Atlassian: Research shows organizations should approach AI at the team level, not the individual level, to achieve true ROI
Our take
The recent findings from Atlassian, as highlighted in their State of Teams Report, offer a crucial course correction for organizations racing to integrate AI. The prevailing approach – focusing on individual AI adoption – is proving surprisingly ineffective in generating tangible ROI. As Dr. Molly Sands points out, while individual productivity may be increasing, the overall organizational benefit remains elusive for most. This echoes a broader trend we’re seeing; the promise of AI isn’t automatically translating into business value. The data suggests that the real leverage lies not in empowering individual users with AI tools, but in fundamentally reshaping how teams work *with* AI. This shift requires a more strategic and holistic approach, one that prioritizes team dynamics and shared understanding over isolated individual gains. To that end, it's worth considering how platforms like llama.cpp are enabling more localized AI development, as explored in Run the Mythos Enhanced Coding Model Locally with llama.cpp and Pi, potentially fostering more controlled and collaborative experimentation within teams. Similarly, the proliferation of AI-generated content, as demonstrated by Deezer’s experience – more than 50% of daily uploads are now AI-generated Music streamer Deezer says more than 50% of daily uploads are AI-generated - highlights the need for teams to establish clear guidelines and shared practices around AI usage to ensure quality and consistency.
Atlassian's identification of "context, workflows, and culture" as key differentiators for high-performing AI teams is particularly insightful. Building a "context graph" – essentially a shared digital record of goals, decisions, and knowledge – is a powerful strategy for aligning teams and providing AI with the necessary background information to operate effectively. This goes beyond simply connecting work items; it’s about creating a living repository of organizational understanding. Furthermore, the emphasis on redesigning end-to-end workflows, rather than just optimizing individual tasks, underscores the importance of a systems-thinking approach. Siloed AI implementations, even if individually efficient, can create friction and inefficiencies when teams are working towards different objectives. The cultural element – fostering a learning environment where experimentation and even failure are accepted – is equally critical. It’s a recognition that AI integration is not a one-time project but an ongoing process of discovery and adaptation. Developing AI working agreements, as Atlassian itself experimented with, is a proactive way to establish shared expectations and prevent the fragmentation of knowledge that can quickly derail AI initiatives.
The observation that AI is primarily exposing existing organizational challenges, rather than creating entirely new ones, is a sobering but valuable reminder. Teams have always struggled with communication gaps, differing assumptions, and a lack of shared context. AI simply amplifies these issues, making them more consequential. This suggests that successful AI adoption isn't just about deploying the right technology; it’s about addressing underlying organizational weaknesses. Investing in team collaboration tools and processes, fostering open communication, and ensuring that everyone is working from the same playbook become even more important in an AI-driven environment. The focus needs to shift from “how can AI help me?” to “how can AI help *us* work better together?” This requires a deliberate effort to cultivate a culture of transparency, shared ownership, and continuous learning, aligning with the broader need for skills development, as outlined in resources like 5 Free Courses to Go From AI Beginner to Practitioner.
Ultimately, Atlassian's research provides a compelling argument for a team-centric approach to AI adoption. While the technology itself is certainly important, it's the way teams leverage it – through shared context, redesigned workflows, and a supportive culture – that will determine whether AI delivers on its promise of increased productivity and ROI. The question now is: will organizations be willing to fundamentally rethink their approach to work, prioritizing team collaboration over individual optimization, to truly unlock the transformative power of AI?
Presented by Atlassian
Most companies are approaching AI adoption backwards by optimizing how individuals 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 senior technology contributor Sam Witteveen at VB Transform 2026.
Sands leads a team of behavioral scientists and psychologists who study how AI is reshaping the way people work together, using those findings to help organizations redesign how work gets done.
"We don't just study it, we also actively go in and change it," she explained. Her teams teach new ways of working and remap how work flows across companies, a challenge that many organizations are still struggling with, she said.
Why AI speed isn’t translating into ROI
Atlassian's annual State of Teams Report, which this year surveyed 12,000 global knowledge workers and interviewed roughly 200 Fortune 1000 executives, found a significant disconnect between activity and value, showing that everyone is using AI, while very few can yet locate where it pays off.
"89% of those executives told us that individuals are speeding up in their companies, and only 6% of them said they could point to specific examples of clear ROI," Sands said.
But roughly 14% of teams had translated AI usage into real value — meaning a single organization could contain a handful of high-performing teams surrounded by others seeing no return at all.
Those leading teams shared three characteristics: context, workflows and culture. The teams pulling ahead were building 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 connects work items, goals and the people doing them, giving AI access to the organizational context it needs.
On workflows, the winning teams redesigned entire end-to-end processes rather than simply accelerating isolated tasks. Otherwise, speeding up individuals who are pointed in slightly different directions only causes them to “very quickly start to crash into each other,” as Sands puts it.
On culture, the fastest-moving teams worked under leaders who explicitly encouraged learning and experimentation, while making it clear that some experiments would fail.
How leaders can move AI from individual hack to team advantage
Experimentation and constraints are the fastest route to learning, Sands said. The teams seeing the biggest gains were deliberately imposing constraints on how they worked, from breaking every task into the smallest practical unit of work (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.
Sands argued that another obstacle isn’t the technology itself but the fact that employees are figuring out AI on their own. Every worker develops different prompts, agents and assumptions, creating another layer of unspoken knowledge inside teams that rarely translates into organizational performance.
To counter that, Atlassian experimented with AI working agreements at the start of projects, asking teams to decide not only what they would use AI for, but what they would deliberately avoid using it for, which agents they would share and what common skills would keep everyone working from the same context. Teams that adopted the practice used AI more, moved faster, made better decisions and ultimately produced higher-quality work.
The broader lesson, Sands said, is that AI isn’t creating entirely new management problems so much as exposing old ones. Teams have always struggled with hidden assumptions and different mental models of their work. AI simply makes those gaps more consequential, increasing the importance of shared context and explicit ways of working.
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