Deliver measurable business value by applying strategic, technical, and ethical frameworks to AI initiatives at scale

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 Description

AI 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.

What 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 for

This 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.

Table of Contents

Part 1: Laying the Groundwork for Al Success

Chapter 1: Understanding the Perils of Al Products

Chapter 2: Building the Enterprise Al Strategy

Part 2: Aligning Projects with Business Impact

Chapter 3: Selecting High-Impact Al Projects

Chapter 4: Beyond the Build: Gaining Leadership Support for Al Initiatives

Chapter 5: Building an Al Proof of Concept and Measuring Your Solution

Part 3: Deploying and Proving ML Value

Chapter 6: Beyond Accuracy: A Guide to Defining Metrics for Adoption

Chapter 7: From Model to Market: Operationalizing ML Systems

Chapter 8: From Metrics to Measurement: Experimentation and Causal Inference

Part 4: Emerging Topics: Generative Al and Al Agents

Chapter 9: Generative Al in the Enterprise: Unlocking New Opportunities

Chapter 10: Understanding GenAI Operations

Chapter 11: Al Agents Explained

Part 5: Responsible Al and Governance

Chapter 12: Introduction to Responsible Al

Chapter 13: Implementing RAI Frameworks, Metrics, and Best Practices

Chapter 14: Building Trustworthy LLMs and Generative Al

Chapter 15: Regulatory and Legal Frameworks for Responsible Al

Chapter 16: The Future of Al Optimization: Trends, Vision, and Responsible Implementation

Chapter 17: Unlock Your Exclusive Benefits

About the Authors

Dr. Chun Schiros is an award-winning technology and AI thought leader and field CTO at a leading cloud company, advising boards and executives on cloud, data, and AI transformation. With decades of experience in financial services, healthcare, and technology, she has built a career bridging business vision and technology execution. She helps organizations maximize AI value through aligned data strategy and scalable models. Her leadership has earned industry recognition for innovation in data and analytics. She translates complex AI and data strategies into business impact and guides organizations to embed AI responsibly at scale. She holds a Ph.D. in electrical engineering with advanced training in probability, statistics, and data science.

Supreet Kaur is a senior AI cloud solutions architect at Microsoft, enabling financial institutions to scale generative AI solutions from proof of concept to production while evangelizing the latest advancements in AI. Previously at Morgan Stanley, she spearheaded the development of a large-scale machine learning-based personalization engine and earned a patent for an innovative evaluation strategy. A recognized thought leader, Supreet has delivered talks at 50+ global events, authored over 30 thought leadership articles, been named a LinkedIn Top Voice for AI content, and featured in more than 10 media outlets.

Rajdeep Arora is a principal data scientist and machine learning architect at a Fortune 1 company, where he drives innovation in personalization and recommendation systems that shape the online customer journey. With experience across startups, consulting, and Fortune 100 companies, he has led machine learning initiatives in knowledge graphs, supply chain optimization, recommendation systems, causal inference, and generative AI, all with a focus on creating human-centered digital experiences. His contributions to the field include patents, technical publications, and interviews on the intersection of AI and personalization. His work reflects a passion for scalable, business-driven solutions that transform how people experience technology.

Dr. Usha Jagannathan is a leading voice in Responsible AI and serves as the Director of AI Products at a global standards body. She specializes in accelerating AI productization, taking solutions from PoC to scalable, trustworthy enterprise systems. She holds a Ph.D. in Technology and E-learning with advanced training in AI for Business and Ethics. With 20+ years of product engineering and IT experience across McKinsey, Marsh, ASU, and Purdue Global, she brings expertise in data/AI engineering, governance, and risk management. Her thought leadership has earned her multiple global awards in Responsible AI and data science. She is passionate about mentoring, having supported over 1,000 young professionals into engineering and product roles.