Manage Your AI Investments Like a PortfolioSKIP TO CONTENTHarvard Business Review LogoHarvard Business Review LogoAI and machine learning|Manage Your AI Investments Like a PortfolioSubscribeSign InLatestMagazineTopicsPodcastsStoreReading ListsData & VisualsCase SelectionsHBR ExecutiveSearch hbr.orgSubscribeLatestPodcastsThe 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 learningManage Your AI Investments Like a Portfolio by Faisal Hoque, Erik Nelson, Tom Davenport and Paul ScadeJanuary 21, 2026Hiroshi Watanabe/Getty ImagesPostPostShareSaveBuy CopiesPrintSummary.   Leer en españolLer em portuguêsPostPostShareSaveBuy CopiesPrintBusiness leaders now face intense pressure to transform their organizations with AI, even though the technology, public attitudes, and the competitive landscape are all still in flux. The result is often too many pilots with too little coordinated oversight. Without a way to systematically decide where to start, how fast to move, and when to stop, AI efforts quickly become a drain on attention and resources rather than a source of advantage. A familiar pattern recurs across many companies: isolated, piecemeal deployments, limited buy-in by senior executives, and weak linkage to strategic goals.Faisal Hoque  is the founder of SHADOKA, NextChapter, and other companies. He is a three-time winner of the Deloitte Technology Fast 50 and Deloitte Technology Fast 500™ awards, and a three-time Wall Street Journal bestselling author. His latest book, TRANSCEND: Unlocking Humanity in the Age of AI, explores the intersection of philosophy, business, technology, and humanity in the AI era.Erik Nelson is a senior vice president at CACI International, responsible for strategic vision and growth in the company’s Enterprise IT division.Tom Davenport Thomas H. Davenport is the President’s Distinguished Professor of IT and Management at Babson College, a research fellow at the MIT Center for Digital Business, co-founder of the International Institute for Analytics, and a Senior Advisor to Deloitte Analytics. He is author of the new book Big Data at Work and the best-selling Competing on Analytics.Paul Scade is a partner at Shadoka and NextChapter and an Honorary Fellow at the University of Liverpool. Hoque, Davenport, Nelson, and Scade are co-authors of the forthcoming book Reimagining Government: Achieving the Promise of AI (Post Hill Press, January 2026).PostPostShareSaveBuy CopiesPrintRead more on AI and machine learning or related topics Analytics and data science, Data management, Finance and investing and Investment managementPartner 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 PreferencesHelp 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 PreferencesHelp 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 ©2026 Harvard Business School Publishing. All rights reserved. Harvard Business Publishing is an affiliate of Harvard Business School.

AI and machine learning investments are currently facing significant challenges stemming from a period of rapid, often uncoordinated, deployment across organizations. The intense pressure on business leaders to transform their operations with AI, alongside the volatile landscape of technological development, public perception, and competitive dynamics, has frequently resulted in a proliferation of isolated AI pilot projects lacking centralized oversight. This pattern demonstrates a recurring tendency: fragmented, project-based implementations, limited engagement from high-level executives, and a deficient connection to overarching strategic objectives. The authors, Hoque, Nelson, Davenport, and Scade, collectively highlight this issue as a primary impediment to realizing the true potential of AI within business.

The core problem centers on a lack of structured strategic approaches to AI adoption. Instead of a cohesive, forward-thinking strategy, companies are frequently reacting to emerging technologies or individual departmental requests, leading to a scattering of initiatives that don’t contribute to a unified organizational advantage. This approach tends to consume valuable resources – time, personnel, and capital – without delivering proportional returns. The authors emphasize the need to move beyond this reactive, siloed model and establish a more deliberate and disciplined framework for managing AI investments.

Specifically, the article advocates for a portfolio approach to AI investments, analogous to managing a financial portfolio. This suggests allocating resources across different projects and initiatives based on a systematic evaluation of potential value, risk, and strategic alignment. The proposed framework emphasizes key considerations such as assessing the maturity of the technology, understanding the level of organizational readiness, and determining the anticipated return on investment. Moreover, the authors stress the significance of securing buy-in and active participation from senior leadership, who must champion the strategic importance of AI and provide the necessary support and resources.

The portfolio approach necessitates prioritizing investments based on clear, quantifiable metrics. This moves beyond simply experimenting with new technologies and focuses on projects that demonstrably contribute to key business goals—such as enhanced operational efficiency, improved customer experience, or development of novel products and services. The article suggests a staged implementation, starting with smaller-scale projects to gain experience and build momentum, before scaling up successful initiatives. It underscores the importance of establishing robust governance structures to oversee AI investments, monitor progress, and ensure accountability.

Furthermore, the authors implicitly suggest that a significant element missing from many AI initiatives is a deep understanding of the data underpinning the technology. The success of any AI implementation hinges on the quality, availability, and relevance of the data used to train and deploy the algorithms—highlighting the need for a strong data management strategy as a core component of the overall investment approach. The article doesn’t offer a detailed methodology, but it paints a compelling argument for treating AI investments with the same careful consideration and strategic rigor that would be applied to any other significant business undertaking.