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Full Description
Deliver measurable business value by applying strategic, technical, and ethical frameworks to AI initiatives at scale
Free with your book: DRM-free PDF version + access to Packt's next-gen Reader*
Key Features
Build AI strategies that align with business goals and maximize ROI
Implement enterprise-ready frameworks for MLOps, LLMOps, and Responsible AI
Learn from real-world case studies spanning industries and AI maturity levels
Book DescriptionAI is only as valuable as the business outcomes it enables, and this hands-on guide shows you how to make that happen. Whether you're a technology leader launching your first AI use case or scaling production systems, you need a clear path from innovation to impact. That means aligning your AI initiatives with enterprise strategy, operational readiness, and responsible practices, and The AI Optimization Playbook gives you the clarity, structure, and insight you need to succeed.
Through actionable guidance and real-world examples, you'll learn how to build high-impact AI strategies, evaluate projects based on ROI, secure executive sponsorship, and transition prototypes into production-grade systems. You'll also explore MLOps and LLMOps practices that ensure scalability, reliability, and governance across the AI lifecycle.
But deployment is just the beginning. This book goes further to address the crucial need for Responsible AI through frameworks, compliance strategies, and transparency techniques. Written by AI experts and industry leaders, this playbook combines technical fluency with strategic perspective to bridge the business-technology divide so you can confidently lead AI transformation across the enterprise.
*Email sign-up and proof of purchase requiredWhat you will learn
Design business-aligned AI strategies
Select and prioritize AI projects with the highest potential ROI
Develop reliable prototypes and scale them using MLOps pipelines
Integrate explainability, fairness, and compliance into AI systems
Apply LLMOps practices to deploy and maintain generative AI models
Build AI agents that support autonomous decision-making at scale
Navigate evolving AI regulations with actionable compliance frameworks
Build a future-ready, ethically grounded AI organization
Who this book is forThis book is for AI/ML leaders and business leaders, CTOs, CIOs, CDAOs, and CAIOs, responsible for driving innovation, operational efficiency, and risk mitigation through artificial intelligence. You should have familiarity with enterprise technology and the fundamentals of AI solution development.
Contents
Table of Contents
Understanding the Perils of AI Products
Building the Enterprise AI Strategy
Selecting High-Impact AI Projects
Beyond the Build: Gaining Leadership Support for AI Initiatives
Building an AI Proof of Concept and Measuring Your Solution
Beyond Accuracy: A Guide to Defining Metrics for Adoption
From Model to Market: Operationalizing ML Systems
From Metrics to Measurement: Experimentation and Causal Inference
Generative AI in the Enterprise: Unlocking New Opportunities
Understanding GenAI Operations
AI Agents Explained
Introduction to Responsible AI
Implementing RAI Frameworks, Metrics, and Best Practices
Building Trustworthy LLMs and Generative AI
Regulatory and Legal Frameworks for Responsible AI
The Future of AI Optimization: Trends, Vision, and Responsible Implementation
