Charting the AI Landscape: a Global Comparative Analysis of National Strategies

This study explores how governments around the world are steering the adoption and development of AI through their national strategies. Between 2017 and mid-2025, more than 80 countries (Author’s data, coupled with information from Our World in Data (2024); OECD AI Dashboard (2025)) released official AI strategies, signalling that AI has become a central pillar of public policy and economic growth. Drawing on an updated dataset of strategies issued between Jan 2018 and June 2025, this analysis maps the emergent pathways, commonalities and divergences in strategic approaches. Using qualitative analysis to systematically analyse 25 countries across different income levels and geographic regions, it becomes clear that all governments are grappling with the cross‑cutting dimensions of AI—ranging from workforce reskilling and data‑governance frameworks to the environmental ramifications of large‑scale model training – but have crafted different sets of solutions to address the challenges. This newly assembled, cross‑national dataset closes a gap in comparative evidence, supplying a solid empirical foundation for debates on governance models, AI adoption trajectories, and national priorities across socioeconomic contexts. The qualitative thematic analysis of national strategies from 25 countries was structured around 8 directional pillars and implementation vectors, as follows: (1) Public‑sector adoption objectives; (2) Ethical concerns and responsible AI development/adoption; (3) Regulatory orientation; (4) Environmental sustainability; (5) International cooperation approaches; (6) Public investment and funding mechanisms; (7) Business environment strategies; (8) Workforce transformation and skills‑development measures. Analysing each national strategy against these eight dimensions resulted in a comparative matrix that enabled the identification of common patterns, gaps, and divergent emphases across jurisdictions. The analysis shows three priority clusters that dominate contemporary strategies. First, the development of research capacity and computational infrastructure receives sustained emphasis, with public‑private partnerships serving as the principal conduit for investment. Second, demand‑side incentives—subsidies, procurement mandates, tax credits for SMEs and start‑ups—are unevenly deployed; some countries embed extensive market‑stimulus measures, while others merely acknowledge them. Third, standards and hardware localisation strategies differ sharply: a minority of states articulate explicit leadership ambitions, whereas most defer to existing international standards. Environmental sustainability receives inconsistent attention; a few strategies dedicate sections to “green AI,” whereas the large majority focuses on deployment without accounting for ecological impact. Overall, these findings reaffirm that a pro‑innovation paradigm—marked by state‑led facilitation of dynamic ecosystems and the integration of AI into the public sector—continues to dominate national strategies, backed by hybrid governance configurations.