As enterprises increasingly integrate AI-powered development tools into software engineering and DevOps workflows, the ability to generate, modify, and deploy code is undergoing a significant transformation. AI-assisted coding and automation are enabling development teams to accelerate code creation, improve individual productivity, and streamline certain aspects of the software development lifecycle. Yet as organizations produce code at unprecedented speeds, a critical question is emerging: Are AI-driven productivity gains actually accelerating enterprise software delivery, or are they creating new bottlenecks across testing, validation, security, and deployment?
For many enterprises, the growing volume of AI-generated code is placing additional pressure on existing DevOps processes, CI/CD pipelines, quality assurance practices, and software governance frameworks. While developers may be producing code faster, engineering and operations teams must still ensure that applications meet requirements for reliability, security, maintainability, and production readiness. At the same time, organizations face growing concerns about technical debt, code quality, fragmented development toolchains, and the operational risks associated with accelerating software delivery without sufficient oversight. As enterprises expand their use of AI across development and DevOps environments, technology leaders must become increasingly disciplined about balancing development speed with software quality, operational resilience, and measurable business outcomes.
Topics of discussion will include, but not be limited to:
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How enterprises are evaluating whether AI-assisted development is delivering measurable improvements in end-to-end software delivery performance versus simply accelerating code creation
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Strategies for addressing emerging bottlenecks across code reviews, automated testing, quality assurance, security validation, and CI/CD pipelines as AI-generated code volumes increase
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How DevOps and platform engineering teams are adapting automation, governance frameworks, and deployment practices to manage AI-generated code while minimizing technical debt, security vulnerabilities, and operational risks
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How engineering and technology leaders can balance development velocity, software quality, production reliability, and operational efficiency while establishing meaningful metrics for evaluating AI-driven DevOps investments
Sandip Pandey, Lead Software Engineer (Android), Mobile Systems, Artificial Intelligence & Machine Learning, PAYPAL
Praveen Cherakkara, Technical Program & Engineering Leader, WHATABURGER
Rahul Chawla, Lead Business Intelligence Data Warehouse Engineer, GOOGLE
Nirmal Gupta, Chief Technology Officer, CRYPTIN
Moderator: Poornakala Sethuraman Ph.D., Artificial Intelligence Advisor & Officer, EMPRESS HEALTH AI & Former Vice President Data Science & Product Strategy, BANK OF AMERICA
