Atlassian Research: AI is making developers faster. It’s also exposing what’s broken in software delivery
BENGALURU – Atlassian, a leading provider of AI collaboration and productivity software for teams, today published its global 2026 State of Developer Experience report. The findings highlight a growing paradox across the software industry: while individual coding speed has surged with the adoption of AI, broader software delivery continues to be hindered by persistent organizational and workflow bottlenecks. For India—home to one of the world’s largest developer ecosystems and rapidly expanding Global Capability Centers (GCCs)—this paradox is especially critical as engineering organizations race to scale AI adoption across distributed teams.
The gap between individual speed and team delivery
The research reveals that accelerated output at the developer workstation is not translating into net time savings for engineering teams. In 2025, 68% of developers reported saving at least 10 hours per week using AI; in 2026, that figure dropped to just 25%.
A primary driver of this shift is the emergence of new re-work cycles: 65% of developers now report regularly spending time reviewing, debugging, and correcting AI-generated code. If the whole process is not connected from start to finish, slow code reviews and problems merging the code later can cancel out the time AI saved earlier —a friction point increasingly felt across Indian engineering teams managing high-volume codebases and distributed delivery pipelines.
Longstanding barriers remain
Despite widespread AI investment, the primary impediments to productivity remain organizational rather than technical.
Similarly to last year’s results, finding information (51%) is developers’ biggest source of friction in 2026, followed by meetings (50%), context switching (45%), technical debt (44%) and unclear requirements (40%).
While AI has the potential to streamline knowledge discovery, map complex dependencies, and automate repetitive handoffs, most deployments remain narrowly focused on code generation rather than end-to-end team flow.
“AI can help developers move quicker, but faster code is not the same as better software delivered sooner. If teams still struggle to find information, clarify requirements and move work through the delivery process, speeding up coding alone won’t translate into business outcomes,” said Andrew Boyagi, Customer CTO at Atlassian.
AI use is not the same as an AI strategy
The report highlights a critical disconnect between the deployment of AI tools and strategic business enablement.
Lack of strategic clarity: Only 17% of developers and 23% of engineering leaders report that their organization has a clearly communicated and implemented AI strategy.
Vanity metrics and pressure: 60% of developers say AI usage is treated as a productivity signal, yet only 42% of leaders believe they have the right metric to separate genuine gains from AI activity.
The strategic dividend: Developers at organizations with a clear AI strategy are twice as likely to report less friction than before AI was introduced.
“Leaders need to be clear about the problems they want AI to solve and measure the outcomes for the whole team, not just how often the tools are used. Making one task faster doesn’t improve the whole system,” adds Boyagi.
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