Bridging Minds and Machines: Emotional Intelligence as Intellectual Capital for Human-AI Collaboration

As AI evolves from a tool to a key collaborator and co-creator across industries, its effectiveness is influenced by humans' abilities to guide, interpret, and refine AI-generated outputs. This study examines how self-awareness, emotional regulation, empathy, adaptability, emotional expression, and interpersonal skills- the core emotional intelligence competencies- moderate human-AI collaboration for creative problem-solving. The research positions emotional intelligence within the intellectual capital framework as a dimension of human capital that enhances both individual performance and organizational capital. Through empirical research with marketing professionals, the study ranks emotional intelligence competencies based on how they improve AI-generated creative outputs. The findings reveal that emotional expression is the most effective competency for enhancing creative outcomes, while adaptability shows negligible influence. This evidence suggests that professionals who can communicate emotional context effectively generate more innovative AI responses than those who focus solely on technical parameters. The study further examines professionals' specific interaction strategies, comparing emotionally informed prompt engineering, iterative refinement, and role-based interaction approaches. The results indicate that prompt clarity consistently ranks as the most effective strategy for enhancing AI responses, while emotional context remains significantly underutilized despite empirical evidence supporting its benefits. This gap between research findings and current professional practice highlights an important opportunity for targeted emotional intelligence development within organizations adopting AI technologies. Indeed, while the emotional context of human-AI interaction remains underutilized despite its proven benefits, organizations that invest in emotional intelligence competencies can achieve improved technological integration, effectively connecting human creativity with AI capabilities. The research presents a structured framework for developing these competencies, with a particular emphasis on emotional expression and contextual prompting techniques. These findings carry significant implications for businesses creating AI implementation strategies, educators designing curricula for future professionals, and policymakers setting guidelines for responsible AI adoption, all while aiming to enhance AI-driven creativity without sacrificing human-centered innovation principles.