DevOps pipelines often struggle with unstable test environments, long feedback loops, high manual effort, and poor test optimization. As applications grow more complex, teams face delays caused by manual debugging, unreliable test setups, and limited predictive insights. These issues impact delivery speed, test reliability, and developer productivity. To solve these problems, this session explains how Generative AI (GenAI) can make the entire DevOps process smarter and more automated. From the time new code is written, GenAI helps improve build quality by spotting and fixing setup issues right away. When it comes to testing, it looks at risk areas, focuses on the most important parts, and helps find problems early by learning from past data and system behavior.

GenAI also makes it easier to understand why something failed. It quickly checks logs and patterns to find the root cause, saving teams a lot of time for debugging. It can even fix some test environment issues on its own, which makes testing more stable and reduces incorrect results.

After testing, GenAI helps with smoother and safer deployments. It predicts possible issues before release by analyzing earlier trends, code quality, and performance. This gives teams better information to decide the best time and way to deploy, reducing the risk of things going wrong in production. Instead of reacting to issues late in the pipeline, GenAI enables a proactive approach identifying likely failure points early and recommending preventive actions. This improves overall pipeline stability and allows development teams to focus on building features rather than fixing broken tests.