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This chapter addresses three core pillars of managing AI at scale. First, we discuss key performance indicators (KPIs) that link AI initiatives to strategic goals and operational performance. Second, we outline an iterative approach to machine learning monitoring, detailing how to observe, evaluate, and improve model behavior in dynamic environments. Finally, we introduce a structured framework for AI-related change management, offering practical tools to navigate employee concerns, foster acceptance, and anchor AI sustainably within the organization.

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AI Monitoring and Change Management

  • 章节

  • 发表日期：

    2026年

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  • Nils Urbach,
  • Daniel Feulner,
  • Annalena Schmid,
  • Dominik Protschky

摘要

This chapter addresses three core pillars of managing AI at scale. First, we discuss key performance indicators (KPIs) that link AI initiatives to strategic goals and operational performance. Second, we outline an iterative approach to machine learning monitoring, detailing how to observe, evaluate, and improve model behavior in dynamic environments. Finally, we introduce a structured framework for AI-related change management, offering practical tools to navigate employee concerns, foster acceptance, and anchor AI sustainably within the organization.