Generative AI is reshaping software engineering at a fundamental level. Modern AI systems can generate code, tests, documentation, and even architectural recommendations at unprecedented speed—yet this acceleration introduces new risks: verification gaps, architectural drift, and a gradual loss of shared engineering understanding. Instead of asking how to operate AI tools, this book investigates the deeper shift they trigger—how generative AI redefines software engineering’s methods, responsibilities, and decision-making.

Through fourteen chapters, the book traces how AI alters every layer of the engineering process. It begins with a real incident where an AI system rewrites a legacy module and “validates” its own output, exposing the dangers of self-confirming reasoning. From there, the book reframes AI not as a productivity helper, but as a cognitive multiplier—one that expands the solution space while increasing the need for human judgment, verification, and governance. The book covers AI driven product discovery, architectural decision making under uncertainty, managing large volumes of AI generated code, and refactoring complex legacy systems. It introduces layered verification strategies, new collaboration patterns for AI augmented teams, chat based decision provenance, and modern productivity metrics suited to AI intensive environments. Governance, risk management, and future organizational models round out the guidance.

Aimed at experienced developers, architects, and technical leaders, this book provides the conceptual foundations and practical operating models needed to build reliable systems in an era where code is cheap but engineering judgment is priceless. It is a roadmap for steering software development through the next decade of AI driven transformation.

What you will learn:

● How generative AI fundamentally changes the economics of software development and engineering workflows

● How to structure AI-assisted development processes across discovery, architecture, coding, refactoring, and testing

● How to prevent architectural drift and verification gaps in AI-generated codebases

● How to introduce governance models and engineering guardrails for AI-augmented teams

● How to design a practical operating model for engineering organizations adopting generative AI

Who this book is for:

This book is for senior, staff, and principal software engineers, as well as software architects adapting to AI augmented development. It is equally valuable for engineering managers and technical leads guiding teams through new workflows and governance models. DevOps and platform engineers working with AI assisted tooling will also benefit. The book offers practical insight into integrating generative AI across the modern engineering stack.

Roberto Trunfio, PhD, is a Development Practice Lead at Gridspertise, an Enel Group company, where he focuses on cloud, edge computing, software architecture, and AI-augmented engineering practices. His work includes defining development guidelines, engineering standards, development practices, and operating models for distributed teams building complex cloud-edge systems.

He holds a PhD in Operations Research, specializing in simulation and optimization algorithms, and has published research in decision support and complex systems. Through applied research and hands-on experimentation, he has explored the integration of AI-assisted development, including MCP servers, development agents, and AI-driven workflows, helping define governance models for the safe adoption of generative AI in enterprise environments.

His current focus is on how generative AI transforms engineering decision-making, architectural design, verification practices, and the operating models of modern software organizations.