The article examines the theoretical basis for the role of emotional intelligence in executive coaching practice. In a dynamic VUCA environment complicated by digital transformation and technological stress, the relevance of this research is determined by the need to enhance leaders’ adaptability through the development of emotional competencies. The work aimed to conduct a systematic analysis of classical and contemporary models of emotional intelligence (ability approach, mixed model, and trait approach), to assess their diagnostic instruments, and to substantiate the mechanisms for integrating EI into the executive coaching cycle. The novelty of the study lies in its multidisciplinary synthesis of data, encompassing the psychometric properties of the MSCEIT, EQ-i, and ESCI, as well as neuro-visualization experiments (fMRI) and HRV biofeedback, alongside consideration of coaching industry trends. For the first time, meta-analytic results on the effectiveness of individual and group coaching interventions have been combined with real-world cases of job crafting and mindfulness training, enabling the construction of a comprehensive methodology for diagnosis, the formulation of emotionally concrete objectives, and practical micro-practices. The main findings demonstrate that developing EI through the structured coach-cycle diagnosis → goals → interventions → verification yields a statistically significant improvement in management outcomes, a reduction of subordinates’ techno-stress, and an enhancement of authentic leadership. Neurophysiological data confirm the effectiveness of PEA sessions for activating self-awareness and ensuring durable transfer of changes into behavior. At the same time, HRV biofeedback and the CSMC model demonstrate measurable business dividends in terms of reduced burnout and turnover. This article will be particularly useful to consultants and practitioners in executive coaching, HR directors, and researchers in organizational psychology.

emotional intelligence, executive coaching, ability approach, mixed model

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