Using AI to make developers faster does not make much sense if your delivery pipeline is slow. Best practices in 2026 are to upgrade your CI/CD infrastructure with AI automation (AIOps, agentic workflows, AI agents as pipeline operators), selective testing (running only relevant tests based on code changes), and parallel processing. However, according to Belitsoft,
AI coding assistants and generative AI tools significantly boost developer productivity
Engineers spend about 30% less time writing code when they use GitHub Copilot, and other AI coding assistants.
These tools handle the repetitive work of writing boilerplate, setting up API endpoints, and generating test code.
AI takes over the simple tasks, so engineers get more time to solve hard problems and figure out complex logic. This theoretically should help teams build software faster, release sooner, and deliver features earlier.
But to keep the product from breaking, engineers must still review all AI-generated code before they deploy it.
More PRs don’t Mean Faster Feature Delivery
AI accelerates code creation but exposes limitations in software delivery. Inside OpenAI, 95% of developers use Codex and submit about 60% more pull requests each week.
According to the AI Productivity Paradox report, engineers using AI tools may merge 98% more pull requests and complete 21% more tasks. However, features are not delivered to customers at a proportionally faster rate because pull request review times rise accordingly.
The 2025 DORA report shows that this individual speed does not make the overall engineering process faster, because testing and release pipelines have not expanded to handle the extra code.
Engineers can produce code “10x” faster, but traditional testing and deployment systems are too slow to process AI-generated code if the number of reviewers and testing hours has not grown to match the volume.
Companies write code faster but have difficulty integrating new components with existing systems. Automated testing pipelines cannot adequately validate the increased code volume, resulting in increased merge failures and degraded system stability.
According to the 2026 State of Software Delivery report, main branch success rates have dropped to 70.8%, a five-year low, well below the recommended 90% benchmark, meaning nearly 3 out of 10 merges are failing.
Recovery may take an average of 72 minutes, up 13% year over year, when engineers try to merge more code without adequate integration testing. The bottleneck is no longer how fast can we write code, but how fast can we safely get it through review and into production.
CI/CD Pipeline is a Bottleneck
AI produces pull requests faster than CI pipelines can process them. Code review, testing, security, and deployment struggle to keep pace with AI‑generated code volume, causing queues and delays.
Teams that once handled, say, dozens of pull requests daily now may face hundreds, creating testing backlogs that last for days.Faster coding does not guarantee improved reliability.
More code means more connections between components, resulting in larger systems that are harder to test and run.
Writing code faster may lead to more bugs without scaling review and testing.
Blocked testing pipelines slow down the entire business. As code waits to be approved, development takes longer and morale may drop.
Potential Solutions
Traditional CI/CD setups struggle to handle the large volume and complexity of AI-generated code. To keep up, teams need autonomous tools, such as AI agents, to fix flaky tests and repair broken builds. By analyzing application changes, AI can also assist with test generation and prioritization, so you can execute only the most impactful test cases. Running tests only as needed lowers build times and costs.
Companies adopting AI coding tools need DevOps engineers who understand agentic workflows, AIOps tooling, and parallel validation pipelines, not just traditional CI/CD configuration.
There are DevOps staff augmentation agencies like Belitsoft who can bring in DevOps engineers already experienced with agentic pipeline tooling, without a 3-6 month internal hiring cycle. For companies that don’t want to build this capability in-house at all, Belitsoft manages the pipeline modernization and ongoing orchestration.
About the Author:
Dmitry Baraishuk is a Partner and Chief Innovation Officer at Belitsoft. Belitsoft is a software engineering company specializing in DevOps, AI integration, and enterprise application modernization. The company serves clients across healthcare, fintech, and enterprise SaaS in the US, UK, and Canada. Belitsoft publishes technology trend analyses to help business and technology leaders make informed decisions about their software investment strategy.
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Company Name: Belitsoft
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Country: Poland
Website: https://belitsoft.com/
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Belitsoft Reviews: How DevOps Survives in the Era of AI-Generated Code first appeared on Web and IT News.
