Artificial Intelligence Adoption in Facility Management Procurement: A Systematic Literature Review and Research Agenda

Facility management (FM) procurement faces increasing complexity due to globalised supply chains, regulatory pressures, and demands for cost optimisation and transparency. Although artificial intelligence (AI) offers transformative potential for procurement efficiency, its adoption in FM contexts remains underexplored, as research on AI in procurement and on digital technologies in FM has developed largely in parallel. This systematic literature review examines AI adoption in FM procurement by addressing four research questions concerning readiness factors, adoption strategies, framework components and theoretical foundations, and the research gaps that justify the development of an expert-consensus AI adoption framework. Following PRISMA 2020 guidelines, 12 databases were searched across six evidence streams for studies published from 2000 to 2026. From 3,245 identified records, 994 unique records remained after deduplication, 500 underwent full-text assessment, and 180 studies met the inclusion criteria. Quality appraisal employed CASP, JBI, MMAT, ROBINS-I, and conceptual quality checklists, and thematic synthesis was used to integrate the evidence. The review identified 33 readiness factors across five dimensions (organisational, technological, human resource, data, and external readiness), with top management support, data quality, and IT infrastructure emerging as the most critical. Twenty-four adoption strategies were categorised into five groups, with phased implementation and executive sponsorship most frequently recommended. The Technology-Organisation-Environment (TOE) framework emerged as the most suitable theoretical lens. Only 18 of 180 studies (10%) specifically addressed AI in FM procurement, revealing a significant domain-specific research gap, while publication output has grown exponentially since 2018. The findings reveal a fragmented research landscape and provide a foundation for developing context-specific AI adoption frameworks for FM procurement. Seven critical research gaps are identified, including the need for domain-specific frameworks, empirical validation through expert consensus methods, and studies in Southeast Asian contexts, offering actionable insights for FM organisations, procurement professionals, technology vendors, and policymakers.

artificial intelligence, facility management, procurement, technology adoption

1. Cavka, H. B., Staub-French, S., & Poirier, E. A. (2017). Developing owner information requirements for BIM-enabled project delivery and asset management. Automation in Construction, 83, 169–183. https://doi.org/10.1016/j.autcon.2017.08.006 [Google Scholar] [Crossref]

2. Pishdad-Bozorgi, P., Gao, X., Eastman, C., & Self, A. P. (2018). Planning and developing facility management-enabled building information model (FM-enabled BIM). Automation in Construction, 87, 22–38. https://doi.org/10.1016/j.autcon.2017.12.004 [Google Scholar] [Crossref]

3. Atkin, B., & Brooks, A. (2021). Total facility management (5th ed.). Wiley-Blackwell. [Google Scholar] [Crossref]

4. Pitt, M., & Tucker, M. (2008). Performance measurement in facilities management: Driving innovation? Property Management, 26(4), 241–254. https://doi.org/10.1108/02637470810900747 [Google Scholar] [Crossref]

5. Bienhaus, F., & Haddud, A. (2018). Procurement 4.0: Factors influencing the digitisation of procurement and supply chains. Business Process Management Journal, 24(4), 965–984. https://doi.org/10.1108/BPMJ-06-2017-0139 [Google Scholar] [Crossref]

6. Toorajipour, R., Sohrabpour, V., Nazarpour, A., Oghazi, P., & Fischl, M. (2021). Artificial intelligence in supply chain management: A systematic literature review. Journal of Business Research, 122, 502–517. https://doi.org/10.1016/j.jbusres.2020.09.009 [Google Scholar] [Crossref]

7. Bag, S., Gupta, S., & Kumar, S. (2021). Industry 4.0 adoption and 10R advance manufacturing capabilities for sustainable development. International Journal of Production Economics, 231, 107844. https://doi.org/10.1016/j.ijpe.2020.107844 [Google Scholar] [Crossref]

8. Zheng, J., Lu, W., Wu, L., & Jiang, H. (2021). Artificial intelligence in construction procurement: Applications and implications. Automation in Construction, 125, 103630. https://doi.org/10.1016/j.autcon.2021.103630 [Google Scholar] [Crossref]

9. Pärn, E. A., Edwards, D. J., & Sing, M. C. P. (2017). The building information modelling trajectory in facilities management: A review. Automation in Construction, 75, 45–55. https://doi.org/10.1016/j.autcon.2016.12.003 [Google Scholar] [Crossref]

10. Tornatzky, L. G., & Fleischer, M. (1990). The processes of technological innovation. Lexington Books. [Google Scholar] [Crossref]

11. Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319–340. https://doi.org/10.2307/249008 [Google Scholar] [Crossref]

12. Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27(3), 425–478. https://doi.org/10.2307/30036540 [Google Scholar] [Crossref]

13. Rogers, E. M. (2003). Diffusion of innovations (5th ed.). Free Press. [Google Scholar] [Crossref]

14. Page, M. J., McKenzie, J. E., Bossuyt, P. M., Boutron, I., Hoffmann, T. C., Mulrow, C. D., Shamseer, L., Tetzlaff, J. M., Akl, E. A., Brennan, S. E., Chou, R., Glanville, J., Grimshaw, J. M., Hróbjartsson, A., Lalu, M. M., Li, T., Loder, E. W., Mayo-Wilson, E., McDonald, S., … Moher, D. (2021). The PRISMA 2020 statement: An updated guideline for reporting systematic reviews. BMJ, 372, n71. https://doi.org/10.1136/bmj.n71 [Google Scholar] [Crossref]

15. Wamba, S. F., Queiroz, M. M., & Trinchera, L. (2021). Dynamics between blockchain adoption determinants and supply chain performance: An empirical investigation. International Journal of Production Economics, 229, 107791. https://doi.org/10.1016/j.ijpe.2020.107791 [Google Scholar] [Crossref]

16. Tranfield, D., Denyer, D., & Smart, P. (2003). Towards a methodology for developing evidence-informed management knowledge by means of systematic review. British Journal of Management, 14(3), 207–222. https://doi.org/10.1111/1467-8551.00375 [Google Scholar] [Crossref]

17. Sterne, J. A., Hernán, M. A., Reeves, B. C., Savović, J., Berkman, N. D., Viswanathan, M., Henry, D., Altman, D. G., Ansari, M. T., Boutron, I., Carpenter, J. R., Chan, A. W., Churchill, R., Deeks, J. J., Hróbjartsson, A., Kirkham, J., Jüni, P., Loke, Y. K., Pigott, T. D., … Higgins, J. P. (2016). ROBINS-I: A tool for assessing risk of bias in non-randomised studies of interventions. BMJ, 355, i4919. https://doi.org/10.1136/bmj.i4919 [Google Scholar] [Crossref]

18. Thomas, J., & Harden, A. (2008). Methods for the thematic synthesis of qualitative research in systematic reviews. BMC Medical Research Methodology, 8(1), 45. https://doi.org/10.1186/1471-2288-8-45 [Google Scholar] [Crossref]

19. Zawawi, E. M. A., Kamaruzzaman, S. N., & Ithnin, N. (2021). FM outsourcing in Malaysia: A Fuzzy Delphi investigation. Journal of Facilities Management, 19(3), 312–332. https://doi.org/10.1108/JFM-09-2020-0063 [Google Scholar] [Crossref]

20. Yin, X., & Zheng, S. (2022). Validating AI adoption framework for construction industry through Fuzzy Delphi Method. Automation in Construction, 138, 104242. https://doi.org/10.1016/j.autcon.2022.104242 [Google Scholar] [Crossref]

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