AI-Enhanced Emotional Intelligence for Adaptive Leadership: Agile Strategies for Change Management

Abstract

Relevance of the research is determined by the need to integrate artificial intelligence and emotional intelligence into adaptive leadership models to enhance the effectiveness of decision-making, conflict resolution, and team cohesion in the context of digital transformation. Particular attention is given to the impact of emotional data on the quality of managerial decisions under uncertainty. It has been revealed that adaptive leaders can respond more effectively to changes when emotional analysis is incorporated.

The aim of the study is to develop a conceptual framework for integrating artificial intelligence and emotional intelligence into adaptive leadership through agile management practices, focusing on optimizing decision-making processes, conflict resolution, and improving team dynamics.

Research methodology includes the analysis of theoretical sources, systematization of approaches to integrating AI and emotional intelligence into leadership models, and the synthesis of practical recommendations for their implementation in organizational contexts.

Research results. It has been established that the implementation of AI for emotional data analysis contributes to the identification of conflict situations, reduction of emotional burnout risks, and increased adaptability of managerial decisions. It has been proven that the use of emotional intelligence in combination with AI provides a faster response to changes in the team’s emotional state and enhances leaders’ communication strategies. It has been identified that adaptive leadership supported by AI allows for systematic management of team emotional reactions during crisis situations.

Scientific novelty lies in the formation of a comprehensive approach to integrating AI and emotional intelligence into adaptive leadership, allowing simultaneous consideration of emotional aspects of team interaction and the use of analytical data for making informed decisions.

Conclusions. The proposed model of AI and emotional intelligence integration creates a basis for the systematization of data on the emotional background of the team and the adaptation of managerial decisions considering emotional indicators. The findings confirm the feasibility of incorporating emotional analysis into the decision-making process to increase management effectiveness.

Prospects for further research include the testing of the model in various sectors and the development of automated tools for analyzing emotional data in real-time. Additionally, further investigation is warranted on the impact of emotional intelligence on management effectiveness in situations characterized by heightened uncertainty.