AI Monitoring and Change Management

Nils Urbach (), Daniel Feulner (), Annalena Schmid () and Dominik Protschky ()
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Nils Urbach: Frankfurt University of Applied Sciences
Daniel Feulner: University of Bayreuth
Annalena Schmid: University of Hohenheim
Dominik Protschky: University of Bayreuth

Chapter 12 in Managing Artificial Intelligence, 2026, pp 267-290 from Springer

Abstract: 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.

Date: 2026
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Persistent link: https://EconPapers.repec.org/RePEc:spr:fuobcp:978-3-032-13308-3_12

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DOI: 10.1007/978-3-032-13308-3_12

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