40 DevOps platform stats for 2026: DORA metrics, AI impact, and Atlassian’s position

Platform teams want proof they’re investing in the right tooling, especially as AI changes delivery workflows. DORA metrics have become the standard language for measuring engineering performance, and data shows a widening gap between elite and low-performing teams. Meanwhile, AI is reshaping how software gets written, reviewed, and shipped, while toolchain sprawl quietly erodes the productivity gains organizations are trying to capture.

This article combines the latest DORA research, the Forrester Wave™ for DevOps Platforms, and Atlassian’s State of Teams 2025 to give you 40 data points that justify platform investment and diagnose where execution breaks down.

The pressure to ship faster, stay secure, and integrate AI without accumulating new coordination debt has made platform engineering one of the most commercially visible disciplines in modern IT.

Organizations that have accumulated toolchains of ten or more disconnected systems are finding that AI cannot help if it has no clean data to act on. That is exactly where DORA metrics, platform consolidation, and Atlassian’s System of Work converge: they all describe the same problem (scattered context) and prescribe the same solution: connected, visible work.

DORA (DevOps Research and Assessment) tracks four key metrics across thousands of organizations each year: deployment frequency, lead time for changes, change failure rate, and time to restore service. The gap between elite and low performers has widened considerably, and the data shows the industry is not improving on aggregate.

1. 182x: Elite performers deploy 182 times more frequently than low performers – on demand versus monthly or less. (DORA State of DevOps 2024)

2. 127x: Elite performers have lead times for changes that are 127 times shorter – under one hour compared to between one week and one month for low performers. (DORA State of DevOps 2024)

3. 8x: Elite performers maintain change failure rates eight times lower than low-performing teams. (DORA State of DevOps 2024)

4. 2,293x: Elite performers restore service from failed deployments 2,293 times faster – typically under one hour versus weeks for low performers. (DORA State of DevOps 2024)

5. Only 19%: Just 19% of teams qualified as elite performers in the 2024 DORA report. (DORA State of DevOps 2024)

6. 17% → 25%: The low-performance tier grew from 17% to 25% between 2023 and 2024, while the high-performance tier shrank from 31% to 22%. The middle is moving down, not up. (DORA State of DevOps 2024)

7. -7.2%: A 25% increase in AI adoption showed a 1.5% decrease in throughput and a 7.2% decrease in stability – DORA’s finding that AI amplifies the strengths of high performers and the dysfunctions of struggling ones, not a shortcut around them. (DORA State of DevOps 2024)

8. 5.3x: Teams that track work together are more likely to produce high-quality deliverables and meet deadlines. (Atlassian State of Teams 2025)

9. 4.1x: Teams that plan and track work in a single place are more likely to meet their deadlines. (Atlassian State of Teams 2025)

10. 2.4x: Teams that plan tasks in one place are more likely to focus on the work that matters most. (Atlassian State of Teams 2025)

11. 93%: Executives say cross-functional collaboration between software and technology teams is more critical to business success than ever before. (Atlassian State of Teams 2025)

12. 89%: Executives say their organization needs to move more rapidly than ever to keep pace with competition. (Atlassian State of Teams 2025)

AI is now embedded in the software development lifecycle at every stage: writing code, reviewing PRs, summarizing incidents, and generating documentation. The productivity case is strong. The adoption gap – and the risk of deploying AI into an unconnected toolchain – is real.

13. 55%: Developers using GitHub Copilot completed tasks 55% faster in controlled experiments. (GitHub Blog – Quantifying Copilot’s Impact)

14. 90%: In Accenture’s enterprise study of thousands of developers, 90% felt more fulfilled with their job when using GitHub Copilot. (GitHub Blog – Copilot Impact with Accenture)

15. 85%: 85% of developers felt more confident in their code quality when using AI assistance. (GitHub Blog – Quantifying Copilot’s Impact)

16. 73%: 73% of developers reported staying in a flow state when using GitHub Copilot – reducing the context interruptions that compound across a full sprint. (GitHub Blog – Quantifying Copilot’s Impact)

17. 71%: Teams admit they are not maximizing AI to help manage and discover information across the organization. (Atlassian State of Teams 2025)

18. 96%: Executives are not completely confident they know how to get teams to use AI more effectively in daily work. (Atlassian State of Teams 2025)

19. 98%: Executives worry their teams are not effectively using AI to eliminate silos between business and engineering. (Atlassian State of Teams 2025)

20. 5.4x: Teams that build and actively use collective knowledge are more likely to produce high-quality work – the same multiplier that makes clean, connected data a prerequisite for useful AI. (Atlassian State of Teams 2025)

21. 5 million MAU: Atlassian’s AI product, Rovo, surpassed 5 million monthly active users, with record agent and token usage. (Atlassian Q2 FY26 Shareholder Letter)

22. 1 million+ seats: Atlassian’s Teamwork Collection – its primary AI monetization vehicle – surpassed 1,000 customers and 1 million seats. (Atlassian Q2 FY26 Shareholder Letter)

23. 120%+: Atlassian’s Cloud net revenue retention rate exceeded 120%, driven by AI expansion through additional users, apps, and tier upgrades. (Atlassian Q2 FY26 Shareholder Letter)

24. 42%: Organizations reported a 42% average reduction in onboarding effort after centralizing administration in Cloud Enterprise – directly reducing the time new engineers spend before they are productive. (Forrester TEI – Atlassian Cloud Enterprise)

25. 11 hours: Forrester models context switching reduction as 11 hours saved per developer per year in the Cloud Enterprise composite analysis – time recaptured directly from tool-to-tool navigation. (Forrester TEI – Atlassian Cloud Enterprise)

Platform engineering emerged as a formal discipline precisely because toolchain sprawl had become an execution problem. Developers spending significant time navigating between tools, re-establishing context, and manually bridging information gaps cannot be addressed by adding more tools. The answer is reducing the number of surfaces that require navigation.

26. 80%: Gartner predicts that 80% of large software engineering organizations will establish platform engineering teams by 2026 – up from 45% in 2022. (Gartner, Platform Engineering)

27. 25%: Executives and teams alike spend a quarter of the workweek searching for information – time lost before real execution begins. (Atlassian State of Teams 2025)

28. 56%: Workers say the only way to get the information they need is to ask someone directly or schedule a meeting. (Atlassian State of Teams 2025)

29. 43%: Knowledge workers say they could work faster if it were simply easier to find the information they need. (Atlassian State of Teams 2025)

30. 41%: Knowledge workers say they could work faster if all teams used the same processes to get work done. (Atlassian State of Teams 2025)

31. 1 in 2: Knowledge workers say teams at their organization unknowingly work on the same things – duplication driven directly by toolchain fragmentation and poor visibility. (Atlassian State of Teams 2025)

32. 2.4 billion: Hours are wasted searching for information each year within Fortune 500 companies alone. (Atlassian State of Teams 2025)

33. 74%: Executives say lack of communication interferes with the speed and quality of their organization’s output. (Atlassian State of Teams 2025)

34. 40%: Jira ticket data shows that 40% of knowledge workers’ direct collaborators sit in a different job function – making cross-tool, cross-team visibility a structural dependency, not an edge case. (Atlassian State of Teams 2025)

35. 7%: Only 7% of executives feel confident they know exactly how each team’s work supports the company’s biggest goals. (Atlassian State of Teams 2025)

36. 20%: Only 20% of knowledge workers feel confident their team has an effective process for quickly informing other teams of decisions that affect their work. (Atlassian State of Teams 2025)

37. Leader – Forrester Wave™ DevOps Platforms, Q2 2025: Atlassian was named a Leader in the Forrester Wave™: DevOps Platforms, Q2 2025, receiving the highest scores possible (5/5) across Vision, Innovation, and Roadmap criteria. (Atlassian – Forrester Wave DevOps 2025)

38. 358%: The Forrester Total Economic Impact study for Atlassian Open DevOps reports a 358% ROI over three years – driven by unified pipelines and automated workflows connecting Atlassian and third-party tools. (Forrester TEI – Atlassian Cloud Enterprise)

39. 230%: The Forrester TEI for Atlassian Cloud Enterprise specifically reports a 230% ROI over three years (risk-adjusted), with a payback period of under six months. (Forrester TEI – Atlassian Cloud Enterprise)

40. $1.7M: The three-year present value of end-user efficiency gains for a 2,750-user organization on Atlassian Cloud Enterprise – the single largest quantified benefit category in the Forrester model. (Forrester TEI – Atlassian Cloud Enterprise)

Teams that deploy frequently, recover quickly, and fail rarely are the ones that have solved the coordination problem first: unified visibility, shared processes, and knowledge that does not require a meeting to locate.

DORA’s 2024 finding that AI adoption correlates with worsened delivery metrics for weaker organizations sharpens that point.

The organizations capturing the most value from AI-assisted delivery already had clean, connected data. Those that haven’t fixed toolchain sprawl are finding that AI accelerates their dysfunction rather than resolving it.

The right platform gives every team – engineering, IT, operations, and business – a shared surface for goals, work, knowledge, and decisions. That is the operating model that separates the 19% of elite performers from the 81% still working their way up.