Artificial Intelligence-Leveraged Leadership to Resolve Resistance to Change: A Way Toward Second-Era Contemporary Businesses

Document Type

Presentation

Abstract

The construct of second-era contemporary businesses (S-ECB) arose with re-engineered fourth generation management (R4thGM) in 2022 to describe the new generation of companies oriented toward sustainability and customers. This generation features Industry 4.0 technologies and high awareness of the circular economy, competitiveness, and diverse stakeholders and is managed as a more contemporary (more sustainable and open) system in a volatile, uncertain, complex, and ambiguous landscape (VUCA landscape) (Hallioui et al., 2023; Hallioui et al., 2022). From a systemic viewpoint, change management mechanisms evolve according to the organization's context. Leadership is a cornerstone of business management. Industry 4.0 digitalization technologies, including artificial intelligence (AI), can enhance leadership to address resistance to change and support managers in leading the digital transition. Indeed, AI is a catalyst for S-ECB from a leadership and change management perspective. However, there remains a scarcity of literature discussing the power of AI-leveraged leadership in strengthening change management. This paper suggests that AI-leveraged leadership is best suited to bridge the gap between AI adoption, leadership, and organizational change management and one of the ways toward S-ECB. The expected outcomes of this initial framework proposal relate to the crucial role of AI in assisting business managers in the change management process, such as enabling real-data-driven decision-making through sentiment analysis and predictive analytics, providing personalized training and development for employees through adaptive learning systems (ALS) and virtual/augmented reality (V/AR), offering chatbots and natural language processing-based customized communication strategies, monitoring the implementation of change initiatives and ensuring real-time stakeholder’s feedback, supporting AI-powered platforms-driven virtual collaboration among work teams, and assessing change readiness through AI-enabled diagnostic tools.

Author Bio

Anouar Hallioui is a remote, international collaborator (research follow) of INTI International University (Negeri Sembilan, Malaysia). He received his Ph.D. in Industrial Engineering from the Faculty of Sciences and Techniques of Fez, Sidi Mohamed Ben Abdellah University (Fez, Morocco). He obtained a professional certificate in university teaching from the University of Hong Kong (Hong Kong, China). He earned a State Engineer's Degree (Dipl.-Ing.) in Mechatronics Engineering from the Faculty of Sciences and Techniques of Fez in 2017. He has industrial experience as a Production Manager, Industrial Cleaning Project Manager, and Site Process works & EHS Manager for industrial corporations. He has had a varied and exciting early career, moving from different industries to academia. His areas of interest include but are not limited to Engineering Management, Contemporary Business Management (Re-engineered 4th Generation Management), Systems Thinking, Systems Approach, System Analysis, Systems Theory, Manufacturing Systems, Production Systems Dependability, Optimization of Systems Maintenance and Productivity in Different Industries, Systemic Decision Making, Sustainability, Circular Economy, Industry 4.0, Risk Assessment, Safety Management, Sustainable and 4.0 Energy Industry, Sustainable and 4.0 Oil and Gas Industry, etc. His research works in these fields are published in International Conferences' Proceedings and International Journals indexed in the highest quality research databases (e.g., Scopus-Elsevier, WoS, etc.). On February 8, 2023, he won the "International Best Researcher Award on New Science Inventions." He is a professional member of the IEOM Society International (Michigan, USA). Furthermore, he is a member of the review board for many international journals, such as the Journal of Cleaner Production, Expert Systems with Applications, Discrete Dynamics in Nature and Society, Management and Production Engineering Review, the International Journal of Critical Infrastructures, the International Journal of System of Systems Engineering, Virtual Economics, etc. In addition, he is on the Editorial Board of Frontiers in Complex Systems as a review editor of the new journal Control and Engineering of Complex Systems.

Keywords

Artificial intelligence; leadership; resistance to change; change management; second-era contemporary businesses; re-engineered fourth generation management

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Artificial Intelligence-Leveraged Leadership to Resolve Resistance to Change: A Way Toward Second-Era Contemporary Businesses

The construct of second-era contemporary businesses (S-ECB) arose with re-engineered fourth generation management (R4thGM) in 2022 to describe the new generation of companies oriented toward sustainability and customers. This generation features Industry 4.0 technologies and high awareness of the circular economy, competitiveness, and diverse stakeholders and is managed as a more contemporary (more sustainable and open) system in a volatile, uncertain, complex, and ambiguous landscape (VUCA landscape) (Hallioui et al., 2023; Hallioui et al., 2022). From a systemic viewpoint, change management mechanisms evolve according to the organization's context. Leadership is a cornerstone of business management. Industry 4.0 digitalization technologies, including artificial intelligence (AI), can enhance leadership to address resistance to change and support managers in leading the digital transition. Indeed, AI is a catalyst for S-ECB from a leadership and change management perspective. However, there remains a scarcity of literature discussing the power of AI-leveraged leadership in strengthening change management. This paper suggests that AI-leveraged leadership is best suited to bridge the gap between AI adoption, leadership, and organizational change management and one of the ways toward S-ECB. The expected outcomes of this initial framework proposal relate to the crucial role of AI in assisting business managers in the change management process, such as enabling real-data-driven decision-making through sentiment analysis and predictive analytics, providing personalized training and development for employees through adaptive learning systems (ALS) and virtual/augmented reality (V/AR), offering chatbots and natural language processing-based customized communication strategies, monitoring the implementation of change initiatives and ensuring real-time stakeholder’s feedback, supporting AI-powered platforms-driven virtual collaboration among work teams, and assessing change readiness through AI-enabled diagnostic tools.