Enterprise AI Systems Engineering

Reactive Publishing

Enterprise AI Systems Engineering is the layer no one talks about, yet it is the layer that decides whether an AI initiative becomes a repeatable revenue-producing asset or a dead prototype hiding in a Confluence page. This book is written for builders, operators, and executives who need to take AI beyond demos, beyond excitement, and into production systems that scale, self-improve, and produce measurable ROI inside real organizations.

Inside, Hayden lays out a comprehensive operating framework for enterprise-grade AI, moving from foundational model selection and prompt pipelines to orchestration, data governance, model lifecycle management, observability, security, and value measurement. The focus is deeply pragmatic: how to architect systems that can be audited, automated, integrated, and optimized over time while minimizing failure modes and regulatory exposure.

What you'll learn includes:
• Foundation model selection & specialization for enterprise use cases
• Systems engineering patterns for agentic workflows
• Data pipelines, knowledge layers, and retrieval architectures
• Fine-tuning, distillation, and domain adaptation strategies
• Model governance, evaluation, alignment, and observability
• API hardening, security, and risk management
• Enterprise deployment patterns across cloud, hybrid, and on-prem
• Unit economics, cost modeling, and ROI frameworks
• From POCs to production: runway, adoption, and scaling playbooks

It bridges multiple disciplines: machine learning, software engineering, product strategy, cybersecurity, and financial analysis. The end goal is clear: give technical and strategic leaders a toolkit for turning AI into a competitive advantage rather than an experiment.

If you're an engineering leader, architect, CTO, CFO, AI program manager, or product executive charged with delivering results, this book gives you the vocabulary, mental models, and systems architecture needed to make AI real in the enterprise world, not just theoretically possible.