AI-Powered Talent Chain Management with Multi-Agent Systems for Industry and Innovation Growth

AI-Powered Talent Chain Management with Multi-Agent Systems for Industry and Innovation Growth | 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 Article AI-Powered Talent Chain Management with Multi-Agent Systems for Industry and Innovation Growth Bin Wang, Juan Zhang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9264887/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 12 You are reading this latest preprint version Abstract This study introduces an innovative methodological framework for AI-driven talent chain management, addressing key challenges in workforce optimization, collaboration dynamics, and innovation assessment within complex and uncertain environments. Traditional talent management methods often lack the adaptability needed to handle dynamic demands and stochastic task variations. To overcome these limitations, the framework incorporates the Adaptive Talent Dynamics Planner, composed of three modules: the Constraint-driven Workforce Optimizer, the Agent-based Collaboration Forecaster, and the Uncertainty-aware Innovation Evaluator. These components optimize workforce allocation, predict collaboration patterns, and evaluate innovation potential under uncertainty. The framework further integrates policy-grounded reasoning and uncertainty-aware refinement to ensure alignment with organizational objectives and robustness to fluctuations in talent and task parameters. By formalizing the problem mathematically and leveraging a multi-agent system architecture, this research provides an adaptive solution for talent chain management. Experimental results demonstrate improvements in allocation efficiency, collaboration prediction accuracy, and innovation assessment, underscoring its potential to support sustainable growth and organizational innovation. Physical sciences/Engineering Physical sciences/Mathematics and computing AI-driven Talent Management Multi-agent Systems Workforce Optimization Collaboration Dynamics Forecasting Uncertainty-aware Innovation Evaluation Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Revision Version 1 posted Editorial decision: Revision requested 20 Apr, 2026 Reviews received at journal 17 Apr, 2026 Reviews received at journal 14 Apr, 2026 Reviews received at journal 13 Apr, 2026 Reviewers agreed at journal 13 Apr, 2026 Reviewers agreed at journal 13 Apr, 2026 Reviewers agreed at journal 13 Apr, 2026 Reviewers invited by journal 13 Apr, 2026 Editor assigned by journal 13 Apr, 2026 Editor invited by journal 13 Apr, 2026 Submission checks completed at journal 07 Apr, 2026 First submitted to journal 07 Apr, 2026 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-9264887","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":626024788,"identity":"ce16603d-4330-43d7-8617-218a47817816","order_by":0,"name":"Bin Wang","email":"","orcid":"","institution":"Xi’an International University","correspondingAuthor":false,"prefix":"","firstName":"Bin","middleName":"","lastName":"Wang","suffix":""},{"id":626024789,"identity":"6bec2755-f71d-4f4c-8ac1-4deefccc5149","order_by":1,"name":"Juan Zhang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABGklEQVRIie3Pv0vDQBTA8RcC6XI6XwjkbzgJFIVA/5UcgXNpQbd0CS2BuATnFkH/EYcXDswScM3YWnCOm0IRL8ngYBJxc7jvcD/gfYYHoNP9w5jsbp4CGAiRT2bFSv2vAOgwMZqRa0UAoRQulKhebIRgR5bNgUYqPaiCX0hxUuzeHuPl7UQi1pbg6+3hyamZ79orc/9S/SRTecrXm1e5SIkI8g3xeeIIQZEJzwHL8+Z9hJwlBHGRUsIkoYKnznyqiOT3QCxniBwxvmzJUU1mdjlOZg0BNMOWQCA9SklH7gbIhSLbDGXY7pKhcBkR4XmpdrGT/l3Yc8nqd4z5w43Mdx+fPmETmVdR5Lu0SPaHHjKW+bdxnU6n0333BfQpcdAGMLIZAAAAAElFTkSuQmCC","orcid":"","institution":"Xi’an International University","correspondingAuthor":true,"prefix":"","firstName":"Juan","middleName":"","lastName":"Zhang","suffix":""}],"badges":[],"createdAt":"2026-03-30 09:26:11","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9264887/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9264887/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":107399036,"identity":"5d09e3ec-017e-4dcf-8d08-e926200ab41b","added_by":"auto","created_at":"2026-04-21 07:12:42","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":623377,"visible":true,"origin":"","legend":"","description":"","filename":"ScientificReports.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9264887/v1_covered_c2d096eb-f370-4c9c-bd2c-b561196a88a5.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"AI-Powered Talent Chain Management with Multi-Agent Systems for Industry and Innovation Growth","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"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":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"AI-driven Talent Management, Multi-agent Systems, Workforce Optimization, Collaboration Dynamics Forecasting, Uncertainty-aware Innovation Evaluation","lastPublishedDoi":"10.21203/rs.3.rs-9264887/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9264887/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"This study introduces an innovative methodological framework for AI-driven talent chain management, addressing key challenges in workforce optimization, collaboration dynamics, and innovation assessment within complex and uncertain environments. 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