The Microsoft AI Insider's Playbook

Most books on Microsoft AI explain what the tools do. This one explains why they were built that way and how to use that knowledge to your advantage.

Krishna C. Mukherjee is a pioneering Microsoft technologist whose teams shipped the intelligent assistance features now used by hundreds of millions of people, including AutoCorrect, AutoFormat, and the spelling and grammar checkers in Microsoft Office. He architected the Intelligent Filing Manager, an AI-driven compliance system whose declarative, service-oriented design foreshadowed patterns that would later become common in SaaS platforms. He led the creation of the Bloomberg Valuation Service, applying predictive analytics to millions of financial instruments across asset classes. His career spans mainframes, personal computing, and the current era of cloud computing and AI, with a consistent focus on turning advanced capabilities into practical enterprise tools.

Tracing the evolution from Microsoft s early innovations to the intelligent enterprise, the book shows how usability, scalability, and automation shaped the modern platform. It then moves into practice: Agile delivery and efficient process design; Azure, SaaS, and strategic cloud AI architecture; Azure AI Services; the Copilot ecosystem; and the Power Platform all grounded in real patterns, case studies, and lessons from finance, health care, retail, and legal services.

The result is a clear, experience-driven guide to building modern cloud AI systems connecting Microsoft s platform decisions with the systems you are building today.

What You Will Learn

• Architect and implement Azure AI Services (now Foundry Tools), including Azure OpenAI Service, Speech, Language, Vision, and Document Intelligence, to build enterprise-grade conversational AI and virtual assistants
• Apply reference patterns for data pipelines, retrieval-augmented applications, model serving, and SaaS integration, while balancing latency, reliability, quality, and cost
• Map business processes, assess data readiness, quantify impact, and prioritize a staged roadmap from proof of concept to production
• Design cloud AI architectures that balance performance, cost, and governance from the outset
• Govern AI deployments in practice, covering security, privacy, compliance, monitoring, drift detection, incident response, and cost control