The next wave of AI value won’t come from buying more tools. Atlassian’s 2026 State of Teams report says it will come from better coordination at the team level.
If you have introduced AI tools into your business over the past year or two and found that your team feels busier and more overwhelmed rather than less, you are not imagining it. Atlassian’s 2026 State of Teams report gives that feeling a name and a cause, and the implications for how small business owners think about AI adoption are significant.
The report, which draws on survey data from thousands of workers across multiple countries including Australia, identifies a paradox at the heart of the current AI moment. AI has finally delivered on its promise of speed. Individual workers are moving faster than ever. And yet work feels more chaotic, not less. The coordination that used to happen naturally, through shared processes, common tools, and team-level routines, has broken down as individuals and departments have adopted AI independently, in different ways, at different speeds, for different purposes.
Speed without coordination
The Australian data in the report is striking. Eighty-seven percent of Australian workers say there is a lack of time or capacity to coordinate with their colleagues. Only 45% say they are working at a comfortable pace. The average knowledge worker is juggling eight projects at a time and spending 37% of their time on tasks unrelated to their actual jobs. These are not the numbers of a workforce that has successfully absorbed a productivity-enhancing technology. They are the numbers of a workforce that is busier, more fragmented, and more stretched than before.
The picture extends to processes. Sixty-nine percent of workers say their processes and workflows are not optimised for AI. That figure is telling: most businesses have added AI tools on top of existing workflows rather than redesigning those workflows around what AI actually makes possible. The result is more tools, more outputs, more information, and less clarity about how it all fits together.
Atlassian has put a dollar figure on what this costs. For Fortune 500 companies, the fragmentation tax, the cost of uncoordinated AI adoption, is estimated at $161 billion annually. That is an enterprise number, and it is not directly transferable to a small business context. But the underlying dynamic is exactly the same at any scale. When different team members are using different AI tools, generating outputs in different formats, working to different rhythms, and not taking the time to coordinate because everyone is too busy moving fast individually, the organisation as a whole loses more than it gains from the individual speed improvements.
For a small business with five, ten, or twenty people, this plays out in familiar ways. The person using an AI writing tool produces content faster, but nobody has agreed on the voice or the approval process. The person using an AI scheduling tool is more efficient with their time, but their calendar no longer syncs naturally with the team’s rhythm. The person using an AI research tool finds information quickly, but their colleagues do not know how to interpret or verify what they bring back. Each individual is faster. The team is more confused.
Where the real problem sits
The Atlassian report locates the core problem at the executive level. Only one in four executives focus their AI implementations at the team level. Most AI rollouts are either top-down mandates, adopt this tool across the organisation, or bottom-up individual adoption, people finding and using tools on their own. Neither approach addresses the coordination layer where most actual work happens.
For SME owners, this is both a diagnosis and an opportunity. The businesses that have rolled out AI in a top-down way without thinking about how teams will actually work together differently are experiencing the fragmentation tax. The businesses that have left AI adoption entirely to individual initiative are experiencing the same thing, just with more variation in which tools people are using. The missing piece in both cases is the team-level conversation about how work gets coordinated now that individuals have more capability.
What SMEs should do differently
The Atlassian report’s central argument is that the next wave of AI return on investment will not come from scaling individual adoption or issuing top-down mandates. It will come from improving coordination at the level where work actually gets done, which is teams. For small business owners, that translates into a practical shift in how they think about introducing AI into their operations.
Rather than asking which AI tool should we add next, the more useful question is how does our team currently coordinate, and how would that need to change for AI to genuinely help rather than add noise? That might mean agreeing on shared workflows before rolling out a new tool. It might mean designating time for the team to coordinate rather than assuming coordination will happen in between the faster individual work. It might mean reviewing which of the eight projects the average team member is juggling are actually necessary, and cutting the ones that exist only because they were easy to start once AI made starting things faster.
The businesses getting the most from AI right now, according to the Atlassian data, are not the ones with the most tools. They are the ones that have thought carefully about how their teams work together and have used AI to improve that coordination rather than accelerate around it. For SME owners with small, agile teams, that is actually an advantage. Redesigning how a team of ten coordinates is considerably faster and easier than doing the same for a team of ten thousand.
The fragmentation tax is real. But for small businesses that move deliberately rather than reactively on AI, it is also entirely avoidable.
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