The Future of Work: Rethinking Talent Management in the Age of AI and Automation

Abstract

The rapid adoption of artificial intelligence (AI) and automation is fundamentally reshaping work, requiring organizations to rethink traditional, linear talent models. This study synthesizes evidence from 117 peer reviewed, organization level studies (2010–2024) to examine how talent management must evolve to remain competitive in the AI era. Three hypotheses guided the review: (H1) adaptive talent systems enhance workforce agility and competitiveness; (H2) AI driven job redesign increases demand for advanced soft skills and lifelong learning relative to technical specialization; and (H3) institutional support (public reskilling, academic partnerships, and regulation) moderates the workforce risks of AI adoption. A systematic review methodology was employed, using structured database searches and staged inclusion/exclusion criteria to ensure rigor. Of the final sample, 66% were quantitative, 24% qualitative, and 10% mixed methods studies, with research concentrated in high income economies and technology intensive industries. Findings show that organizations integrating AI with dynamic capability models, continuous learning ecosystems, and human AI job design report higher adaptability and retention than those using automation primarily for administrative efficiency. There is growing emphasis on transferable skills such as critical thinking, data literacy, and socio emotional intelligence, and evidence that institutional scaffolding reduces displacement risk and broadens workforce access. Limitations include over representation of large firms in Europe and North America, under representation of SMEs and Global South contexts, and variability in definitions of AI and talent. This review contributes a systems-oriented framework spanning ethical HR analytics, middle manager enablement, and cross sector reskilling coalitions to help organizations design AI savvy, inclusive, and resilient talent strategies.