A Practical Book on Artificial Intelligence

Beschreibung

Overview The book is a practitioner's guide to putting artificial intelligence into productive use inside organisations. Its argument is that the bottleneck is no longer model capability but adoption: access to a powerful model is not by itself a durable advantage, and lasting value comes from connecting models to proprietary context, real workflows, appropriate controls, operational ownership, and measurable outcomes. The manuscript works that thesis through the full stack, from how large language models behave to how agent systems are governed, operated, and paid for. The author states the promise plainly: after reading it, a practitioner should be better able to choose the right AI use cases, explain how modern AI systems behave, design architectures that reduce failure modes, apply governance without blocking progress, and move from prototypes to production with clear controls and measurable value. The treatment is deliberately non-mathematical, introducing the concepts without requiring a mathematical background, while remaining technical enough for the architects and engineers who have to ship. Two qualities distinguish the manuscript in a crowded category. First, it sits between the research book and the executive book, addressing the architects, engineers, product owners, and team leads who, as the author puts it, are often left to figure things out on their own. Audience The stated readership is practitioners responsible for bringing AI into real-world organisations: enterprise architects integrating AI into business processes, developers building intelligent applications, IT and platform teams enabling AI services, product owners designing AI-driven workflows, governance and compliance professionals evaluating risk, and leaders responsible for operational transformation. The breadth is intentional, since the author argues that building responsible enterprise AI is a team sport requiring a shared vocabulary, and a reader-pathways table early in the book routes each role through the chapters that matter most to it.
Strategic chapter Beyond the AI hype: the valley between capability and adoption, capital running ahead of revenue, open weights, job exposure, and autonomous-action risk
what an agent is, building blocks, agent patterns, and multi-agent systems. Introducing agents adoption strategy, regulatory compliance, use-case selection, and adoption across industries
Economics: cost engineering for tokens, latency and agent loops, and the intention economy

Autor*in

Markus Loosen
Markus Loosen is a Senior Architect at Microsoft Europe Consulting Organization. He holds a degree as Dipl. Phys.-Ing. and Electrotechnic Assistant, and brings more than 24 years of computing experience with deep industry knowledge in automotive, media, and banking. He combines industry, technical, and transformation expertise across data engineering and machine learning, with a strong focus on strategy and innovation consulting. As a C-level advisor and CTO of the Azure Innovation Accelerator, he leads large-scale AI transformation programs for enterprise customers. Outside work he's a father of one, with interests in cars, gadgets, programming, and cooking - an analogy he likes to draw with architecture itself.

Themen in »A Practical Book on Artificial Intelligence«

Artificial Intelligence Agent Large Language Modell (LLM) AI Transformation Agentic AI Machine Learning

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