BIS AI Economic Analysis 2025: Global Growth Disparities in the Artificial Intelligence Era

The Bank for International Settlements has released a comprehensive analysis examining how generative artificial intelligence affects economic growth across 56 economies and 16 industries. This groundbreaking research, conducted by leading economists Leonardo Gambacorta, Enisse Kharroubi, Aaron Mehrotra, and Tommaso Oliviero, reveals stark differences in how AI benefits advanced economies versus emerging markets.

Using an empirical methodology inspired by seminal economic research, the study demonstrates that AI’s economic impact is far from uniform across countries and sectors. The findings have profound implications for global economic development, international competitiveness, and policy frameworks designed to harness AI’s potential while addressing growing inequality concerns.

Global AI Growth Disparities: The BIS Framework

The BIS analysis reveals that generative AI’s economic benefits are unevenly distributed across the global economy, with advanced economies positioned to capture disproportionate gains compared to emerging markets. This disparity stems from fundamental differences in economic structures, technological readiness, and sectoral composition between developed and developing nations.

The research methodology employs a robust framework analyzing differential growth effects across multiple dimensions. By examining 56 economies across 16 distinct industries, the study provides unprecedented granularity in understanding how AI affects different economic contexts and development stages.

Advanced economies demonstrate superior capacity to leverage AI capabilities due to several structural advantages: higher concentrations of knowledge-intensive industries, better technological infrastructure, and more developed human capital capable of effectively implementing AI solutions. These advantages create compound benefits that accelerate the widening gap between advanced and emerging economies.

The study’s empirical approach builds on established economic research methodologies while incorporating contemporary data reflecting current AI adoption patterns and capabilities. This comprehensive analysis provides policymakers and business leaders with critical insights for navigating the evolving AI landscape.

Sectoral Exposure Analysis Across 56 Economies

The BIS research demonstrates significant variation in AI exposure across different economic sectors, with profound implications for national competitiveness and growth trajectories. Professional services, including finance and information technology, emerge as the most AI-exposed sectors due to their heavy reliance on cognitive and analytical capabilities that AI can enhance or augment.

Healthcare represents another high-exposure sector where AI applications in diagnostics, treatment planning, and administrative processes create substantial productivity improvements. Advanced manufacturing also benefits significantly from AI integration through improved quality control, predictive maintenance, and supply chain optimization.

Conversely, sectors with substantial physical components, such as construction, agriculture, and traditional manufacturing, show lower immediate exposure to current AI capabilities. However, this differential exposure creates strategic implications for countries depending heavily on these traditional sectors for economic growth and employment.

The sectoral analysis reveals that countries with economic structures weighted toward high-exposure industries gain competitive advantages in the AI era. This structural bias favors advanced economies that typically have larger service sectors and more sophisticated manufacturing capabilities compared to emerging markets focused on commodity production and labor-intensive industries.

Advanced vs Emerging Market Productivity Gaps

The productivity differentials between advanced and emerging economies represent perhaps the most significant finding of the BIS analysis. Advanced economies possess structural characteristics that enable them to capture AI’s productivity benefits more effectively than emerging markets, potentially exacerbating existing global economic inequalities.

Advanced economies benefit from several interconnected advantages that compound AI’s positive effects. Higher education levels, better technological infrastructure, and more sophisticated regulatory frameworks create environments conducive to successful AI implementation. These factors work synergistically to maximize productivity gains from AI investments.

Emerging markets face multiple barriers to realizing AI’s full potential. Limited technological infrastructure, skills gaps in cognitive and analytical capabilities, and regulatory uncertainties constrain the ability to effectively deploy AI solutions. Additionally, economic structures focused on traditional industries provide fewer opportunities for AI-driven productivity improvements.

The research indicates that these productivity gaps may widen in the short term as advanced economies accelerate AI adoption while emerging markets struggle with implementation challenges. This divergence has implications for international trade competitiveness, foreign investment flows, and global economic development patterns.

However, the analysis also suggests potential for emerging economies to leapfrog certain developmental stages through strategic AI investments. Countries that successfully build AI readiness capabilities may achieve accelerated development trajectories, though this requires significant policy coordination and resource allocation.

Cognitive Work and Knowledge-Intensive Activities

The BIS study emphasizes that AI’s impact concentrates primarily on cognitive and knowledge-intensive activities, creating differentiated effects across occupations and industries. This concentration has profound implications for labor markets, skill development, and economic competitiveness in the global economy.

Cognitive work encompasses analytical tasks, creative problem-solving, information processing, and decision-making activities that benefit from AI augmentation. Advanced economies typically have higher concentrations of workers engaged in these activities, particularly in professional services, finance, technology, and research sectors.

Knowledge-intensive industries require specialized expertise, continuous learning, and sophisticated analytical capabilities that AI can enhance rather than replace. This complementary relationship between AI and human expertise creates productivity multipliers that benefit skilled workers and knowledge-based organizations.

The differential distribution of cognitive work across economies explains much of the projected growth disparities. Countries with larger knowledge worker populations and more sophisticated service sectors are positioned to capture greater benefits from AI implementation compared to economies focused on physical production or resource extraction.

This trend raises important questions about skills development, education policy, and workforce preparation in emerging economies. Strategic investments in education, particularly in analytical and technological capabilities, become critical for countries seeking to benefit from AI-driven growth opportunities.

National AI Preparedness and Technology Readiness

AI preparedness emerges as a critical determinant of economic benefits in the BIS analysis, with significant variations across countries reflecting different levels of technological infrastructure, regulatory frameworks, and institutional capabilities. This preparedness gap largely explains differential AI adoption rates and productivity impacts across economies.

Technology readiness encompasses multiple dimensions including digital infrastructure, telecommunications networks, data governance frameworks, and cybersecurity capabilities. Advanced economies typically possess more developed technological foundations that support effective AI implementation and scaling.

Regulatory preparedness involves clear frameworks for AI development, deployment, and governance that provide certainty for businesses while protecting consumer interests. Countries with sophisticated regulatory approaches can attract AI investments and support innovation while managing potential risks and social implications.

Institutional capabilities include government support for research and development, public-private partnerships, and educational institutions capable of producing AI-skilled workforce. These institutional foundations create ecosystems that support AI innovation and adoption across multiple sectors and applications.

The research demonstrates that countries investing in AI preparedness capabilities achieve better outcomes from AI implementation. This finding suggests that strategic policy interventions can influence AI’s economic impact, providing emerging economies with pathways to enhance their competitiveness in the AI era.

Production Structure Dependencies

Production structure emerges as a fundamental determinant of AI’s economic impact, with countries’ industrial composition significantly influencing their ability to benefit from AI technologies. The BIS analysis reveals how historical economic development patterns create path dependencies that affect contemporary AI adoption potential.

Advanced economies typically feature production structures dominated by services, advanced manufacturing, and knowledge-intensive industries that readily benefit from AI integration. These sectors involve complex analytical tasks, information processing, and decision-making activities that AI can enhance through automation and augmentation capabilities.

Emerging economies often rely on production structures emphasizing resource extraction, basic manufacturing, and labor-intensive industries that offer fewer immediate opportunities for AI-driven productivity improvements. While AI applications exist in these sectors, the productivity gains are typically smaller and implementation more challenging.

The research demonstrates that production structure differences explain significant portions of the projected AI impact variations across countries. Nations with service-heavy economies and sophisticated manufacturing capabilities are positioned to capture greater AI benefits compared to commodity-dependent economies.

However, the analysis also indicates opportunities for emerging economies to reshape their production structures through strategic AI investments. Countries that successfully develop AI-enabled industries may achieve structural transformation that enhances their long-term competitiveness and development prospects.

Short-term Economic Impact Assessment

The short-term economic impacts of AI present a complex landscape of opportunities and challenges that vary significantly across different economic contexts and development levels. The BIS research provides detailed projections for how AI affects economic growth, productivity, and competitiveness in the immediate term.

Advanced economies are projected to experience positive short-term growth effects from AI adoption, driven by productivity improvements in high-exposure sectors and successful integration of AI capabilities into existing economic structures. These benefits compound through network effects and spillover impacts across interconnected industries.

Emerging economies face more mixed short-term prospects, with potential benefits offset by implementation challenges, structural constraints, and limited technological readiness. The research suggests that without targeted interventions, these economies may experience widening competitiveness gaps with advanced economies.

The study emphasizes that short-term impacts may not reflect long-term trends, as emerging economies could potentially achieve rapid catch-up growth through strategic AI investments and leapfrogging opportunities. However, realizing this potential requires significant policy coordination and resource allocation.

Employment effects represent a critical dimension of short-term impact, with different implications across skill levels and sectors. While AI may displace certain jobs, it also creates new opportunities, particularly for workers capable of complementing AI capabilities with human expertise and creativity.

Policy Implications for Global Development

The BIS analysis generates critical policy implications for addressing AI-driven growth disparities and ensuring more equitable global development in the AI era. Policymakers must consider both domestic strategies and international coordination mechanisms to manage AI’s transformative economic effects.

For emerging economies, the research suggests focusing on building AI readiness through investments in technological infrastructure, education systems, and regulatory frameworks. Strategic priorities include developing digital infrastructure, enhancing educational curricula to include AI-relevant skills, and creating supportive regulatory environments for AI innovation.

Advanced economies should consider their responsibilities in supporting global AI development through technology transfer, capacity building assistance, and international cooperation initiatives. Addressing global AI disparities serves long-term interests by expanding markets and preventing destabilizing economic inequalities.

International institutions like the BIS, World Bank, and IMF have roles in facilitating AI-related policy coordination, providing technical assistance, and monitoring global AI impact trends. These institutions can support best practice sharing and help countries develop effective AI strategies aligned with their economic contexts.

The research emphasizes that proactive policy interventions are essential for managing AI’s disruptive potential while maximizing its benefits for global economic development. Without strategic coordination, AI could exacerbate existing inequalities and create new sources of international economic instability.

Investment in human capital emerges as a universal priority across all economies, though with different emphases. Advanced economies should focus on maintaining technological leadership and managing workforce transitions, while emerging economies should prioritize building foundational capabilities for AI adoption and implementation.