{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,12]],"date-time":"2026-02-12T17:08:30Z","timestamp":1770916110960,"version":"3.50.1"},"reference-count":26,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2026,2,12]],"date-time":"2026-02-12T00:00:00Z","timestamp":1770854400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Logistics"],"abstract":"<jats:p>Background: Artificial Intelligence (AI) is increasingly adopted across logistics and service operations, yet limited research explains how it supports back-end event logistics or what factors enable or hinder its implementation. This study investigates how AI can be applied across event logistics processes and identifies the key barriers and critical success factors shaping its adoption. Methods: A sequential exploratory qualitative design was employed. First, semi-structured interviews with experienced event professionals generated context-specific insights. These findings informed a two-round Delphi study with 10 experts, where items were prioritised and consensus assessed using Kendall\u2019s coefficient of concordance (W) and chi-square tests. Results: The results indicate that AI delivers the greatest value in pre-event planning activities, particularly scheduling and supplier coordination. Resistance to change and the lack of industry-specific AI tools emerged as the main adoption barriers, while technological infrastructure, system integration, and change management were identified as critical success factors. Conclusions: The study provides practical guidance for event organisers and technology providers by highlighting where AI investments are most likely to generate operational benefits and how organisational readiness can be strengthened. It also underscores the need for improved sustainability-focused tools and better data practices.<\/jats:p>","DOI":"10.3390\/logistics10020048","type":"journal-article","created":{"date-parts":[[2026,2,12]],"date-time":"2026-02-12T15:28:11Z","timestamp":1770910091000},"page":"48","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Artificial Intelligence Adoption in Event Logistics: Barriers, Critical Success Factors, and Expert Consensus from a Delphi Study"],"prefix":"10.3390","volume":"10","author":[{"given":"Sofia","family":"Matias","sequence":"first","affiliation":[{"name":"Departamento de Marketing, Opera\u00e7\u00f5es e Gest\u00e3o Geral, ISCTE\u2014Instituto Universit\u00e1rio de Lisboa, 1649-026 Lisbon, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4074-1586","authenticated-orcid":false,"given":"Alvaro Lopes","family":"Dias","sequence":"additional","affiliation":[{"name":"Business Research Unit, ISCTE\u2014Instituto Universit\u00e1rio de Lisboa, Av. das For\u00e7as Armadas, 1649-026 Lisbon, Portugal"},{"name":"C\u00e9sar Ritz Colleges, Brig Campus, Englisch-Gruss-Strasse 43, 3902 Brig-Glis, Switzerland"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4920-0498","authenticated-orcid":false,"given":"Leandro","family":"Pereira","sequence":"additional","affiliation":[{"name":"Business Research Unit, ISCTE\u2014Instituto Universit\u00e1rio de Lisboa, Av. das For\u00e7as Armadas, 1649-026 Lisbon, Portugal"}]}],"member":"1968","published-online":{"date-parts":[[2026,2,12]]},"reference":[{"key":"ref_1","unstructured":"Precedence Research (2025, June 20). 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