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AI Monitoring and Change Management

In: Managing Artificial Intelligence

Author

Listed:
  • Nils Urbach

    (Frankfurt University of Applied Sciences)

  • Daniel Feulner

    (University of Bayreuth)

  • Annalena Schmid

    (University of Hohenheim)

  • Dominik Protschky

    (University of Bayreuth)

Abstract

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.

Suggested Citation

Handle: RePEc:spr:fuobcp:978-3-032-13308-3_12
DOI: 10.1007/978-3-032-13308-3_12
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