Additional information
by Chun Schiros (Author), Supreet Kaur (Author), Rajdeep Arora (Author)
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 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.
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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
- 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
