Software has become the primary engine of innovation, yet translating AI experimentation into production at scale remains challenging. Despite strong momentum, organizations struggle to convert pilots into robust, scalable engineering practices. Rising infrastructure costs, fragmented workflows, and increasing governance requirements are prompting a reassessment of how AI is operationalized across the software development lifecycle (SDLC).
Concurrently, a new generation of AI-assisted engineering capabilities is enhancing how software is developed and secured. Tools such as GitHub Copilot accelerate coding, testing, and problem-solving, while GitHub Advanced Security enables early vulnerability detection and strengthens software supply chain integrity.
In this edition of MIT SMR Connections, Xebia and GitHub, in collaboration with MIT SMR India, convene CIOs, CTOs, VPs, senior IT leaders, and transformation heads to examine how organizations can move beyond isolated AI pilots toward production-grade engineering platforms.
Briefing Points
1. The CXO Blueprint for Scaling AI Pilots
Most enterprises have successfully run AI pilots; however, only a few have cracked the code on scaling them into reliable, repeatable engineering practices. CXOs need a structured roadmap that addresses the full journey.
2. AI as Force-Multiplier for Engineering Teams
AI in software engineering has evolved beyond code completion to autonomously handling tasks across the development lifecycle—from planning and coding to testing and review. The question now is whether the greatest gains will come from embedding these capabilities directly into existing workflows.
3. Building Secure and Resilient Software Delivery Ecosystems
Learn how GitHub Advanced Security helps detect vulnerabilities early, strengthen code quality, and safeguard your software supply chain—while maintaining development speed.
4. Measuring Excellence in the AI Era
In the AI era, traditional engineering metrics—such as lines of code, sprint velocity, and defect counts—are insufficient to capture true performance. How can CXOs adopt more meaningful measures to assess outcomes and evaluate business impact?
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