Institutional Readiness and Transformational Barriers: Artificial Intelligence Adoption Frameworks for Organizational Delivery Capability

Institutional Readiness and Transformational Barriers: Artificial Intelligence Adoption Frameworks for Organizational Delivery Capability | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Institutional Readiness and Transformational Barriers: Artificial Intelligence Adoption Frameworks for Organizational Delivery Capability Vipul Razdan This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8696847/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract The paper has demonstrated substantially slower adoption of artificial intelligence technologies compared to peer sectors such as healthcare, retail, and manufacturing operations, raising critical questions about organizational readiness, technological barriers, and structural impediments to AI-driven project delivery transformation. This paper investigates the gap between AI’s demonstrated capabilities and the project management community’s apparent reluctance to systematically integrate intelligent automation into project planning, execution, monitoring, and control processes. Through analysis of current AI vendor strategies (foundational models vs. autonomous bespoke systems), the research identifies critical decision factors that determine organizational eligibility for AI adoption within project contexts: capital investment requirements, data architecture maturity, dataset quality standards, scalability potential across diverse project typologies, and risk tolerance thresholds. The paper categorizes inherent implementation challenges into technical, financial, and organizational domains, including concerns about model adequacy across heterogeneous project types, cost-prohibitiveness for small-to-medium enterprises, ethical governance of autonomous decision-making in project contexts, and the potential marginalization of resource-constrained organizations lacking sufficient data infrastructure. Additionally, the work examines transitional pathways that position domain-specific AI foundation models as intermediate stepping stones enabling progressive organizational adaptation and capability development, with implications for the future role and skillset evolution of project management professionals operating in algorithmic decision environments. The analysis concludes that successful AI integration in project management requires concurrent attention to technological investment, organizational maturity assessment, ethical frameworks, and workforce adaptation strategies, rather than treating AI adoption as a purely technical implementation challenge Artificial Intelligence Project Management Organizational Readiness Technology Adoption Foundational Models Ethical AI Digital Transformation Capability Maturity Implementation Barriers Full Text Additional Declarations The authors declare no competing interests. Associated Publications Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8696847","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[{"doi":"10.1109/ICIPTM69057.2026.11465386","date":"","title":"","authors":"","journal":"","logo":""}],"authors":[{"id":580295096,"identity":"fc9557c9-ba31-4091-8370-8735858745c0","order_by":0,"name":"Vipul Razdan","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAvElEQVRIie3PoQrDMBCA4YRCVKC2fYuoqNI+yMyFQKf2BhWZmtmrzE4HClOjsYWa9g3mtpmxa2G6NzdYfnXiPo5jLBb7ydL7w7wKHPjeU43KRlHPxNFJPop2mWhEDR4UyFCeDi1eaYrNOunAA2SDPV8Nkku9c2skP+ILoAarPRLuWgpJuAPorA4TkaRSJAy8L3VPvZJKKZhxFnSPV4Dyi0DCn66sdNhO460p1skns2wCdX2u+mY5FovF/qw30WdE1oK3PdEAAAAASUVORK5CYII=","orcid":"","institution":"","correspondingAuthor":true,"prefix":"","firstName":"Vipul","middleName":"","lastName":"Razdan","suffix":""}],"badges":[],"createdAt":"2026-01-26 05:48:51","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":true,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":true},"doi":"10.21203/rs.3.rs-8696847/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8696847/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":107705201,"identity":"8d55bac8-60ab-4db3-bfa7-3b5bbfd3d0c0","added_by":"auto","created_at":"2026-04-24 09:09:29","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":118086,"visible":true,"origin":"","legend":"","description":"","filename":"InstitutionalReadinessandTransformationalBarriersArtificialIntelligenceAdoptionFrameworksforOrganizationalDeliveryCapability.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8696847/v1_covered_1127307d-264d-478e-a312-bb6ea477fe50.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003eInstitutional Readiness and Transformational Barriers: Artificial Intelligence Adoption Frameworks for Organizational Delivery Capability\u003c/p\u003e","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Artificial Intelligence, Project Management, Organizational Readiness, Technology Adoption, Foundational Models, Ethical AI, Digital Transformation, Capability Maturity, Implementation Barriers","lastPublishedDoi":"10.21203/rs.3.rs-8696847/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8696847/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe paper has demonstrated substantially slower adoption of artificial intelligence technologies compared to peer sectors such as healthcare, retail, and manufacturing operations, raising critical questions about organizational readiness, technological barriers, and structural impediments to AI-driven project delivery transformation. 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