Leaders Assume Employees Are Excited About AI. They’re Wrong.SKIP TO CONTENTHarvard Business Review LogoHarvard Business Review LogoAI and machine learning|Leaders Assume Employees Are Excited About AI. They’re Wrong.SubscribeSign 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 learningLeaders Assume Employees Are Excited About AI. They’re Wrong. by Deborah Lovich, Stephan Meier and Chenault TaylorNovember 26, 2025Deagreez/Getty ImagesPostPostShareSavePrintSummary.   Leer en españolLer em portuguêsPostPostShareSavePrintAt many organizations, senior leaders have a positive view of their employees’ ability and willingness to use AI. In our recent survey of 1,400 U.S.-based employees, 76% of executives reported that their employees feel enthusiastic about AI adoption in their organization. But the view from the bottom up is less sunny: Just 31% of individual contributors expressed enthusiasm about adopting AI. That means leaders are more than two times off the mark.Deborah Lovich is a managing director and senior partner in the Boston office of Boston Consulting Group. She leads the firm’s Future of Work topic globally and is a fellow of the BCG Henderson Institute.SMStephan Meier is the James P. Gorman Professor of Business Strategy at Columbia Business School and the author of the book “The Employee Advantage: How Putting Workers First Helps Business Thrive.”CTChenault Taylor is a principal at BCG and an ambassador at the BCG Henderson Institute.PostPostShareSavePrintRead more on AI and machine learning or related topics Technology and analytics, Change management and Generative AIPartner 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.

Senior leadership’s perception of employee enthusiasm regarding artificial intelligence adoption within organizations stands in stark contrast to the genuine sentiment among individual contributors. Research, based on a survey of 1,400 U.S.-based employees, reveals a significant disconnect, with 76% of executives asserting that their workforce demonstrated excitement about integrating AI, while only 31% of employees themselves expressed similar feelings. This disparity represents a more than twofold misjudgment, demonstrating a critical gap in understanding the workforce’s actual attitudes and concerns. The article, authored by Deborah Lovich, Stephan Meier, and Chenault Taylor, highlights a crucial element of effective organizational change and strategic implementation.

The research underscores that leadership’s assumption of widespread excitement isn't supported by the experiences and anxieties of those directly impacted by AI integration. This misalignment can have detrimental consequences for the successful adoption of AI. A leader’s perception does not dictate the reality of employee experience. The implications extend beyond mere optimism; it points to potential resistance, decreased productivity, and a stifled innovation environment if leaders proceed under the mistaken assumption that the underlying workforce is ready and willing to embrace these changes.

Specifically, the analysis suggests that the reason for the discrepancy may lie in the fact that many employees perceive AI not as a beneficial tool, but rather as a threat to their roles and job security. The authors do not elaborate on this potential concern, but it is a key factor that contributes to employee hesitation. The article does not offer insights into the factors driving employee anxiety, such as potential job displacement, skill gaps, or the perceived dehumanization of work. The article serves as a wake-up call for organizations to actively solicit and understand employee perspectives before implementing AI initiatives, allowing for tailored approaches that address genuine concerns and foster a collaborative and informed adoption process. Without this deeper understanding, efforts to integrate AI risk being met with resistance and ultimately fail to deliver their intended benefits.