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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Spis treści

The AI Optimization Playbook. Drive business success with proven AI strategies, best practices, and responsible innovation eBook -- spis treści

• 1. Understanding the Perils of AI Products
• 2. Building the Enterprise AI Strategy
• 3. Selecting High-Impact AI Projects
• 4. Beyond the Build: Gaining Leadership Support for AI Initiatives
• 5. Building an AI Proof of Concept and Measuring Your Solution
• 6. Beyond Accuracy: A Guide to Defining Metrics for Adoption
• 7. From Model to Market: Operationalizing ML Systems
• 8. From Metrics to Measurement: Experimentation and Causal Inference
• 9. Generative AI in the Enterprise: Unlocking New Opportunities
• 10. Understanding GenAI Operations
• 11. AI Agents Explained
• 12. Introduction to Responsible AI
• 13. Implementing RAI Frameworks, Metrics, and Best Practices
• 14. Building Trustworthy LLMs and Generative AI
• 15. Regulatory and Legal Frameworks for Responsible AI
• 16. The Future of AI Optimization: Trends, Vision, and Responsible Implementation