Unlocking creativity with artificial intelligence: field and experimental evidence on human resource management in the ready-made garments industry

Purpose This study examines how human–AI collaboration influences human resource management (HRM) creativity in Bangladesh’s readymade garments (RMG) industry, a labor-intensive sector facing persistent manpower challenges related to skills mismatch, absenteeism, turnover, worker voice, compliance, women’s advancement and production continuity. The study develops a human-centered “Goldilocks” perspective, arguing that AI contributes most effectively to HRM innovation when collaboration is neither too limited nor excessive. Design/methodology/approach Drawing on human-centered AI theory, creativity research and sector-specific HRM scholarship, the study adopts a three-study design. Study 1 examines AI collaboration in an RMG-focused HRM innovation task among business professionals. Study 2 extends the analysis to a larger working-adult sample with crowdsourced creativity ratings. Study 3 develops a field-oriented HRM model incorporating collaboration intensity, AI self-efficacy, knowledge diversity, problem restructuring, HR innovation climate, age and tenure. Findings Across the studies, moderate human–AI collaboration produces stronger creative HRM outcomes than low or high collaboration. Knowledge diversity emerges as the strongest explanatory mechanism, suggesting that AI enhances HRM creativity mainly by broadening the range of ideas, comparisons and workforce-related solutions available to managers. Excessive AI reliance, however, risks generic recommendations, anchoring effects, reduced contextual judgement and surveillance-oriented HRM practices. Originality/value The study contributes to manpower and HRM research by explaining how responsible AI collaboration can support creative workforce management in a labor-intensive emerging-economy industry. It advances a calibrated model of AI-enabled HRM in which technology strengthens, rather than replaces, human judgement, ethical interpretation and context-sensitive decision-making.