Impact of artificial intelligence on the sales effectiveness of SMEs: A systematic review
DOI:
https://doi.org/10.47606/ACVEN/PH0392Keywords:
Artificial intelligence, SMEs, sales effectiveness, innovation, operational efficiency, digital transformationAbstract
Artificial Intelligence (AI) has become a key driver of digital transformation for small and medium-sized enterprises (SMEs), enhancing productivity, innovation, and operational efficiency. This study aims to analyze, through a systematic review of scientific literature published between 2021 and 2025, the impact of AI on the sales effectiveness of SMEs, focusing on operational efficiency, innovation, decision-making, and talent management. The results show that AI significantly enhances lead generation, cost reduction, and personalized marketing strategies while optimizing productivity and real-time data management. However, its implementation faces barriers such as the shortage of specialized personnel, financial limitations, and ethical challenges related to algorithm transparency. It is concluded that AI represents a strategic tool for competitive and sustainable growth in SMEs, provided it is adopted with proper technological and organizational planning.
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AlRoshoud, A. N., & El-Gohary, H. O. (2024). Exploring the impact of combining neuromarketing and AI adoption on the perceived effectiveness of social media advertising campaigns. International Journal of Customer Relationship Marketing and Management, 15(1), 1–28. https://doi.org/10.4018/ijcrmm.361975 DOI: https://doi.org/10.4018/IJCRMM.361975
Alyusha, A., & Ledentsov, A. (2022). Industry Modern: A solution for sustainable business performance’s technology challenges. Aptisi Transactions on Technopreneurship (ATT), 4(3), 306–312. https://doi.org/10.34306/att.v4i3.283 DOI: https://doi.org/10.34306/att.v4i3.283
Bertschek, I., & Wambach, A. (2024). AI in Europe – Is regulation the answer to being a laggard? The Economists’ Voice, 21(2), 379–385. https://doi.org/10.1515/ev-2024-0043 DOI: https://doi.org/10.1515/ev-2024-0043
Bettiol, M., Capestro, M., Di Maria, E., & Micelli, S. (2021). SMEs @ Industry 4.0: A comparison between top and average performers. Sinergie Italian Journal of Management, 39(3), 27–48. https://doi.org/10.7433/s116.2021.03 DOI: https://doi.org/10.7433/s116.2021.03
Gladysz, B., Matteri, D., Ejsmont, K., Corti, D., Bettoni, A., & Haber Guerra, R. (2023). Platform-based support for AI uptake by SMEs: Guidelines to design service bundles. Central European Management Journal, 31(4), 463–478. https://doi.org/10.1108/CEMJ-08-2022-0096 DOI: https://doi.org/10.1108/CEMJ-08-2022-0096
Green competitive advantage and SMEs: Is big data the missing link? (2023). Journal of Competitiveness, 15(1), 56–72. https://doi.org/10.7441/joc.2023.01.05 DOI: https://doi.org/10.7441/joc.2023.01.05
Kopka, A., & Fornahl, D. (2024). Artificial intelligence and firm growth—Catch-up processes of SMEs through integrating AI into their knowledge bases. Small Business Economics, 62(1), 63–85. https://doi.org/10.1007/s11187-023-00754-6 DOI: https://doi.org/10.1007/s11187-023-00754-6
Kraus, K., Kraus, N., Hryhorkiv, M., Kuzmuk, I., & Shtepa, O. (2022). Artificial intelligence in established Industry 4.0. WSEAS Transactions on Business and Economics, 19, 1884–1900. https://doi.org/10.37394/23207.2022.19.170 DOI: https://doi.org/10.37394/23207.2022.19.170
Lafuente, E., Rabetino, R., & Leiva, J. C. (2025). Learning from success and failure: Implications for entrepreneurs, SMEs, and policy. Small Business Economics, 64(1), 1–10. https://doi.org/10.1007/s11187-024-00889-0 DOI: https://doi.org/10.1007/s11187-024-00889-0
Lannon, F., Lyons, R., & O’Connor, C. (2024). Generation AI and family business: A perspective article. Journal of Family Business Management, 14(3), 470–474. https://doi.org/10.1108/JFBM-07-2023-0116 DOI: https://doi.org/10.1108/JFBM-07-2023-0116
Martinez, L. B., Scherger, V., & Orazi, S. (2023). Post-pandemic performance of micro, small and medium-sized enterprises: A Self-Organizing Maps application. Cogent Business & Management, 10(3), 2276944. https://doi.org/10.1080/23311975.2023.2276944 DOI: https://doi.org/10.1080/23311975.2023.2276944
Nasrollahi, M., Ramezani, J., & Sadraei, M. (2021). The impact of big data adoption on SMEs’ performance. Big Data and Cognitive Computing, 5(4), 68. https://doi.org/10.3390/bdcc5040068 DOI: https://doi.org/10.3390/bdcc5040068
Padilla Ospina, A. M., Ospina-Holguín, J. H., & Medina-Vásquez, J. E. (2022). Importancia y efecto de los factores financieros y asociados a la financiación en la intensidad de la innovación de las PyMEs colombianas. Revista de Métodos Cuantitativos para la Economía y la Empresa, 33(2), 1–20. https://doi.org/10.46661/revmetodoscuanteconempresa.5581 DOI: https://doi.org/10.46661/revmetodoscuanteconempresa.5581
Pfister, P., & Lehmann, C. (2023). Measuring the success of digital transformation in German SMEs. Journal of Small Business Strategy, 33(1), 1–17. https://doi.org/10.53703/001c.39679 DOI: https://doi.org/10.53703/001c.39679
Ragazou, K., Passas, I., Garefalakis, A., Galariotis, E., & Zopounidis, C. (2023). Big data analytics applications in information management driving operational efficiencies and decision-making: Mapping the field of knowledge with bibliometric analysis using R. Big Data and Cognitive Computing, 7(1), 13. https://doi.org/10.3390/bdcc7010013 DOI: https://doi.org/10.3390/bdcc7010013
Rojas-Berrio, S., Rincón-Novoa, J., Sánchez-Monrroy, M., Ascúa, R., & Montoya-Restrepo, L. A. (2022). Factors influencing 4.0 technology adoption in manufacturing SMEs in an emerging country. Journal of Small Business Strategy, 32(3), 1–17. https://doi.org/10.53703/001c.34608 DOI: https://doi.org/10.53703/001c.34608
Sandoval-Gómez, R. J., Álvarez-Cedillo, J. A., Castellanos-Sánchez, E. I., Álvarez-Sánchez, T., & Pérez-García, R. (2023). Development of a technological innovation and social entrepreneurship training program to generate services in a Mexican public entity. Eastern-European Journal of Enterprise Technologies, 6(13), 74–87. https://doi.org/10.15587/1729-4061.2023.289753 DOI: https://doi.org/10.15587/1729-4061.2023.289753
Segarra-Blasco, A., Tomàs-Porres, J., & Teruel, M. (2025). AI, robots and innovation in European SMEs. Small Business Economics, 64(3), 675–692. https://doi.org/10.1007/s11187-025-01017-2 DOI: https://doi.org/10.1007/s11187-025-01017-2
Strilets, V., Frolov, S., Datsenko, V., Tymoshenko, O., & Yatsko, M. (2022). State support for the digitalization of SMEs in European countries. Problems and Perspectives in Management, 20(4), 290–305. https://doi.org/10.21511/ppm.20(4).2022.22 DOI: https://doi.org/10.21511/ppm.20(4).2022.22
Taherizadeh, A., & Beaudry, C. (2023). An emergent grounded theory of AI-driven digital transformation: Canadian SMEs’ perspectives. Industry and Innovation, 30(9), 1244–1273. https://doi.org/10.1080/13662716.2023.2242285 DOI: https://doi.org/10.1080/13662716.2023.2242285
Weber, P. (2023). Unrealistic optimism regarding artificial intelligence opportunities in human resource management. International Journal of Knowledge Management, 19(1), 1–19. https://doi.org/10.4018/IJKM.317217 DOI: https://doi.org/10.4018/IJKM.317217
Zoubi, M. A., Alfaris, Y., Fraihat, B., Otoum, A., Nawasreh, M., & Alfandi, A. (2023). An extension of the diffusion of innovation theory for business intelligence adoption: A maturity perspective on project management. Uncertain Supply Chain Management, 11(2), 465–472. https://doi.org/10.5267/j.uscm.2023.3.003 DOI: https://doi.org/10.5267/j.uscm.2023.3.003
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Copyright (c) 2025 George Carrasco Camones, Merly Leyla León Palacios de Canales, Patricia Elizabeth Lossio Larrea, Enrique Wilfredo Puente Paredes, Ana Maria Holgado Quispe
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