Abstract
Purpose. The purpose of this qualitative study was to explore how a customized generative artificial intelligence (AI) model could evaluate and enhance leadership competencies among mid-level business leaders in modern organizations. This research investigated the use of an AI tool to assess decision-making, strategic thinking, emotional intelligence, team motivation, cultural sensitivity, and ethical reasoning, while offering real-time, adaptive feedback to users.
Theoretical Framework. Although the study was not anchored in a single leadership theory, it drew upon foundational ideas in leadership development, adult learning theory, and the application of artificial intelligence in organizational training. The approach integrated concepts from experiential learning and AI-driven cognitive feedback systems to frame the study’s design and data analysis.
Methodology. Twenty mid-level business leaders participated in a four-week leadership training program using a custom GPT-based AI model. Participants engaged with the AI through weekly leadership scenarios and reflective questions. Their responses were evaluated by the AI, which provided immediate feedback and alternative suggestions. Data was collected through participant responses, coded evaluations, and semi-structured post-intervention interviews. The qualitative data was coded using thematic analysis to identify patterns of growth and user perception across the training period.
Findings. The findings indicated that participants improved in several leadership competencies over the four-week period, particularly in decision-making and emotional intelligence. The AI model’s adaptive feedback mechanism played a critical role in driving reflection, confidence, and conceptual understanding. Participants also reported high engagement and found the tool to be accessible, relevant, and useful for professional development.
Conclusions and Recommendations. The results suggest that customized AI tools have the potential to complement traditional leadership development by offering scalable, personalized learning experiences. Participants perceived the model as effective in simulating real-world challenges and prompting deeper self-awareness. Further research is recommended to expand the participant pool, explore long-term impact, and examine cross-industry applicability. Future studies might also investigate ethical considerations in AI coaching and compare AI-led development with traditional instructor-led training formats to determine relative effectiveness.