SHAIPE

Summary: A heartfelt acknowledgment of the key individuals who made this book possible, including Rachel Woods who coined the term AI Operations, Brett Hurt who encouraged the writing process, and others who provided support and expertise throughout the journey.

Summary: This chapter explores the evolution of AI from back-office tools to transformative generative AI, examining the challenges organizations face in adoption and introducing AI Operations as the framework to bridge the gap between AI potential and real-world implementation.

Summary: This chapter introduces AI Operations (AI Ops) as a structured approach to embedding AI into organizational processes, emphasizing practical outcomes over technical achievements and explaining how it complements existing operational disciplines to empower 'superhuman' employees.

Summary: This chapter outlines how AI Operations serves as a scalable framework for organizations of all sizes, from startups to global enterprises, and identifies the different personas within organizations who can benefit from implementing AI Ops principles.

Summary: This chapter explores how organizations can create an environment where AI adoption thrives by addressing the human side of transformation through proven change management principles and tailored educational approaches for different employee personas.

Summary: This chapter explores how organizations can leverage their existing data assets—however imperfect—to begin realizing AI's potential, while simultaneously improving their data infrastructure to support more advanced AI applications through various stages of data maturity.

Summary: This chapter explores how organizations can empower their teams to engage meaningfully with AI tools, moving beyond technical implementation to foster a culture where AI becomes a trusted and effective partner in day-to-day operations through proper training and literacy programs.

Summary: This chapter explores how to recognize and cultivate the human qualities that make employees truly AI-ready—those who can transform AI literacy into meaningful business outcomes through curiosity, adaptability, critical thinking, and collaboration skills.

Summary: This chapter examines how to ensure that initial enthusiasm for AI translates into meaningful, high-quality outcomes rather than superficial implementations, providing strategies to help employees shift from passive AI users to strategic AI collaborators.

Summary: This chapter explores how to create a human-centric approach to AI that empowers employees to achieve superhuman results through strategic collaboration with AI tools, focusing on augmentation rather than replacement and building AI systems that complement human strengths.

Summary: This chapter examines how AI Operations can democratize innovation, allowing employees at all levels to bring their ideas to life without traditional bureaucratic barriers, reducing the cost of experimentation and enabling grassroots innovation throughout the organization.

Summary: This chapter outlines the three-phase journey of AI Operations adoption, from initial quick-win prototypes that inspire curiosity, through systematic scaling and education, to enterprise-wide AI integration that transforms business processes and decision-making.

Summary: This chapter provides a structured framework for discovering and prioritizing AI initiatives that drive real impact, focusing on the AI Ops interview process to uncover valuable opportunities by engaging directly with employees and differentiating between practical applications and overhyped distractions.

Summary: This chapter explores how to build an effective AI Operations team with the right blend of technical expertise, strategic thinking, and adaptability, introducing the STACK-B framework for core team qualities and providing guidance on team structures for different organizational sizes and budgets.

Summary: This chapter provides a practical guide to transforming AI from an idea into a fully integrated capability, covering the essential building blocks of AI Ops solutions including large language models, automation frameworks, and data integration strategies across different phases of implementation.

Summary: This chapter explores the critical elements of AI discoverability and education, focusing on how organizations can ensure employees can easily find, access, and effectively use AI tools through centralized portals, integration with existing systems, and comprehensive training programs.

Summary: This chapter examines the balance between text-based interfaces and traditional graphical user interfaces (GUIs) in AI adoption, exploring how each serves different user needs and how organizations can determine the most effective interface approach for various AI applications.

Summary: This chapter explores how AI measurement evolves across three phases of adoption, balancing qualitative and quantitative metrics to capture AI's true value at every stage, from initial qualitative feedback to sophisticated financial tracking as AI becomes deeply integrated into business systems.

Summary: This chapter explores a vision of knowledge work 5–10 years ahead, examining how AI Operations will evolve from automation to augmentation, with knowledge workers becoming orchestrators managing networks of AI agents, while addressing the cultural, ethical, and strategic challenges of this transformation.

Summary: This final chapter recaps the essential concepts of AI Operations covered throughout the book and provides practical guidance on how organizations can begin implementing AI Ops, emphasizing the importance of strategy, culture, and execution working in tandem to achieve sustainable AI success.