Artificial Intelligence Applications Across Procurement Modules: A Systematic Literature Review
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Abstract
Artificial intelligence (AI) has become an important driver of procurement transformation, improving decision-making, operational efficiency, supplier management, and organizational resilience. Despite the rapid growth of AI research in procurement, the existing literature remains fragmented because previous studies have primarily examined AI from technological, digital transformation, or source-to-pay perspectives only. This fragmentation limits a comprehensive understanding of how AI applications differ across individual procurement modules. Therefore, this study aims to systematically synthesize the existing literature on AI applications across procurement modules by identifying AI implementation patterns, dominant AI technologies, organizational benefits, implementation challenges, and future research opportunities.
This study employed a systematic literature review (SLR) following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Relevant studies published between 2020 and 2026 were systematically identified, screened, and analyzed using procurement modules as the primary analytical perspective. The synthesis focuses on AI applications across spend analysis, strategic sourcing, supplier selection, contract management, purchase requisition, purchase order management, invoice processing, supplier relationship management, and supplier risk management.
The findings reveal that AI adoption is unevenly distributed across procurement modules. Strategic procurement activities, particularly supplier selection, strategic sourcing, contract management, and supplier risk management, have received substantially greater research attention than operational procurement activities. Machine learning remains the dominant technology for predictive analytics and decision optimization, while natural language processing and large language models are primarily employed for document-intensive procurement activities. Furthermore, generative AI is emerging as a transformative capability that extends AI applications beyond process automation towards procurement planning, supplier collaboration, knowledge management, and strategic decision support. The review also identifies recurring implementation challenges related to data quality, organizational readiness, AI governance, explainability, and human-AI collaboration.
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References
Abdulla, A., & Baryannis, G. (2024). A hybrid multi-criteria decision-making and machine learning approach for explainable supplier selection. Supply Chain Analytics, 7, 100074. https://doi.org/10.1016/j.sca.2024.100074
Aejas, B., Belhi, A., & Bouras, A. (2025). Using AI to ensure reliable supply chains: Legal relation extraction for sustainable and transparent contract automation. Sustainability, 17(9), 4215. https://doi.org/10.3390/su17094215
Althabatah, A., Yaqot, M., Menezes, B., & Kerbache, L. (2023). Transformative procurement trends: Integrating Industry 4.0 technologies for enhanced procurement processes. Logistics, 7(3), 63. https://doi.org/10.3390/logistics7030063
Arslan, H., Işık, Y. E., & Görmez, Y. (2024). A deep learning-based solution for digitization of invoice images with automatic invoice generation and labelling. International Journal on Document Analysis and Recognition, 27(1), 97–109. https://doi.org/10.1007/s10032-023-00449-4
Azman, N. A., Mohamed, A., & Jamil, A. M. (2021). Artificial intelligence in automated bookkeeping: A value-added function for small and medium enterprises. JOIV: International Journal on Informatics Visualization, 5(3), 224–230. https://doi.org/10.30630/joiv.5.3.669
Bafna, D., Jumla, T., Rajesh, V. S., Vijay, V., & Upadhyaya, S. R. (2024). Ad-spend analytics. In International Conference on Innovations in Computational Intelligence and Computer Vision (pp. 225–236). https://doi.org/10.1007/978-981-97-6992-6_18
Baviskar, D., Ahirrao, S., & Kotecha, K. (2021). Multi-layout unstructured invoice documents dataset: A dataset for template-free invoice processing and its evaluation using AI approaches. IEEE Access, 9, 101494–101512. https://doi.org/10.1109/ACCESS.2021.3096739
Charnor, I. T., & Quartey, E. K. (2024). Electronic procurement adoption and procurement performance: Does institutional quality matter? Business Process Management Journal, 30(6), 1783–1807. https://doi.org/10.1108/BPMJ-02-2024-0106
Cui, R., Li, M., & Zhang, S. (2022). AI and procurement. Manufacturing & Service Operations Management, 24(2), 691–706. https://doi.org/10.1287/msom.2021.0989
Dikmen, I., Eken, G., Erol, H., & Birgonul, M. T. (2025). Automated construction contract analysis for risk and responsibility assessment using natural language processing and machine learning. Computers in Industry, 166, 104251. https://doi.org/10.1016/j.compind.2025.104251
Fallah Lajimi, H., Hamedi, M., & Arab, A. (2026). An integrated risk management model with application to supplier segmentation. Supply Chain Forum: An International Journal, 27(2), 209–232. https://doi.org/10.1080/16258312.2025.2515817
Frederico, G. F., Kumar, V., & Garza-Reyes, J. A. (2021). Impact of the strategic sourcing process on the supply chain response to the COVID-19 effects. Business Process Management Journal, 27(6), 1775–1803. https://doi.org/10.1108/BPMJ-01-2021-0050
Ghosh, S., Mandal, M. C., & Ray, A. (2021). Strategic sourcing model for green supply chain management: An insight into automobile manufacturing units in India. Benchmarking: An International Journal, 29(10), 3097–3132. https://doi.org/10.1108/BIJ-06-2021-0333
Guida, M., Caniato, F., Moretto, A., & Ronchi, S. (2023). The role of artificial intelligence in the procurement process: State of the art and research agenda. Journal of Purchasing and Supply Management, 29(2), 100823. https://doi.org/10.1016/j.pursup.2023.100823
Hickok, M. (2024). Public procurement of artificial intelligence systems: New risks and future proofing. AI & Society, 39(3), 1213–1227. https://doi.org/10.1007/s00146-022-01572-2
Hoffreumon, C., Forman, C., & Van Zeebroeck, N. (2024). Make or buy your artificial intelligence? Complementarities in technology sourcing. Journal of Economics & Management Strategy. https://doi.org/10.1111/jems.12563
Hunger, T., Erfurth, P. J., Arnold, M. G., & Wichmann, M. G. (2025). Integrated strategic sourcing of critical raw materials in an era of global disruptions. International Journal of Production Economics, 292, 109879. https://doi.org/10.1016/j.ijpe.2025.109879
Joshi, A., Singh, M., Parashar, D., & Singh, M. (2026). A multi-agent large language model framework for intelligent vendor evaluation and risk-aware procurement decisions. Scientific Reports, 16(1), 50952. https://doi.org/10.1038/s41598-026-50952-x
Khan, S. A. R., Yu, Z., Kumar, A., & Zavadskas, E. K. (2023). Artificial intelligence, blockchain, and sustainable procurement: A systematic perspective on digital procurement transformation. Journal of Sustainable Finance & Investment. https://doi.org/10.1080/23789689.2023.2165782
Kim, C.-Y., Jeong, J.-G., Choi, S.-W., & Lee, E.-B. (2022). An AI-based automatic risks detection solution for plant owner’s technical requirements in equipment purchase order. Sustainability, 14(16), 10010. https://doi.org/10.3390/su141610010
Lakhal, N., Aazami, A., & Kummer, S. (2025). The role of artificial intelligence across the source-to-pay framework: Theoretical and practical aspects. Digital Business, 5(2), 100160. https://doi.org/10.1016/j.digbus.2025.100160
Li, X., Culmone, V., De Reyck, B., & Yoo, O. S. (2025). Automating Procurement Practices Using Artificial Intelligence. INFORMS Journal on Applied Analytics, 55(3), 195–223. https://doi.org/10.1287/inte.2023.0099
Maheshwari, J. G., Tanushri, N., Gowda, R., Bhutiyal, V. S., & Rao, K. R. (2026). AI-enabled procurement automation using generative artificial intelligence. IEEE Access, 14, 19099–19113. https://doi.org/10.1109/ACCESS.2026.3658394
Mirzaee, H., & Ashtab, S. (2024). Artificial intelligence adoption for sustainable procurement decision-making. Sustainability, 16(19), 8325. https://doi.org/10.3390/su16198325
Modgil, S., Singh, R. K., Mathiyazhagan, K., & Żywiołek, J. (2025). Role of generative AI towards sustainable procurement. The International Journal of Logistics Management. https://doi.org/10.1108/IJLM-01-2025-0062
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. The BMJ, 372, n71. https://doi.org/10.1136/bmj.n71
Reitsma, E., Haug, A., Hilletofth, P., & Johansson, E. (2025). A conceptualization and empirical investigation of strategic sourcing approaches for engineer-to-order manufacturers. Production Planning & Control, 36(13), 1733–1751. https://doi.org/10.1080/09537287.2024.2414331
Sahoo, S. K., Goswami, S. S., & Halder, R. (2024). Supplier selection in the age of Industry 4.0: A review on MCDM applications and trends. Decision Making Advances, 2(1), 32–47. https://doi.org/10.31181/dma21202420
Spreitzenbarth, J. M., Bode, C., & Stuckenschmidt, H. (2024). Artificial intelligence and machine learning in purchasing and supply management: A mixed-methods review of the state-of-the-art in literature and practice. Journal of Purchasing and Supply Management, 30, 100896. https://doi.org/10.1016/j.pursup.2024.100896
Tajally, A., Vamarzani, M. Z., Ghanavati-Nejad, M., Zeynali, F. R., Abbasian, M., & Bahengam, A. (2025). A hybrid machine learning-based decision-making model for viable supplier selection problem considering circular economy dimensions. Environment, Development and Sustainability, 27(3), 1–33. https://doi.org/10.1007/s10668-025-06014-9
Tang, Y., Zheng, C., Kassem, M., Xie, X., & Su, X. (2026). Bridging the research–practice gap in construction contract management with NLP and LLMs. Automation in Construction, 181, 106614. https://doi.org/10.1016/j.autcon.2025.106614
van Hoek, R. (2024). Insight from industry: Early lessons learned about AI adoption in core procurement processes, directions for managers and researchers. Supply Chain Management: An International Journal, 29(4), 794–803. https://doi.org/10.1108/SCM-02-2024-0143
Wu, P.-J., Cheng, W., & Goh, M. (2025). Quality risk management: Effects of cognitive bias and supplier risk levels. Asia Pacific Journal of Marketing and Logistics, 37(8). https://doi.org/10.1108/APJML-04-2025-0753
Zhou, H., Gao, B., Tang, S., Li, B., & Wang, S. (2025). Intelligent detection on construction project contract missing clauses based on deep learning and NLP. Engineering, Construction and Architectural Management, 32(3), 1546–1580. https://doi.org/10.1108/ECAM-02-2023-0172