How AI-Generated Code Creates Review Bottlenecks in Software Delivery

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Adoption of CI/CD

Over 83% of professionals utilizing DevOps methodologies implement CI/CD pipelines, making this approach the most prevalent within the group according to JetBrains’ The State of CI/CD in 2026 report. This adoption rate surpasses other practices such as containerization, orchestration, infrastructure as code, GitOps, and platform engineering. CI/CD remains central to modern software delivery processes.

Tool Selection and Adoption

Organizational tool selection is influenced by client requirements and legacy systems. GitHub Actions dominates adoption, followed by GitLab CI and Jenkins. Azure DevOps Server ranks next, ahead of TeamCity. Additional tools include AWS CodePipeline and CodeBuild, Bitbucket Pipelines, and custom solutions.

Enterprises Using Multiple CI/CD Platforms

Enterprises utilizing multiple CI/CD platforms often cite varying client specifications, diverse technology stacks, and inherited systems as reasons. Departments serving different clients may be required to use tools mandated by external stakeholders.

Developer Tool Preferences

Most developers indicate no intention to transition CI/CD tools, with those considering changes highlighting factors such as scaling expenses, maintenance demands, tool unification efforts, and configuration complexity.

Hosting Environments

On-premises and self-managed cloud infrastructure host the majority of primary CI/CD systems, with 72.8% of respondents operating in these environments. This marks the highest on-premises adoption rate recorded over the five-year span of the survey. Hosting decisions reflect teams’ priorities for control over hardware, networks, data, build environments, security, and costs, resulting in a hybrid deployment model landscape.

AI Applications in CI/CD Workflows

Current AI applications in CI/CD workflows focus on code review, test generation or correction, and build failure analysis. Organizations with self-hosted systems emphasize their continued relevance, particularly for entities requiring heightened infrastructure, data, security, or cost control.

“Most AI use currently happens around the pipeline. Teams use it to review code, write or fix tests, and diagnose build failures. At the same time, self-hosting remains an important part of the CI/CD landscape, especially for organizations that need more control over their infrastructure, data, security, or costs.” – Olga Bedrina, Product Marketing Manager for TeamCity

AI Integration in Testing Processes

AI integration in testing processes extends to maintaining pipeline configurations, quality and security checks that can halt builds, deployment assistance, and incident response post-deployment. Developers access CI/CD tools via MCP servers and agent capabilities, linking existing assistants to development workflows. Web interfaces remain the primary interaction method for these systems.

Barriers to AI Adoption

Organizations not utilizing AI in CI/CD cite low priority, data privacy and intellectual property concerns, satisfaction with current setups, and cost considerations as barriers. Smaller organizations are more likely to assert that their existing approaches meet requirements without AI.

Challenges in AI Implementation

Teams leveraging AI report challenges including increased review workloads due to generated changes, difficulties in assessing reliability, unpredictable AI-related expenses, and complexities in tracking AI-originated modifications and debugging introduced issues.