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AI In DevOps Statistics: DevOps is a set of cultural philosophies, practices, and tools that combines software development (Dev) and IT operations (Ops) to shorten the systems development life cycle and deliver high-quality software continuously. AI is now part of almost every developer’s daily work. It helps write code, fix bugs, and speed up everyday tasks. But even with all this use, AI still hasn’t reached deep into the CI/CD pipelines that make up the heart of DevOps.
Developers using its AI tool finished coding tasks much faster than those who didn’t use it. A follow-up study with Accenture found that developers using the tool shipped more code and had fewer problems getting that code approved. Many teams also say they use AI tools every day and that these tools make their work faster and easier.
A survey of knowledge workers and company leaders found that teams lose a large chunk of their time each day just searching for answers, which is exactly the kind of problem AI tools are now being built to solve. This article looks at the latest facts and trends shaping how AI is being used across DevOps today.
Stats That Matter the Most
- The global AI in DevOps market was valued at USD 2.9 billion in 2023, projected to reach USD 24.9 billion by 2033, a 24% CAGR.
- On May 22, 2026, GitHub reported that Copilot served 140,000 organizations, nearly 3 times the previous year’s total, with overall usage growing more than 100% year over year.
- In 2024, 82% of developers used AI for writing code, the most common AI use case in software development.
- Developers using GitHub Copilot completed controlled coding tasks 55% faster, and about 85% felt more confident about their code quality with AI support.
- 73% of organizations do not use AI in their CI/CD workflows at all, while only 1% have fully embedded AI into CI/CD.
- 93% of respondents used at least 1 AI tool for coding or development work, and 78% used at least 1 AI coding assistant, agent, or code editor.
- Elite DevOps teams represent just 19% of teams, deploying on demand with an approximately 5% change-failure rate and recovery in under 1 hour.
- In 2025, 82% of container users ran Kubernetes in production, up from 80% in 2024 and 66% in 2023.
- Harness reported that companies estimated 26% of their AI spending was wasted, based on a survey of 700 engineering leaders and practitioners.
Global AI in DevOps Market Growth
(Source: market.us)
- The global AI in DevOps market was valued at USD 2.9 billion in 2023.
- The market is estimated to reach USD 3.6 billion in 2024 and USD 4.5 billion in 2025.
- The market could rise further to USD 20.1 billion in 2032 and USD 24.9 billion by 2033.
- Overall, the global AI in DevOps market is expected to grow at a compound annual growth rate of 24% during the forecast period.
Recent AI in DevOps Statistics
- On September 23, 2026, AI coding startup ByteAsk raised USD 1 million in a Y Combinator-led pre-seed round to develop coding agents for C and C++ software projects.
- On July 29, 2026, Harness reported that companies estimated 26% of their AI spending was wasted, based on a survey of 700 engineering leaders and practitioners.
- On June 10, 2026, GitLab and Google Cloud expanded their DevSecOps partnership by adding Gemini 3.5 to GitLab Duo Agent Platform and Gemma 4 to GitLab Duo Self-Hosted.
- On June 9, 2026, Datadog launched more than 100 new capabilities, including autonomous Bits AI agents designed to detect, investigate, and resolve development, security, and operational issues.
- On June 2, 2026, GitHub introduced its Copilot desktop application for managing AI-agent sessions, issues, pull requests, and background automations from one workspace.
- On May 22, 2026, GitHub reported that Copilot served 140,000 organizations, nearly 3 times the previous year’s total, while overall usage grew by more than 100% year over year.
- On April 21, 2026, Datadog reported that 69% of companies used at least 3 AI models, about 5% of production AI requests failed, and capacity limits caused nearly 60% of those failures.
- On April 14, 2026, GitLab and Google Cloud integrated Vertex AI models with the GitLab Duo Agent Platform, allowing customers to count agent usage toward existing Google Cloud spending commitments.
- On February 25, 2026, TCS and GitLab formed a global partnership to deploy AI agents and industry-specific workflow templates across planning, coding, testing, security, and software deployment.
Popular Uses of AI in Software Development
- In 2024, 82% of developers used AI for writing code, while 9.2% were interested in using it for this task.
- AI was used to search for answers by 67.5% of developers, while 17.6% expressed interest.
- 56.7% used AI for debugging and technical help, compared with 25.9% who were interested.
- AI supported code documentation for 40.1% of developers, while 38.2% wanted to use it.
- 34.8% used AI to generate content or synthetic data, and 33.1% were interested in this application.
- AI was used to learn about a codebase by 30.9% of developers, while 40.6% expressed interest.
- 27.2% used AI for code testing, compared with 46.2% who wanted to use it.
- AI supported code commits and reviews for 13.2% of developers, while 40.9% were interested.
- 12.2% used AI for project planning, and 31.7% expressed interest.
- AI was used for predictive analytics by 5.3% of developers, while 39.8% wanted to use it.
- Only 4.5% used AI for deployment and monitoring, although 39.6% were interested in this application.
(Reference: statista.com)
AI-Enhanced Software Development Lifecycle
- Developers using GitHub Copilot completed controlled coding tasks 55% faster.
- In an Accenture study involving thousands of developers, 90% felt more satisfied with their jobs when using GitHub Copilot.
- About 85% of developers felt more confident about their code quality with AI support.
- Approximately 73% said GitHub Copilot helped them remain focused during complete development sprints.
- Around 71% of teams admitted they were not fully using AI to manage and find organizational information.
- About 96% of executives lacked complete confidence in their ability to help teams use AI effectively in daily work.
- Nearly 98% of executives worried that their teams were not using AI effectively to remove barriers between business and engineering functions.
- Teams that actively built and used shared knowledge were 5.4 times more likely to produce high-quality work.
AI Adoption in DevOps
- Around 90% of technology professionals use AI every day for software-development work, including coding, testing, deployment automation, and incident response.
- Approximately 74% of organizations apply DevOps practices throughout the software-development lifecycle. These practices provide the automated workflows and data pipelines required to integrate AI into development and operations.
- About 65% of software professionals rely heavily on AI tools for daily development tasks.
AI-Driven Productivity in DevOps
- Around 80% of organizations using AI-enhanced DevOps practices reported major productivity improvements. These gains included faster deployments, shorter change lead times, quicker incident response, and lower costs.
- Approximately 60% of developers used AI to solve work-related problems at least half of the time. This reflects AI’s growing role in routine development workflows.
- About 50% of organizations using DevOps were classified as elite or high-performing teams. AI supports these teams by automating processes and improving development efficiency.
AI Use in CI/CD Workflows
- 73% of organizations do not use AI in their CI/CD workflows.
- 9% of organizations have only read about or lightly tested AI for CI/CD.
- 6% of organizations use AI occasionally in their CI/CD workflows.
- 2% of organizations use AI regularly in CI/CD processes.
- Only 1% of organizations have fully embedded AI into their CI/CD workflows.
- Another 9% of respondents were unsure how their organization uses AI in CI/CD.
(Source: jetbrains.com)
AI Tools Used for Coding and Development Work
- 93% of respondents used at least 1 AI tool for coding or other development-related work.
- 78% used at least 1 AI coding assistant, agent, or code editor.
- 34% used ChatGPT through its web, desktop, or mobile applications rather than through third-party tools.
- 32% used GitHub Copilot for development tasks.
- 30% used at least 1 AI code editor.
- 27% used at least 1 command-line AI tool, such as Claude Code, Gemini CLI, or Codex CLI.
- 21% used Anthropic Claude Code, and another 21% used Cursor.
- 13% used at least 1 JetBrains AI tool, while 11% specifically used JetBrains AI Assistant.
| AI tool or category | Share of respondents |
| At least 1 AI tool | 93% |
| At least 1 AI coding assistant, agent, or code editor | 78% |
| ChatGPT web, desktop, or mobile apps | 34% |
| GitHub Copilot | 32% |
| At least 1 AI code editor | 30% |
| At least 1 CLI AI tool, including Claude Code, Gemini CLI, or Codex CLI | 27% |
| Anthropic Claude Code | 21% |
| Cursor | 21% |
| At least 1 JetBrains AI tool | 13% |
| JetBrains AI Assistant | 11% |
DevOps Research and Assessment Statistics
- Elite teams represent 19% of teams. They deploy on demand, often multiple times a day, deliver changes in under 1 day, have an approximately 5% change-failure rate, and recover from incidents in under 1 hour.
- High-performing teams account for 22% of teams. They deploy from weekly to monthly, have lead times of 1 day to 1 week, and recover within 1 day. Their change-failure rate varies in the 2024 data.
- Medium-performing teams make up 35% of teams. They deploy weekly to monthly, take 1 week to 1 month to deliver changes, and recover from incidents within 1 day to 1 week.
- Low-performing teams represent 25% of teams. They deploy monthly to every 6 months, have lead times of 1 to 6 months, record change-failure rates of 46%–60%, and need 1 week to 1 month to recover from incidents.
Containers and Cloud-Native Adoption in DevOps
- In 2025, 82% of container users ran Kubernetes in production, up from 80% in 2024 and 66% in 2023.
- Nearly all organizations, or 98%, reported some level of cloud-native adoption.
- In 59% of organizations, most or nearly all software development was cloud-native.
- Around 66% of organizations used Kubernetes to run generative AI inference workloads.
- However, 44% of organizations had not yet deployed AI or machine-learning workloads on Kubernetes.
Closing
AI now reaches nearly every part of software development, from writing and debugging code to testing and documentation, with coding and search the most common uses. Productivity, confidence, and focus all improve when developers use AI tools like GitHub Copilot, yet most organizations still keep AI out of CI/CD pipelines, citing trust concerns.
Elite teams remain a minority, and executives doubt their own ability to guide AI adoption. Continued investment from GitHub, GitLab, and Datadog is narrowing that gap.
FAQ
AIOps refers specifically to applying AI and machine learning to IT operations tasks like monitoring, anomaly detection, and incident response, while DevOps is the broader practice of combining development and operations teams and workflows, with AIOps acting as a supporting technology within it.
AI analyzes system logs, performance metrics, and alerts in real time to detect anomalies, predict potential outages, and automatically trigger remediation steps, reducing downtime and the time needed to resolve incidents.
Yes, AI coding assistants can generate code snippets, suggest improvements, detect security vulnerabilities, and review pull requests, though human developers typically still verify and approve changes before deployment.
AI can optimize build and deployment pipelines by predicting failures before they happen, automating rollback decisions, intelligently allocating testing resources, and reducing the manual effort needed to maintain continuous integration and delivery processes.
Key challenges include the complexity of integrating AI tools into existing workflows, data quality issues, over reliance on automated decisions, security risks from AI generated code, and the need for skilled teams to manage these systems.
