How Does the Use of Language Matter in Organizational Change Processes? Setting Up an AI-Based Analytics Approach

Sabine Van Almsick and Christian Stary

Chapter 16 in AI-Driven Revolution:Transforming the Business Landscape, 2025, pp 367-386 from World Scientific Publishing Co. Pte. Ltd.

Abstract: Change management requires communication. For instance, company succession is a crucial phase for medium-sized companies. Like any change process, it is characterized by a multitude of challenges, including the role of internal communication and linguistic expressions. A central concern in this context is the identification of resistance that can arise in managing change. The use of language plays a crucial role in verbal resistance. It manifests itself in employee discussions as well as in internal chats and emails. Corresponding data serve as an indicator of potential difficulties in the change process. In order to recognize and address verbal resistance, we introduce a design science approach focusing on a systematic analysis of internal change communication. It includes social business platforms for formal and informal communication, as these promote the understanding and implementation of corporate goals. In this chapter, we capture the initial design science cycle as it targets the identification of performance indicators for the way language is used in organizational communication as a part of an organization’s culture. It is also investigated how these indicators relate to traditional KPIs, such as sales figures. The subsequent design cycles apply AI-based data analytics, such as semantic clustering, to develop a communication-based resonance and feedback system that includes early warnings of resistance to change in management processes. Intervention mechanisms can then be based on organizational communication evidence. It is expected that a targeted communication strategy and the early recognition of resistance are essential factors for a successful transition in company succession or when a company is sold.

Keywords: Artificial Intelligence; Data Analytics; AI; Digital Landscape; Organizational Strategies; AI Technologies; Machine Learning; Natural Language Processing; Robotics; Digital Transformation; Business Models; Efficiency; Value Propositions; Advanced Analytics; Predictive Modelling; Customer Experiences; AI-driven; Ethical AI; Data Privacy; Algorithmic Bias; Regulation Compliance; Responsible AI; Sustainable AI; Practical Applications; Business Innovation; Emerging Technologies; Industry 4.0; High Tech; Ethics Regulation; Business Leadership; Pattern Recognition; Information Technology; Entrepreneurs; Management (search for similar items in EconPapers)
JEL-codes: L1 L2 L21 L26 M1 (search for similar items in EconPapers)
Date: 2025
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