AI Strategy Playbook for Business Leaders: Turning Models into Profit (Paperback) by Joe Russell on MixCache.com
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AI Strategy Playbook for Business Leaders: Turning Models into Profit MTA
A strategic guide for executives and product managers to prioritize AI initiatives, measure ROI, and build high-impact AI products

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About this book:
AI Strategy Playbook for Business Leaders: Turning Models into Profit

This book, "AI Strategy Playbook for Business Leaders: Turning Models into Profit," serves as a comprehensive guide for executives and product managers navigating the complexities of integrating Artificial Intelligence into business operations. It addresses the critical gap between early AI experimentation and the consistent generation of measurable business value. The playbook emphasizes a disciplined, outcome-oriented approach, moving beyond technological hype to focus on strategic prioritization, economic viability, and the operational realities of AI.

The book is structured into three main parts: value discovery, portfolio choices, and execution at scale, culminating in strategies for market adoption and organizational change. Key themes include rigorously identifying and quantifying AI opportunities ("Sizing the Prize"), ensuring the readiness and governance of data, and making informed decisions on whether to build, buy, or partner for AI capabilities. A central focus is placed on establishing robust ROI models, defining meaningful KPIs and guardrails to prevent perverse incentives, and validating AI's impact through experimentation rather than intuition.

Further, the playbook delves into the operational and organizational aspects essential for scaling AI. It outlines the critical roles and skills needed for cross-functional AI teams, explores various organizational designs (hubs, pods, hybrid models), and discusses effective funding mechanisms. Crucially, it details the transition from development to production through MLOps and DataOps, the importance of Human-in-the-Loop workflows for quality management, and comprehensive strategies for responsible AI, including risk, legal, and ethical considerations.

Finally, the book provides guidance on taking AI products to market, covering positioning, differentiation, pricing, packaging, and the vital role of customer success in value realization and expansion. It also addresses the continuous challenges of vendor management, ecosystem leverage, scaling through platformization and reuse, and securing AI systems from evolving threats. The concluding chapter offers a practical "90-Day Playbook" with checklists and templates, enabling leaders to immediately apply the book's principles and initiate their AI transformation journey with focus and discipline.

What You'll Find Inside:
  • A step-by-step framework for identifying high-impact AI use cases, quantifying their business value, and ensuring data readiness before implementation
  • Practical approaches to prioritize AI initiatives at scale using scoring models and portfolio management techniques that balance business value, feasibility, and strategic alignment
  • Guidance on building robust ROI models with proper baselines, counterfactuals, and attribution methods to measure and prove AI's true business impact
  • Strategies for organizing AI teams, establishing effective operating rhythms, and managing organizational change to drive adoption and realize value from AI investments
  • Actionable advice on productizing AI solutions through MLOps and DataOps practices, designing human-in-the-loop workflows, and creating go-to-market strategies that monetize outcomes rather than just models
Who's It For:

This book is designed for executives, general managers, and product leaders who own P&Ls and customer outcomes and need to make strategic decisions about AI investments. It's ideal for business leaders who want to treat AI as a disciplined business investment rather than a technological experiment, focusing on measurable outcomes and profitable implementation. The playbook provides practical guidance for those responsible for prioritizing AI initiatives, measuring ROI, building effective teams, and driving organizational change to turn AI models into sustainable profit.

Author:

Joe Russell

Published By:

MixCache.com


Date Published:

March 3, 2026

Language:

English

Word Count:

48,240 words

Reading Time:

3 hours 23 minutes

Sample:

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