{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,6]],"date-time":"2026-06-06T04:10:22Z","timestamp":1780719022957,"version":"3.54.1"},"reference-count":62,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2026,2,27]],"date-time":"2026-02-27T00:00:00Z","timestamp":1772150400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100006109","name":"VEGA","doi-asserted-by":"publisher","award":["1\/0764\/26"],"award-info":[{"award-number":["1\/0764\/26"]}],"id":[{"id":"10.13039\/501100006109","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Systems"],"abstract":"<jats:p>Managerial decision-making is a core component of business management and plays a particularly critical role in Sustainable Business Models (SBMs), where it supports long-term competitiveness, adaptability, and positive environmental and social impact. SBMs are inherently complex, dynamic, and data-intensive, requiring advanced analytical capabilities to continuously monitor and optimize sustainability performance across Environmental, Social, and Governance (ESG) dimensions. Artificial Intelligence (AI) introduces new technological opportunities that fundamentally transform managerial decision-making by enabling advanced modeling, simulation, and the analysis of incomplete and heterogeneous data. The purpose of this research is to systematically analyze and synthesize existing AI-supported decision-making approaches used in sustainable business models, with a focus on how these methods transform traditional managerial decision-making frameworks through the integration of Environmental, Social, and Governance (ESG) criteria, and to assess the key benefits, limitations, and implementation conditions of AI-supported decision systems for achieving long-term organizational sustainability. Using a systematic literature review and comparative synthesis of recent theoretical and empirical studies, the research maps key AI-based decision-making approaches applied in sustainable business models and compares their managerial relevance across ESG dimensions. The results provide a structured overview of how different AI techniques contribute to sustainability monitoring, resource optimization, and risk assessment, while also outlining critical organizational, governance, and ethical constraints affecting their practical deployment.<\/jats:p>","DOI":"10.3390\/systems14030245","type":"journal-article","created":{"date-parts":[[2026,2,27]],"date-time":"2026-02-27T15:52:38Z","timestamp":1772207558000},"page":"245","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Artificial Intelligence in Managerial Decision-Making for Sustainable Business Models: A Systematic Literature Review"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0009-0001-1554-7741","authenticated-orcid":false,"given":"Michal","family":"Urbanovi\u010d","sequence":"first","affiliation":[{"name":"Department of Managerial Theories, Faculty of Management Science and Informatics, University of \u017dilina, Univerzitn\u00e1 8215\/1, 010 26 \u017dilina, Slovakia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5200-2441","authenticated-orcid":false,"given":"Martin","family":"Holub\u010d\u00edk","sequence":"additional","affiliation":[{"name":"Department of Managerial Theories, Faculty of Management Science and Informatics, University of \u017dilina, Univerzitn\u00e1 8215\/1, 010 26 \u017dilina, Slovakia"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,2,27]]},"reference":[{"key":"ref_1","unstructured":"Hud\u00e1kov\u00e1, M., and M\u00edka, V.T. 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