Artificial Intelligence in Small and Medium Enterprises: Its Influence on Business Performance in the Bangsamoro Autonomous Region in Muslim Mindanao

Artificial intelligence (AI) is a key driver of digital transformation, yet its adoption among small and medium enterprises (SMEs) remains uneven, particularly in developing regions. This study examined the readiness of SMEs in the Bangsamoro Autonomous Region in Muslim Mindanao (BARMM), Philippines, to adopt AI and its relationship with business performance using the Technology–Organization–Environment (TOE) framework. A quantitative descriptive–correlational design was employed, with data collected from 150 SME owners and managers, and analyzed using descriptive statistics and Pearson correlation. The results indicate that SMEs demonstrate moderate technological and organizational readiness but low environmental readiness, reflecting limited external support and market demand. Business performance was also found to be moderate, suggesting that AI adoption yields incremental improvements in operational and economic outcomes. Correlation analysis revealed that AI readiness is significantly associated with business performance, with organizational readiness showing the strongest influence. The study concludes that SMEs are in a transitional stage of digital transformation, where internal capabilities play a more critical role than external factors, and emphasizes the need to strengthen human capital, technological capacity, and institutional support to enhance AI adoption and competitiveness.

artificial intelligence, SMEs, TOE framework, business performance, digital transformation

1. Alaimo, C., Kallinikos, J., & Valderrama, E. (2023). Artificial intelligence and the transformation of organizations. Information and Organization, 33(2), 100457. https://doi.org/10.1016/j.infoandorg.2023.100457 [Google Scholar] [Crossref]

2. Bughin, J., Seong, J., Manyika, J., Chui, M., & Joshi, R. (2018). Notes from the AI frontier: Modeling the impact of artificial intelligence on the world economy. McKinsey Global Institute. [Google Scholar] [Crossref]

3. Dwivedi, Y. K., Hughes, L., Ismagilova, E., Aarts, G., Coombs, C., Crick, T., … Williams, M. D. (2021). Artificial intelligence (AI): Multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research. International Journal of Information Management, 57, 101994. https://doi.org/10.1016/j.ijinfomgt.2019.08.002 [Google Scholar] [Crossref]

4. Gangwar, H., Date, H., & Ramaswamy, R. (2015). Understanding determinants of cloud computing adoption using an integrated TAM-TOE model. Journal of Enterprise Information Management, 28(1), 107–130. https://doi.org/10.1108/JEIM-08-2013-0065 [Google Scholar] [Crossref]

5. Haefner, N., Wincent, J., Parida, V., & Gassmann, O. (2022). Artificial intelligence and innovation management: A review, framework, and research agenda. Technological Forecasting and Social Change, 178, 121548. https://doi.org/10.1016/j.techfore.2022.121548 [Google Scholar] [Crossref]

6. Maroufkhani, P., Iranmanesh, M., Ghobakhloo, M., Nilashi, M., & Fathi, M. (2022). Big data analytics adoption: Determinants and performances among small and medium-sized enterprises. Information Systems Frontiers, 24(2), 563–578. https://doi.org/10.1007/s10796-020-10092-5 [Google Scholar] [Crossref]

7. Ransbotham, S., Kiron, D., Gerbert, P., & Reeves, M. (2021). Expanding AI’s impact with organizational learning. MIT Sloan Management Review, 62(4), 1–9. [Google Scholar] [Crossref]

8. Trocin, C., Mikalef, P., Papamitsiou, Z., & Conboy, K. (2023). Responsible AI for digital transformation: A research agenda. Information Systems Frontiers, 25, 1271–1287. https://doi.org/10.1007/s10796-021-10139-7 [Google Scholar] [Crossref]