Intelligent Management and Artificial Intelligence: Trends, Challenges, and Opportunities, Vol.1

Purpose: This paper examines how national governments use policy levers to shape the supply of AI-related competences, distinguishing clearly between skills for AI development (advanced R&D, engineering) and skills for AI adoption (work-force and citizen literacy). It synthesises theory and practice to propose a multi-level framework that links human-capital investment, innovation-ecosystem coordination and inclusion goals to concrete skill outcomes. Need for the study: Global surveys show acute shortages of AI specialists and low baseline AI literacy among workers; yet most national strategies still treat “AI skills” as a single policy target. Little academic work has analysed how different policy mixes map onto these two distinct gaps or what constitutes a balanced, equitable skill strategy. Methodology: The study conducts a structured review of recent research, international guidelines and national AI strategies. It integrates insights through comparative thematic coding and builds a conceptual model—the Nested AI-Skill Ecosystem—that explicates causal links between policy instruments (education reform, R&D subsidies, PPPs, immigration, ethics standards) and the twin skill domains. Findings: The “AI skills gap” is dual: specialist shortages impede frontier innovation, while weak adoption skills limit productivity diffusion. Policy effectiveness hinges on coherent bundles: regulation or funding alone is insufficient without ecosystem coordination. Three archetypal government approaches emerge—development-led, adoption-led and balanced—each traceable to different strategic narratives and institutional capacities. Public–private partnerships and lifelong-learning infrastructures are critical cross-cutting enablers; ethical-AI guidelines indirectly create new training demand. Practical Implications: Policymakers should set dual, measurable targets for developer talent and broad AI literacy; embed AI ethics across all curricula; align R&D grants with mandatory up-/re-skilling components; and establish governance bodies that integrate education, labour and innovation portfolios. The proposed typology offers a diagnostic tool for auditing existing national strategies and designing adaptive, inclusive skill ecosystems for the algorithmic age.