The advent of Generative AI demands a fundamental paradigm shift in leadership, moving from outdated industrial-era models of transactional oversight to a sophisticated, research-driven approach focused on human-machine collaboration. This new leadership framework requires a "research-based manager" who can distinguish between technological hype and reality, using empirical evidence to make strategic decisions rather than succumbing to market narratives. A key behavioural transformation involves adopting a "Two-Levels Above" mindset, where managers align projects with long-term organisational goals, viewing AI implementation as part of a larger strategic scheme. This evolution addresses the critical paradox of automation, which threatens the experiential talent pipeline by eliminating the junior-level roles necessary for developing senior expertise. To manage this, leaders must foster collaborative frameworks, such as the "Centaur" model (strategic division of labour) and the "Cyborg" model (seamless cognitive integration), to optimise team performance. Structurally, organisations must adapt by creating new governance roles like the Chief AI Officer (CAIO) to drive innovation and the Chief Ethical Officer (CETHO) to ensure responsible and humane technology deployment. Furthermore, risk management in AI governance benefits from consensus-driven models like the Japanese Ringi system, which distributes responsibility and enhances resilience. This comprehensive approach, which also involves quantifying the ROI of departments through risk-based metrics, is essential for navigating the societal "Trust Inflexion Point," bridging the gap between executive enthusiasm and employee concerns. Ultimately, the modern leader must synthesize technical literacy, strategic foresight, and ethical responsibility to build organisational trust and drive sustainable innovation.
Partha Majumdar (2026) studied this question.