Machine learning and AI technology-induced skill gaps and opportunities for continuous development of middle-skilled employees

Purpose Based on the sociotechnical systems theory, we examined the human–technology interactions in the context of future works conditioned by machine learning (ML) and artificial intelligence (AI). Skills needed to support career sustainability and the future of the workforce, particularly for the middle-skilled workforce in the contemporary United States America (USA) context, were also studied. Design/methodology/approach We conducted a scenario analysis to demonstrate the potential roles that human resource professionals may perform to fill the skill gaps given their expertise in the shaping and skilling processes. Findings Assessing the success of the integration of AI and ML into the middle-skilled workforce requires a multi-faceted approach that considers performance metrics, cost-effectiveness, job satisfaction, environmental impact and innovation. Employees with AI skills can be more competitive in the workforce and forward to high-skilled positions. Research limitations/implications Empirical research and related studies focusing on evaluations of reskilling and upskilling processes and outcomes would support career sustainability and the future development of middle-skilled workers. Practical implications Through a proactive strategic career development plan with AI integration, middle-skilled workers may enhance their career sustainability and be prepared for future higher-skilled work. Social implications The economic downturn caused by technology-induced unemployment may be addressed by unleashing middle-skilled workforce potentials for future work created by AI and robotics and sustaining economic competitiveness. Originality/value This article offers important implications for human resource development theory-minded researchers and scholarly practitioners.