Developing People, Leading Change: Talent Development and Knowledge Leadership as Pillars of Human–AI Collaboration in UAE Government
Year: 2026
Type: book-chapter
Abstract: The integration of artificial intelligence (AI) into government operations is reshaping what public sector employees are expected to know, do, and become. Yet technological capability alone does not determine the success of this transformation: the human dimensions of AI adoption, how organizations develop people for AI-era work, and how leaders navigate the cultural, cognitive, and institutional shifts that AI demands are the most consequential and least understood variables in the equation. This chapter advances a conceptual argument around two interconnected pillars of Human–AI collaboration (HAC) in the public sector: talent development and knowledge leadership for change. Drawing on organizational learning theory, human capital development frameworks, and the emerging literature on AI literacy, the chapter identifies three competency clusters for AI-era government work: AI literacy, data competence, and distinctly human skills. These themes theorize the conditions under which upskilling and reskilling initiatives translate into sustained individual and organizational performance improvement. On the change management and knowledge leadership front, the chapter examines why AI adoption so frequently fails to realize its potential, and what knowledge leaders must do to create the conditions for successful, sustained HAC. Drawing on Kotter’s transformational change model, psychological safety theory, and the KM leadership literature, the chapter identifies three core competency domains for AI-era knowledge leaders: strategic AI literacy, change facilitation capacity, and ethical stewardship. The chapter concludes with an integrative talent–leadership nexus framework and implications for government human resources (HR) leaders, knowledge management (KM) practitioners, and policymakers navigating the human side of AI-driven governance transformation.
Source: IntechOpen eBooks
Authors: Blaique Lama, Hadi El-Farr, Grace Dagher
Language: English
Domain: Social Sciences
Open Access status: hybrid
OpenAlex ID: W7213359299