About this book:

*The Practical AI Playbook for Business Leaders* provides a comprehensive roadmap for organizations to transition from AI experimentation to sustainable, enterprise-scale implementation. The book begins by demystifying core concepts—distinguishing between generative and predictive models—and establishing a disciplined framework: Diagnose, Strategize, Pilot, Scale, and Govern. It emphasizes that success is not driven by technology alone but by anchoring AI to specific business problems where it can reduce costs, grow revenue, or mitigate risk.

The first half of the book focuses on the foundational requirements for a successful rollout, including data readiness, modern cloud and edge infrastructure, and the necessity of MLOps (Machine Learning Operations). The author argues that data quality is the "fuel" for AI and that robust lifecycle management is required to prevent "model decay." It also provides a strategic guide for the "build vs. buy" decision, suggesting that companies should buy for supporting functions while building custom solutions only for core competitive advantages.

The middle section addresses the human and organizational dimensions of AI. It outlines various team structures—ranging from centralized Centers of Excellence to embedded product teams—and clarifies roles like ML Product Managers and Prompt Engineers. A significant portion of the text is dedicated to change management, urging leaders to foster a culture of "human-AI collaboration" through upskilling and transparent communication to overcome employee resistance and fear of displacement.

The final chapters offer functional playbooks for departments such as Sales, Marketing, Customer Support, and Operations, providing specific use cases like lead scoring, automated content generation, and predictive maintenance. The book concludes with a rigorous look at ethics, governance, and impact measurement, insisting that responsible AI practices and clear ROI metrics (OKRs) are essential for long-term viability. It closes by highlighting emerging trends like multimodal models and autonomous agents, advising leaders to treat their AI strategy as a living, adaptable document.

What You'll Find Inside:

• A proven Diagnose→Strategize→Pilot→Scale→Govern framework for systematic AI implementation that turns curiosity into measurable business outcomes
• How to build compelling AI business cases with ROI modeling, impact vs. effort prioritization, and stakeholder-specific narratives that secure funding
• Practical methods for identifying high-value use cases through process mapping, stakeholder interviews, and hypothesis-driven ideation that avoids 'technology in search of a problem'
• Clear vendor vs. build decision criteria with evaluation checklists, cost modeling, and hybrid approaches that balance speed, control, and risk
• Building responsible AI teams through ethical frameworks, MLOps, change management, and domain-specific playbooks for sales, marketing, operations, and HR

Who's It For:

This book is designed for business leaders, executives, and functional managers who need to implement AI strategically in their organizations. It's particularly valuable for C-suite executives shaping AI vision and investment strategy, as well as leaders of data, product, engineering, sales, marketing, operations, and HR teams looking for domain-specific AI applications. The book bridges the gap between technical possibilities and business value, providing actionable frameworks for leaders who need to drive measurable outcomes from AI initiatives without getting lost in technical complexity.