How Lenovo Built an AI-Powered Supply ChainSKIP TO CONTENTHarvard Business Review LogoHarvard Business Review LogoOperations and supply chain management|How Lenovo Built an AI-Powered Supply ChainSubscribeSign 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 LogoOperations and supply chain managementHow Lenovo Built an AI-Powered Supply Chain by Robert HandfieldMay 27, 2026Xinhua News Agency/Getty ImagesPostPostShareSavePrintSummary.   Leer en españolLer em portuguêsPostPostShareSavePrintMost companies deploying AI in their supply chains are making a common mistake: They are starting with the technology before understanding their data. They launch pilots, experiment with forecasting tools, and deploy isolated optimization engines—then wonder why almost none of it scales. The problem isn’t the AI. It’s that they skipped the step that makes AI trustworthy. Build intelligence on a broken data foundation and you get broken intelligence, every single time.Robert Handfield is the Bank of America Distinguished Professor of Operations and Supply Chain Management and executive director of the Supply Chain Resource Cooperative at North Carolina State University’s Poole College of Management in Raleigh, North Carolina.PostPostShareSavePrintRead more on Operations and supply chain management or related topic AI and machine learningPartner 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.

Most organizations implementing artificial intelligence within their supply chains frequently encounter scaling issues because they prioritize deploying the technology over a thorough understanding of their underlying data. This approach often involves initiating pilots, experimenting with forecasting instruments, and deploying discrete optimization engines without first establishing a solid foundation. The fundamental problem is not the artificial intelligence itself, but rather the omission of a critical preparatory step: ensuring the intelligence is trustworthy. The core message emphasizes that building intelligence upon a flawed data foundation will inevitably result in flawed intelligence.

Robert Handfield, who is the Bank of America Distinguished Professor of Operations and Supply Chain Management and executive director of the Supply Chain Resource Cooperative at North Carolina State University’s Poole College of Management, highlights this principle. He points out that the failure to properly manage the data precedes the failure of the AI deployment. Consequently, organizations must recognize that the reliability of their AI systems is directly contingent upon the integrity and quality of the data they use. Ignoring the prerequisite step of establishing a robust and sound data base leads to unreliable outcomes, making any subsequent optimization efforts ineffective when scaled across the enterprise.