Which driver emphasizes running focused pilots, measuring outcomes, and developing repeatable processes for scaling AI?

The described approach—running focused pilots, measuring outcomes, and creating repeatable processes—is the essence of an experience-driven AI strategy. This iterative methodology allows an organization to "learn by doing" in a controlled manner. It focuses on demonstrating value quickly, gathering insights, and building internal capabilities and confidence. This strategic process ensures that scaling is based on proven success and tangible business outcomes, rather than purely on technology acquisition or regulatory pressure. It is a practical way to manage the journey of AI transformation.

A. Regulatory governance focuses on compliance with external laws and policies, not on the internal strategic process of piloting and scaling.

C. Data governance is a critical enabler for AI but is a foundational component, not the strategic driver for iterative scaling.

D. Technology and data strategy defines the "what" (infrastructure, platforms), while this question describes the "how" (the process of scaling).

1. Microsoft AI Business School. (n.d.). AI strategy for business leaders. Microsoft Learn. Module: "Build an AI strategy to generate business value." This module outlines the importance of starting with pilot projects to prove value and develop a scalable approach.

2. Iansiti, M., & Lakhani, K. R. (2020). Competing in the Age of AI: Strategy and Leadership When Algorithms and Networks Run the World. Harvard Business Press. Chapter 5, "The Learning Loop," discusses the strategic importance of iterative experimentation and learning for scaling AI capabilities.

3. Stanford University. (n.d.). CS221: Artificial Intelligence: Principles and Techniques. Course materials often discuss the practical application of AI, emphasizing pilot projects and iterative development as key to successful implementation in business contexts.