see real productivity gains typically restructure roles and processes simultaneously with tool deployment. Those that simply add AI to existing workflows see minimal returns.

For enterprises planning 2026 AI investments, the takeaway is clear. Budget for process redesign alongside tool purchase. Assign teams to map workflows before and after AI implementation. Measure team-level metrics like handoff speed and approval cycles, not just individual task completion rates