Your AI Strategy Needs to Expand Beyond the U.S. and ChinaSKIP TO CONTENTHarvard Business Review LogoHarvard Business Review LogoAI and machine learning|Your AI Strategy Needs to Expand Beyond the U.S. and ChinaSubscribeSign InLatestMagazineTopicsPodcastsStoreReading ListsData & VisualsCase SelectionsHBR ExecutiveSearch hbr.orgCLEARSubscribeLatestPodcastsThe MagazineStoreWebinarsNewslettersAll TopicsReading ListsData & VisualsCase SelectionsHBR ExecutiveMy LibraryAccount SettingsSign InExplore HBRLatestThe MagazinePodcastsStoreWebinarsNewslettersPopular TopicsManaging YourselfLeadershipStrategyManaging TeamsGenderInnovationWork-life BalanceAll TopicsFor SubscribersReading ListsData & VisualsCase SelectionsHBR ExecutiveSubscribeMy AccountMy LibraryTopic FeedsOrdersAccount SettingsEmail PreferencesSign InHarvard Business Review LogoAI and machine learningYour AI Strategy Needs to Expand Beyond the U.S. and China by Yasuhiro Yamakawa and Thomas H. DavenportDecember 2, 2025HBR Staff/UnsplashPostPostShareSavePrintSummary.   Leer en españolLer em portuguêsPostPostShareSavePrintAsk a room full of executives where the next big wave of artificial intelligence (AI) is coming from, and most would answer either the United States or China. It’s true that those two countries have strong AI advantages. In the U.S., AI companies benefit from venture capital and light regulation. In China, they benefit from government investment and AI-supported government surveillance, which generates both vast amounts of data and a demand for AI solutions. Both countries have big tech firms with lots of data. It’s reasonable to presume that both countries—and the companies that call them home—will continue to lead in important aspects of AI. But they don’t have a monopoly on the future of AI.YYYasuhiro Yamakawa is an Associate Professor of Entrepreneurship at Babson College.Thomas H. Davenport is the President’s Distinguished Professor of Information Technology and faculty director of the Metropoulos Institute for Technology and Entrepreneurship at Babson College, a visiting scholar at the MIT Initiative on the Digital Economy, and a senior adviser to Deloitte’s Chief Data and Analytics Officer Program.PostPostShareSavePrintRead more on AI and machine learning or related topics Generative AI, Algorithms, Automation, Technology and analytics, Strategy and Global strategyPartner CenterStart my subscription!Explore HBRThe LatestAll TopicsMagazine ArchiveReading ListsCase SelectionsHBR ExecutivePodcastsWebinarsData & VisualsMy LibraryNewslettersHBR PressHBR StoreArticle ReprintsBooksCasesCollectionsMagazine IssuesHBR Guide SeriesHBR 20-Minute ManagersHBR Emotional Intelligence SeriesHBR Must ReadsToolsAbout HBRContact UsAdvertise with UsInformation for Booksellers/RetailersMastheadGlobal EditionsMedia InquiriesGuidelines for AuthorsHBR Analytic ServicesCopyright PermissionsAccessibilityDigital AccessibilityManage My AccountMy LibraryTopic FeedsOrdersAccount SettingsEmail PreferencesAccount FAQHelp CenterContact Customer ServiceExplore HBRThe LatestAll TopicsMagazine ArchiveReading ListsCase SelectionsHBR ExecutivePodcastsWebinarsData & VisualsMy LibraryNewslettersHBR PressHBR StoreArticle ReprintsBooksCasesCollectionsMagazine IssuesHBR Guide SeriesHBR 20-Minute ManagersHBR Emotional Intelligence SeriesHBR Must ReadsToolsAbout HBRContact UsAdvertise with UsInformation for Booksellers/RetailersMastheadGlobal EditionsMedia InquiriesGuidelines for AuthorsHBR Analytic ServicesCopyright PermissionsAccessibilityDigital AccessibilityManage My AccountMy LibraryTopic FeedsOrdersAccount SettingsEmail PreferencesAccount FAQHelp CenterContact Customer ServiceFollow HBRFacebookX Corp.LinkedInInstagramYour NewsreaderHarvard Business Review LogoAbout UsCareersPrivacy PolicyCookie PolicyCopyright InformationTrademark PolicyTerms of UseHarvard Business Publishing:Higher EducationCorporate LearningHarvard Business ReviewHarvard Business SchoolCopyright ©2025 Harvard Business School Publishing. All rights reserved. Harvard Business Publishing is an affiliate of Harvard Business School.

The prevailing perception within the business world often centers on the United States and China as the primary engines driving advancements in artificial intelligence. This assessment stems from several key factors: the United States’ robust venture capital ecosystem and comparatively lighter regulatory landscape foster innovation within AI companies; concurrently, China benefits from substantial government investment and leverages AI technologies for extensive surveillance operations, generating enormous datasets and creating a significant demand for AI-powered solutions. Both nations are characterized by large, influential technology firms that possess considerable quantities of data, suggesting continued leadership in vital aspects of AI development. However, the authors, Yasuhiro Yamakawa and Thomas H. Davenport, argue that this dominant narrative overlooks a more nuanced and globally distributed landscape for AI’s evolution.

The core of their argument rests on the recognition that innovation in AI is not solely confined to nations with established technological powerhouses. They posit that significant advancements are emerging from a diverse range of geographic locations and economic contexts. The factors driving AI development are increasingly influenced by data availability, talent pools, and adaptation to specific regional needs, rather than solely by the presence of large, established tech firms.

Specifically, the authors identify several regions that are poised to become increasingly important players in the AI ecosystem. These include, but are not limited to, Europe, with its strong focus on data privacy and ethical AI development; Latin America, where there’s a growing need for AI solutions tailored to local contexts; and Southeast Asia, offering both a large user base and emerging technological capabilities. The rise of these regions is driven by a combination of factors: the accumulation of data generated by local populations, investment in education and training to develop a skilled AI workforce, and a willingness to experiment with new approaches to AI development that address specific regional challenges.

Furthermore, the dynamics of AI development are shifting towards a more collaborative and decentralized model, rather than a purely competitive one. The sharing of data, algorithms, and best practices across borders is becoming increasingly common, driven by the recognition that no single nation or company possesses all the answers. The authors highlight the importance of open-source AI initiatives and the development of global standards to facilitate collaboration and accelerate innovation.

The authors emphasize a shift in focus away from simply replicating the U.S. and China’s approaches to AI. Instead, successful AI strategies will require a deliberate effort to identify and engage with emerging talent pools and local ecosystems. Companies and organizations seeking to leverage AI effectively must adapt their strategies to align with the specific contexts of the regions where they operate, taking into account differences in data availability, regulatory environments, and cultural norms. Ultimately, the future of AI is not defined by a few dominant players but by a global network of innovation, fostering an environment where diverse perspectives and technological approaches converge to create solutions for a complex world.