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How enterprise engineering teams can successfully adopt AI Written by GitHub with Table of contents Foreword AI-powered software development, explained The unique challenges of operationalizing AI in software development at the enterprise level How AI is reshaping the enterprise software development lifecycle The benefits of taking an platform-first approach to AI A roadmap for operationalizing AI in enterprise engineering teams Foreword The software development industry has reached a pivotal milestone in the evolution of generative AI (artificial intelligence). While much of the world has grappled with this technology and its use cases, one survey by GitHub found that 92% of developers said they were already using generative AI coding tools both in and outside of work in early 2023. This shows that developers are quickly embracing the benefits of AI in their software builds, often before organizations have considered how best to operationalize AI across engineering teams. The last time the software industry had to implement change management at this scale was with the introduction of DevOps as a development methodology over a decade ago. Now, the industry is in a watershed moment where developer tool chains are evolving faster than ever before with the widespread availability of AI—one Gartner study has even found that 80% of code will be produced by AI in 2026. As AI tools continue to evolve, engineering leaders now face a world where AI is infused throughout far more of the software development lifecycle (SDLC). So, how can enterprise engineering leaders think about AI’s evolving role in software development and ensure that teams are prepared to ship high- quality, secure software at scale? In this guide, we’ll explore best practices for adopting AI-powered software development in enterprise engineering teams and the benefits that come with unifying your tech stack with a single, AI-powered platform.
Page — 4How enterprise engineering teams can successfully adopt AI AI coding tools—also known as AI-powered coding assistants or AI programming tools—are software applications that utilize generative AI and machine learning techniques to help developers and programmers in various aspects of the software development process. These tools are designed to augment the capabilities of developers, improve code quality, and accelerate the software development process—and they have become increasingly valuable in modern software development by helping developers be more productive and efficient in their work. These tools used to primarily live mainly in the integrated development environment (IDE) to help developers write and refine code. Today, industry leaders are increasingly applying AI to more parts of the typical developer workflow ranging from the command line interface (CLI) to infrastructure orchestration, security vulnerability detection, and more. AI-powered software development, explained
