The Transatlantic AI Divide: Understanding Adoption Gaps and Their Economic Implications

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Abstract: This article examines the emerging gap in artificial intelligence adoption between the United States and Europe, drawing on recent survey evidence from workers and firms across major economies. Analysis of over 55,000 worker responses and firm-level data from 32 countries reveals that US AI adoption substantially exceeds European rates, with 43% of US workers using AI compared to 32% in Europe as of early 2026. The adoption gap reflects multiple factors, including demographic composition, firm characteristics, and critically, differences in management practices and organizational support for AI use. Industries with higher AI adoption show faster productivity growth in both regions, though employment effects remain unclear. The findings suggest that without strategic intervention, diverging AI adoption patterns may perpetuate existing productivity differences between the US and Europe, echoing earlier gaps in information and communication technology diffusion.

The economic landscape of advanced economies stands at a potential inflection point. Since the mid-1990s, labor productivity growth in the United States has consistently outpaced that of Europe, with US output per hour increasing 85% between 1995 and 2025 compared to just 29% in Europe (Bick et al., 2026). Research has linked this divergence substantially to differential adoption and utilization of information and communication technologies (ICT), where American firms invested more aggressively and realized greater returns (Ark et al., 2008; Bloom et al., 2012).

Today, artificial intelligence—particularly generative AI technologies that emerged into mainstream consciousness with ChatGPT's November 2022 release—represents a comparable technological inflection point. The transformative potential of AI has sparked considerable debate about its macroeconomic implications (Acemoglu, 2025; Bontadini et al., 2025). Yet the economic impact of any general-purpose technology depends fundamentally on adoption patterns: who uses it, how intensively, and with what organizational support.

Early evidence suggests a familiar pattern may be emerging. Recent analysis combining worker and firm surveys across the US and Europe documents substantial gaps in AI adoption that mirror historical ICT adoption patterns (Bick et al., 2026). This raises an urgent policy question: will AI adoption patterns exacerbate existing transatlantic productivity differences, or does this technological wave offer opportunities for European economies to close historical gaps?

The Current State of AI Adoption

Worker-Level Adoption Patterns

Recent internationally comparable surveys provide the most comprehensive picture to date of AI adoption across advanced economies. Data collected from approximately 55,000 workers across seven countries—the US, Germany, UK, France, Italy, Netherlands, and Sweden—reveal substantial cross-country variation in generative AI use for work purposes (Bick et al., 2026).

As of early 2026, approximately 43% of US workers report using generative AI for their jobs. This exceeds adoption rates in all surveyed European countries, which range from 26% in Italy to 36% in the United Kingdom. The magnitude of the US advantage varies considerably: US adoption exceeds Italian adoption by roughly 68%, while the gap with the UK is approximately 18%.

Beyond simple adoption rates, the intensity of use matters for productivity impacts. American workers who adopt AI use it more intensively than their European counterparts. In the US, workers spend roughly 5.2% of total work hours using AI (including non-users who contribute zero). European countries range from 1.0% to 1.8% of work hours. This intensity gap means that differences in AI's potential productivity impact exceed what simple adoption rates suggest.

The adoption gap has widened over time. Between mid-2025 and early 2026, US worker adoption increased by 3.6 percentage points, the largest increase among surveyed countries. Meanwhile, countries with the lowest initial adoption rates—Germany, France, and Italy—saw increases of only 0.1 to 1.1 percentage points (Bick et al., 2026).

Firm-Level Adoption Patterns

Firm adoption data paint a complementary picture, though direct US-Europe comparisons face measurement challenges. The European Union's ICT Usage and E-Commerce in Enterprises Survey covers 32 countries and asks firms about usage of eight specific AI technologies for various business purposes. In 2025, approximately 20% of EU firms with at least 10 employees reported using at least one AI technology for any business purpose. However, adoption varies dramatically across European countries, from over 35% in Denmark, Finland, and Sweden to less than 10% in several Eastern and Southern European nations.

Comparing US and European firm adoption requires careful attention to question wording. The US Business Trends and Outlook Survey (BTOS) historically focused specifically on AI use "in producing goods or services," a narrower scope than the EU survey's "any business purpose." Under this production-focused definition, approximately 7% of US firms reported AI use in early 2025, nearly double the 4% EU average (Bick et al., 2026). However, this comparison likely understates US firm adoption, as production represents just one of many potential business functions.

The EU data reveal that firms using AI for any purpose substantially exceed those using it specifically for production—roughly five times higher on average. Applying this relationship to US production-focused data suggests that approximately 34% of US firms may use AI for any business purpose, placing the US among the European adoption leaders alongside the Netherlands and approaching Nordic countries' rates.

The Measurement Challenge

A critical finding is that worker and firm adoption rates are highly correlated across countries, with correlation coefficients of 0.75 to 0.99 depending on aggregation level (Bick et al., 2026). This consistency across measurement approaches strengthens confidence in the observed patterns. However, previous research highlighting large worker-firm adoption gaps in the US appears partially attributable to the BTOS's narrow focus on production uses rather than a genuine disconnect between worker and firm adoption (Bonney et al., 2024; McElheran et al., 2024).

Understanding the Adoption Gap

Compositional Factors

Adoption rates vary systematically with worker and firm characteristics. AI use is substantially higher among university-educated workers (25 percentage points higher on average), younger workers (14 percentage points higher for those under 46), and male workers (4 percentage points higher, though this gap has narrowed). Adoption also varies markedly across occupations and industries, with computer and mathematical occupations and information/communication industries showing particularly high rates (Bick et al., 2026).

Larger firms demonstrate substantially higher adoption rates. In the US, establishments with over 250 employees have adoption rates of 53%, compared to 26% in establishments with fewer than 10 employees. European countries show similar gradients, though the slope varies with overall country adoption levels.

Given these patterns, could cross-country adoption differences simply reflect different demographic and industrial structures? An Oaxaca-Blinder decomposition addressing this question finds that differences in age, education, gender, occupation, industry, and firm size statistically account for approximately 55% of the US-Europe adoption gap on average (Bick et al., 2026). Industry, occupation, and firm size contribute more to this explained component (67%) than demographic differences (33%).

However, this analysis leaves roughly 45% of the gap unexplained by compositional factors. Moreover, the explained share varies considerably across countries, from essentially 100% for Sweden to 20% for the UK. This heterogeneity suggests that factors beyond worker and firm composition play important roles in determining national adoption rates.

The Management Practices Connection

Historical research on ICT adoption offers potential insights. Studies of the 1990s and 2000s productivity divergence found that US firms not only invested more heavily in ICT but also realized greater returns from those investments (Bloom et al., 2012). A critical mediating factor was management practices. US firms scored systematically higher on indices measuring structured management approaches, particularly around performance management and incentive structures.

The relationship between management and technology adoption appears to reflect technology's complementarity with organizational practices. Information technologies enable new ways of organizing work, managing information flows, and coordinating activities, but realizing these benefits requires organizational adaptation (Bresnahan et al., 2002; Milgrom & Roberts, 1990). Firms with more structured, performance-oriented management appear better positioned to make these complementary adjustments.

Recent evidence suggests similar dynamics may operate for AI adoption. At the country level, World Management Survey scores correlate strongly with both firm AI adoption rates (ρ = 0.81 for any-purpose adoption, ρ = 0.83 for production adoption) and worker adoption rates across European countries and the US (Bick et al., 2026).

Within-country evidence reinforces this pattern. In the UK, firm AI adoption rises sharply with management quality: only 2% of firms in the bottom management decile adopt AI, compared with 20% in the top decile (Office for National Statistics, 2024). This relationship persists after controlling for firm size, age, industry, and geographic region.

Worker-Level Management Perceptions

To examine management-AI links more directly, recent worker surveys adapted World Management Survey questions to create worker-level assessments of their employers' personnel management practices (Bick et al., 2026). Workers rated the extent to which their employer rewards performance, bases promotions on performance, and addresses poor performance. These responses, combined into a standardized index, show strong positive associations with AI adoption.

A one-standard-deviation increase in a worker's management index associates with a 9.6-percentage-point increase in AI adoption probability, controlling for demographics, occupation, industry, and firm size. This relationship holds both within countries and across countries: the US has the highest average management index, and countries with higher indices tend toward higher adoption.

What specific mechanisms link better management to AI adoption? Three organizational practices show particularly strong associations: whether employers encourage AI use, whether they provide access to AI tools, and whether they offer AI training. Firms with higher management indices are substantially more likely to provide all three forms of support.

Organizational Support and Encouragement

The Critical Role of Employer Encouragement

Among organizational support mechanisms, encouragement emerges as especially powerful. Workers whose employers encourage AI use show dramatically higher adoption rates. Among workers receiving no AI training or tools, 47% adopt AI if their employer encourages use, compared to just 10% without encouragement (Bick et al., 2026).

Employer provision of AI tools also predicts higher adoption, though the effect is smaller. Among workers receiving neither encouragement nor training, 21% adopt if provided tools versus 10% without tools. Interestingly, AI training shows no significant association with adoption once encouragement and tool provision are controlled for. This suggests that training's apparent correlation with adoption may operate through its association with encouragement rather than through direct skill development.

The pattern suggests that adoption barriers may be less about worker awareness or capability and more about organizational signals and support. When employers actively encourage AI experimentation and use, workers appear considerably more willing to adopt, even without formal training programs.

Cross-Country Patterns in Organizational Support

The provision of organizational support varies substantially across countries. In the US, 42% of workers receive both encouragement and tool provision simultaneously, compared to 17% in France and 16% in Italy (Bick et al., 2026). Conversely, 44% of US workers receive none of the three support types (encouragement, tools, training), versus 69-70% in France and Italy.

These differences in organizational support appear to account for a substantial portion of cross-country adoption gaps. An expanded Oaxaca-Blinder decomposition incorporating employer encouragement alongside demographics and firm composition statistically explains nearly all of the US-Europe adoption gap in most countries. Encouragement alone accounts for approximately 80% of the explained gap (Bick et al., 2026).

This finding echoes earlier work on US AI adoption documenting encouragement as the single strongest predictor of worker adoption (Bick et al., 2026). It suggests that organizational factors—how firms choose to promote, support, and integrate AI into work processes—may be more important than worker-level characteristics or technological access in determining adoption patterns.

Economic Consequences of Differential Adoption

Table 1: Transatlantic AI Adoption Rates and Economic Impacts by Country

Country	Worker AI Adoption Rate (%)	Firm AI Adoption Rate (%)	AI Usage Intensity (Work Hours %)	Weekly Time Saved per AI User (Hours)	Employer AI Encouragement Rate (%)	Productivity Growth Impact (Inferred)
United States	43%	34%	5.2%	2.5	42%	Estimated 0.5-1.3 percentage point labor productivity lead over Europe; 2.9-3.7% growth relative to trend.
United Kingdom	36%	Not in source	1.0-1.8%	2.1-2.4	Not in source	Significant positive correlation; high management-AI link found in top decile firms.
Sweden	31-35%	35%	1.0-1.8%	2.1-2.4	Not in source	Adoption gap fully explained by industry/firm composition; approaching US levels.
Netherlands	31-35%	~34%	1.0-1.8%	2.1-2.4	Not in source	High firm adoption leader; strong correlation with US management models.
France	27-31%	Not in source	1.0-1.8%	2.1-2.4	17%	Substantial organizational barriers; 70% of workers receive no AI support.
Germany	27-31%	Not in source	1.0-1.8%	2.1-2.4	Not in source	Lower initial adoption growth compared to US; positive industry-level productivity correlation.
Italy	26%	Not in source	1.0-1.8%	2.1-2.4	16%	Lowest adoption rate among surveyed; high support gap compared to US.

Micro-Level Productivity Evidence

Does AI adoption actually improve worker productivity? A rapidly expanding experimental literature provides increasingly clear affirmative answers across diverse work contexts. Software developers provided access to GitHub Copilot completed coding tasks 55% faster without quality degradation (Peng et al., 2023), a finding replicated in large-scale field experiments showing 26% productivity gains (Cui et al., 2026). Professional writers using ChatGPT completed writing tasks 40% faster while improving quality by 18% (Noy & Zhang, 2023).

Similar patterns emerge across contexts: management consultants completing tasks within GPT-4's capabilities showed 12% more completed tasks with 40% higher quality (Dell'Acqua et al., 2026); law students completed legal research tasks 12-32% faster without quality reduction (Choi et al., 2024); customer support agents resolved 15% more issues per hour (Brynjolfsson et al., 2025); and radiologists using AI doubled scan volumes while maintaining diagnostic accuracy (Goldsmith-Pinkham et al., 2026).

These micro-level gains are substantial and consistent across skill levels and task types, though nuances emerge. Benefits often concentrate among less-experienced workers, potentially reducing skill gaps (Brynjolfsson et al., 2025; Noy & Zhang, 2023). However, AI can reduce performance on tasks outside its competency when users over-rely on its suggestions (Dell'Acqua et al., 2026; Otis et al., 2024).

Worker-Reported Time Savings

Experimental studies offer clean causal identification but limited external validity. To assess broader applicability, recent worker surveys asked AI users to estimate time savings from AI use during the previous week (Bick et al., 2026). Among users, reported savings average 5.8% of work hours, with considerable variation. Approximately 22% report saving less than one hour weekly, 21% save one hour, 40% save 2-3 hours, and 17% save four or more hours.

These self-reported estimates align reasonably with experimental findings, providing some assurance of plausibility. Accounting for non-users who save zero time by construction, aggregate time savings reach 2.3% of all work hours in the US and 1.0-1.8% in European countries. These differences translate to approximately 0.5-1.3 percentage points of additional labor productivity in the US relative to Europe as of early 2026 (Bick et al., 2026).

Countries with higher adoption rates show both higher aggregate time savings and higher savings per user, suggesting that intensive users concentrate in high-adoption countries. Time savings also correlate positively with frequency of use: workers using AI daily report substantially higher weekly savings than those using it occasionally.

Industry-Level Productivity Patterns

The critical macroeconomic question is whether these micro-level effects aggregate to measurable productivity gains in national statistics. Recent evidence suggests they might. Analysis of European industry-level data covering 29 countries finds that industries with higher firm AI adoption rates experienced faster productivity growth from 2019-2024, controlling for country and industry fixed effects (Bick et al., 2026).

A 10-percentage-point increase in industry AI adoption associates with 2-5 percentage points of additional cumulative productivity growth over periods ending in 2024, depending on specification. These associations are statistically significant and robust to excluding productivity outliers and the volatile utilities industry. Importantly, a placebo test using 2015-2019 data—predating mainstream generative AI—finds small and insignificant coefficients, suggesting the post-2019 relationship is not merely correlation with pre-existing trends.

Parallel analysis for the US, where productivity data extend through 2025-Q3, employs a different specification due to data limitations: productivity growth relative to each industry's 2015-2019 trend. This approach yields similar findings. A 10-percentage-point increase in worker AI adoption associates with approximately 3.7 percentage points of additional cumulative productivity growth relative to trend from 2019-2025, and 2.9 percentage points from 2022-2025 (Bick et al., 2026).

The similarity between European and US estimates is striking, despite different measurement approaches (firm vs. worker adoption) and specifications (industry fixed effects vs. deviations from trend). Both suggest that industries experiencing higher AI adoption have indeed seen faster recent productivity growth, with magnitudes consistent with the aggregated micro-level evidence.

Interpreting the Productivity Patterns

Several important caveats accompany these findings. First, the associations are not causal. Industries that adopt AI may differ in other ways that independently affect productivity. However, the insignificant placebo results and the consistency across methods and geographies provide some reassurance.

Second, the time horizon is short. AI adoption accelerated primarily since 2023, giving limited time for productivity effects to manifest fully. Historical evidence from previous general-purpose technologies suggests productivity impacts may take years to fully materialize as firms learn to reorganize work processes (Brynjolfsson et al., 2021).

Third, measured productivity may capture quality improvements imperfectly. If AI primarily improves output quality rather than quantity, conventional productivity measures may understate true gains.

Despite these limitations, the convergent evidence—experimental micro studies showing large individual gains, worker surveys showing substantial time savings, and industry data showing adoption-productivity correlations—collectively suggests AI is already generating measurable productivity benefits at meaningful scale.

Implications for Employment

Alongside productivity impacts, considerable attention focuses on AI's potential employment effects. Will AI-driven productivity growth translate to job displacement, or will it primarily change job content while maintaining employment levels?

Recent empirical evidence remains mixed. Studies examining pre-generative-AI periods show varying results: negative employment effects in US commuting zones from 2000-2020 (Bonfiglioli et al., 2024), no significant effects in exposed industries and occupations from 2010-2018 (Acemoglu et al., 2022), and increasing employment shares in exposed occupations across 16 European countries from 2011-2019, particularly in countries with high education and technology diffusion (Albanesi et al., 2025).

Analysis focusing on generative AI's emergence finds no meaningful employment or earnings impacts in Denmark through 2024 among workers in particularly exposed occupations (Humlum & Vestergaard, 2025a). Industry-level analysis across 29 European countries similarly finds no robust association between AI adoption rates and employment growth from 2019-2025 or 2022-2025 (Bick et al., 2026). Parallel US analysis yields consistent results: essentially no relationship between industry AI adoption and employment share growth relative to trend.

These null findings for employment contrast with the positive productivity associations. Several interpretations are possible. First, AI may be too recently adopted to show employment effects that may emerge over longer horizons. Second, productivity gains may manifest through quality improvements or new capabilities rather than labor reductions. Third, demand expansion from quality improvements or cost reductions may offset direct labor-saving effects.

The absence of clear negative employment effects in these data provides some reassurance to policymakers, though continued monitoring remains essential as adoption deepens and matures.

Building Long-Term AI Capabilities

Beyond Immediate Adoption: Organizational Readiness

The evidence suggests that realizing AI's potential requires more than providing workers with technology access. Organizational practices and culture appear central to successful adoption. This insight has important implications for policy and practice.

For firms, the findings suggest that AI adoption strategies should emphasize not just technology procurement but organizational readiness. This includes establishing clear processes for identifying valuable AI use cases, creating environments where experimentation is encouraged, providing both tools and active encouragement to workers, and potentially adapting management systems to better leverage AI capabilities.

The limited apparent value of formal training programs in isolation is noteworthy. While training may build technical skills, adoption appears more sensitive to organizational signals about whether AI use is desired, supported, and valued. Creating an environment where workers feel encouraged to experiment may matter more than formal skill development.

Management Practices as Economic Infrastructure

The strong association between management quality and AI adoption suggests that management practices function as a form of economic infrastructure—invisible but consequential for technological diffusion and productivity growth. Countries and firms with more structured, performance-oriented management appear better positioned to capitalize on AI opportunities.

This perspective has implications for economic development and competitiveness policy. Traditional technology policy often focuses on research and development, education and skills, or direct technology deployment. The evidence here suggests that management capability deserves comparable attention.

Policy interventions might include disseminating management best practices, supporting management education and training, or incentivizing organizational experimentation and adaptation. Historical evidence suggests such efforts can be effective: the postwar diffusion of American management practices to other countries contributed to productivity catch-up, and more recent initiatives promoting structured management have shown promise in developing economies (Bloom et al., 2013).

Data and Measurement Priorities

The analysis also highlights critical data needs. Understanding AI's economic impact requires ongoing measurement of adoption patterns, productivity effects, and labor market outcomes. Current data infrastructure is nascent, with important gaps and inconsistencies.

Priorities for improved measurement include harmonizing firm-level AI adoption surveys across countries, incorporating AI adoption questions into labor force surveys to enable more detailed analysis, developing consistent frameworks for measuring AI intensity and usage patterns, and creating linked employer-employee datasets to examine how firm AI adoption affects workers.

Beyond surveys, complementary measurement approaches using textual analysis of job postings, resumes, patents, and firm communications offer promise for tracking AI's diffusion and impact at scale (Acemoglu et al., 2022; Babina et al., 2024; Bonfiglioli et al., 2024).

Looking Forward: Convergence or Divergence?

The emerging transatlantic AI gap raises important questions about future economic trajectories. Will differential AI adoption exacerbate existing US-Europe productivity differences, or might European economies close historical gaps?

The optimistic scenario emphasizes that AI technologies are broadly accessible, with powerful capabilities available at low cost. Unlike earlier ICT investments requiring substantial capital expenditures, generative AI offers transformative capabilities through accessible interfaces and modest subscription costs. This accessibility might enable rapid European catch-up if organizational barriers can be addressed.

The pessimistic scenario notes that the adoption gap has widened rather than narrowed in recent periods, with early leaders pulling further ahead (Bick et al., 2026). If adoption follows a technology diffusion curve, countries starting behind may remain behind throughout the adoption cycle. Moreover, the evidence on management practices suggests that lagging adoption may reflect deeper organizational and institutional factors resistant to rapid change.

The reality will likely reflect both dynamics in different sectors and countries. Nordic countries, the Netherlands, and parts of Western Europe already demonstrate adoption rates approaching or matching US levels in some dimensions. These countries may benefit fully from AI's productivity potential. Other European economies face larger gaps and may experience widening productivity differentials unless active interventions accelerate adoption.

Policy Considerations

Removing Adoption Barriers

The analysis suggests several potential policy levers for accelerating AI adoption:

Encouraging organizational support: Since employer encouragement emerges as the strongest adoption predictor, policies might incentivize firms to actively promote AI experimentation. This could include public campaigns highlighting successful AI use cases, certifications or recognition programs for AI-supportive employers, or tax incentives linked to organizational AI integration efforts.

Addressing regulatory uncertainty: Concerns about privacy, ethics, and compliance may inhibit some firms from encouraging AI use. Clear regulatory frameworks that provide certainty while protecting important values could remove adoption barriers.

Supporting management capability development: Given management practices' strong association with adoption, initiatives to diffuse structured management approaches—particularly around performance management and innovation support—may indirectly accelerate AI diffusion.

Facilitating experimentation: Policies that reduce the cost and risk of organizational experimentation with new technologies can accelerate learning and adoption. This might include innovation vouchers, support for pilot programs, or platforms for sharing lessons across organizations.

Balancing Innovation and Protection

European policymakers face tensions between accelerating AI adoption and protecting workers and citizens from potential harms. The EU AI Act represents one approach, establishing risk-based regulations to ensure responsible AI development and use. However, overly restrictive approaches risk widening adoption gaps.

The evidence here suggests that thoughtfully designed policies need not face a stark tradeoff. Since adoption appears more sensitive to organizational support and encouragement than to technology access, policies that establish clear guardrails while encouraging experimentation within those boundaries might effectively balance competing objectives.

Moreover, the lack of clear negative employment effects to date provides some room for policies that encourage adoption while monitoring for adverse impacts. This might involve creating robust real-time labor market monitoring systems alongside policies that actively promote beneficial AI adoption.

Investment in Complementary Capabilities

AI's economic impact will depend not just on adoption rates but on complementary capabilities:

Digital infrastructure: Reliable, high-speed connectivity enables AI tool access, particularly cloud-based services. Continued investment in digital infrastructure remains essential.

Skills and adaptability: While formal AI training shows limited association with adoption in current data, this may reflect training program quality rather than skill irrelevance. Investing in high-quality programs that build both technical AI skills and broader capabilities for working with AI remains important.

Data infrastructure: AI's value depends on data access and quality. Policies promoting data availability while protecting privacy—such as data trusts or interoperability requirements—can enhance AI's potential.

Research and development: While this analysis focuses on adoption of existing technologies, continued innovation in AI capabilities will determine long-term possibilities. Supporting fundamental AI research remains a sound investment, even as attention shifts toward adoption and diffusion.

Conclusion

The evidence reviewed here documents substantial gaps in AI adoption between the United States and Europe, with the US holding leads of approximately 11 percentage points in worker adoption and similarly elevated firm adoption rates. These gaps are consequential: micro-level evidence shows AI generating substantial productivity improvements for individual workers, and industry-level data suggest these gains are aggregating to measurable productivity growth in national statistics.

The adoption gap reflects multiple factors. Demographic and industrial composition explain roughly half of the US-Europe difference, with the remainder associated with organizational practices—particularly management quality and employer encouragement of AI use. This suggests that closing the gap will require not just ensuring technology access but fostering organizational environments conducive to experimentation and change.

The policy implications are nuanced. While the findings might seem to argue for aggressive adoption promotion, they equally suggest that sustainable adoption depends on organizational readiness rather than technology push. Effective policies will likely emphasize removing barriers to experimentation, building management capabilities, providing regulatory clarity, and creating environments where firms and workers feel encouraged to explore AI's potential.

The historical parallel with ICT diffusion is instructive but not determinative. AI's greater accessibility relative to earlier ICT investments offers grounds for optimism about European catch-up potential. However, the widening rather than narrowing gap in recent periods, and the apparent importance of organizational factors that change slowly, counsels against complacency.

Perhaps most importantly, the early evidence of productivity gains without clear employment disruption suggests a potential window where AI can deliver economic benefits while avoiding the worst-case scenarios of mass displacement. Whether this pattern persists as adoption deepens remains uncertain, but it provides at least temporary space for policies that promote beneficial AI adoption while carefully monitoring and addressing any emerging adverse effects.

The trajectory of AI adoption in coming years will shape economic prospects across advanced economies. Understanding the sources of current adoption gaps and the mechanisms linking adoption to economic outcomes provides essential input for policies aimed at ensuring AI contributes to broad-based prosperity rather than widening existing disparities.

Research Infographic

The AI Management Gap

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Jonathan H. Westover, PhD is Chief Research Officer (Nexus Institute for Work and AI); Associate Dean and Director of HR Academic Programs (WGU); Professor, Organizational Leadership (UVU); OD/HR/Leadership Consultant (Human Capital Innovations). Read Jonathan Westover's executive profile here.

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Suggested Citation: Westover, J. H. (2026). The Transatlantic AI Divide: Understanding Adoption Gaps and Their Economic Implications. Human Capital Leadership Review, 37(3). doi.org/10.70175/hclreview.2020.37.3.4