Keywords
Sales automation
Benefits
Challenges
Trends
Business strategies
How to Cite
Abstract
Artificial intelligence has been established as a technology that transforms business strategies by optimizing sales processes through automation, predictive models, and advanced personalization. Although multiple studies exist, there is still insufficient evidence to draw firm conclusions about its overall impact on organizations. Therefore, this systematic review aimed to examine the incorporation of artificial intelligence into business strategies for sales process automation, identifying its trends, benefits, and challenges in order to provide a solid foundation for future research. The search conducted in Scopus, Web of Science (WoS), ScienceDirect, SpringerLink, IEEE Xplore, and Google Scholar yielded a total of 120 articles; after the screening process, 10 studies were selected. The methodological quality of the included articles was assessed using an adaptation of the NOS scale. The findings indicated that AI increases predictive accuracy, enhances customer management, and enables hyper-personalization and operational efficiency. They also revealed challenges such as data quality issues, cultural resistance, talent shortages, and ethical risks. It was concluded that AI is an effective tool for optimizing commercial processes; however, further research is needed to strengthen the evidence regarding its effectiveness and sustainability.
References
Agostinho, C., Dikopoulou, Z., Lavasa, E., Perakis, K., Pitsios, S., Branco, R., Reji, S., Hetterich, J., Biliri, E., Lampathaki, F., Rodríguez Del Rey, S., & Gkolemis, V. (2023). Explainability as the key ingredient for AI adoption in Industry 5.0 settings. Frontiers in Artificial Intelligence, 6. https://doi.org/10.3389/frai.2023.1264372
Badenes-Plá, V., & Molares-Cardoso, J. (2023). La inteligencia artificial ante los retos de la industria de la moda. Beneficios y aplicaciones en la fase de comercialización y marketing. Razón y Palabra, 27(118), 20–32. https://doi.org/10.26807/rp.v27i118.2073
Bae, J.-M. (2016). A suggestion for quality assessment in systematic reviews of observational studies in nutritional epidemiology. EpiH Epidemiology and Health, 38, e2016014. https://doi.org/10.4178/epih.e2016014
Beyari, H., & Hashem, T. (2025). The role of artificial intelligence in personalizing social media marketing strategies for enhanced customer experience. Behavioral Sciences, 15(5), 700. https://doi.org/10.3390/bs15050700
Blahušiaková, M. (2023). Business process automation – new challenges to increasing the efficiency and competitiveness of companies. Strategic Management, 28(3), 18–33 https://doi.org/10.5937/StraMan2300038B
Boonmee, C., Mangkalakeeree, J., & Jeong, Y. (2025). Towards sustainable digital transformation: AI adoption barriers and enablers among SMEs in Northern Thailand. Sustainable Futures, 10, 101169. https://doi.org/10.1016/j.sftr.2025.101169
Bussmann, N., Giudici, P., Marinelli, D., & Papenbrock, J. (2021). Explainable machine learning in credit risk management. Computational Economics, 57, 203–216. https://doi.org/10.1007/s10614-020-10042-0
Ching Ruíz, Y. (2024). El uso de la inteligencia artificial en la transformación del mercadeo: Tendencias y futuro. Centros: Revista Científica Universitaria, 13(2), 291–296. https://doi.org/10.48204/j.centros.v13n2.a5304
Cillo, P., & Rubera, G. (2025). Generative AI in innovation and marketing processes: A roadmap of research opportunities. Journal of the Academy of Marketing Science, 53, 684–701. https://doi.org/10.1007/s11747-024-01044-7
Coronado Garcia, B. (2024). Inteligencia artificial aplicada al comercio. Ra-Ma Editorial. https://books.google.com.pe/books/about/Inteligencia_Artificial_aplicada_al_come.html?id=Y603EQAAQBAJ&redir_esc=y
Cuesta-Valiño, P., Kazakov, S., Durán-Álamo, P & Gutiérrez-Rodríguez, P. (2025). The role of AI as an unconventional salesperson in consumer buying decisions, satisfaction and happiness. European Research on Management and Business Economics. 31(2). https://doi.org/10.1016/j.iedeen.2025.100278
Gkikas, D. C., & Theodoridis, P. K. (2024). Predicting online shopping behavior: Using machine learning and Google Analytics to classify user engagement. Applied Sciences, 14(23), 11403. https://doi.org/10.3390/app142311403
Jena, S., Yang, J., & Tan, F. (2023). Unlocking sales growth: Account prioritization engine with explainable AI. arXiv Cornell University, (arXiv:2306.07464) https://doi.org/10.4852023)50/arXiv.2306.07464
Khneyzer, C., Boustany, Z., & Dagher, J. (2024). AI-driven chatbots in CRM: Economic and managerial implications across industries. Administrative Sciences, 14(8), 182. https://doi.org/10.3390/admsci14080182
Kumar, V., Ashraf, A. R., & Nadeem, W. (2024). AI-powered marketing: What, where, and how? International Journal of Information Management, 77, 102783. https://doi.org/10.1016/j.ijinfomgt.2024.102783
Landeo Julcarima, V. A., Morales Del Pozo, P. Z., Vilca Rodríguez, R. R., López Miguel, M. L., & Cerrón León, W. F. (2024). Inteligencia artificial generativa y su impacto en el marketing y las ventas. Universidad Privada de Huancayo Franklin Roosevelt. https://doi.org/10.47186/visct.v8i2.136
Madanchian, M. (2024). The impact of artificial intelligence marketing on e-commerce sales. Systems, 12(10), 429. https://doi.org/10.3390/systems12100429
Marcillo, F., Vinueza Verdezoto, Á. E., Romero López, A. M., & Begnini, L. (2024). La inteligencia artificial como aliada en la gestión de ventas: Caso de estudio empresa MakroHospital. Revista Científica Kosmos, 3(2), 33-48. https://doi.org/10.62943/rck.v3n2.2024.90
Minh, D., Wang, H. X., Li, Y. F. & Nguyen, T. N. (2022). Explainable artificial intelligence: A comprehensive review. Artificial Intelligence Review, 55, 3503–3568. https://doi.org/10.1007/s10462-021-10088-y
Nair, R., Bose, A., Iyer, M., & Chopra, R. (2023). Optimizing Sales Automation Workflows with AI: Leveraging Natural Language Processing and Reinforcement Learning Algorithms Journal of AI ML Research, 12(2). https://joaimlr.com/index.php/v1/article/view/20
Nandakishor, M. (2025). SalesRLAgent: A reinforcement learning approach for real-time sales conversion prediction and optimization. Cornell University. (arXiv:2503.23303) [Preprint]. arXiv. https://doi.org/10.48550/arXiv.2503.23303
Nidhi.V, Prakash G., A., Balaji, B., Aakancha, R., Santhi, M., & Sanjana, G. (2025). AI in the enterprise: Overcoming adoption barriers and maximizing business value. Journal of Information Systems Engineering and Management, 10(29s). https://doi.org/10.52783/jisem.v10i29s.4540
Oldemeyer, L., Jede, A., & Teuteberg, F. (2025). Influence of company size and AI implementation challenges in manufacturing companies. Journal of Global Entrepreneurship Research, 15(42). https://doi.org/10.1007/s40497-025-00446-3
Ortiz, F. (2025). Desafíos de implementar IA en la gestión empresarial. Jounal contribution https://doi.org/10.6084/m9.figshare.28400798
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., McGuinness, L. A… & Alonso-Fernández, S. (2021). Declaración PRISMA 2020: una guía actualizada para la publicación de revisiones sistemáticas. Revista Española de Cardiología, 74(9), 790–799. https://doi.org/10.1016/j.recesp.2021.06.016
Pionce Arteaga, M. A., Caicedo Plúa, C. R., Delgado Lucas, H. B., & Murillo Quimiz, L. R. (2022). Chatbots para ventas y atención al cliente. Journal Tech Innovation, 1(1), 107–116. https://doi.org/10.47230/Journal.TechInnovation.v1.n1.2022.107-116
Quispe Mendoza, R., García Gamboa, L. K., Riveros Saona, L. E., & Ruiz Castillo, N. E. (2024). Beneficios del marketing digital optimizados por la inteligencia artificial. SCIÉNDO, 27(4). https://doi.org/10.17268/sciendo.2024.094
Rezazadeh, A. (2020). AA Generalized Flow for B2B Sales Predictive Modeling: An Azure Machine Learning Approach. (arXiv:2002.01441) https://doi.org/10.48550/arXiv.2002.01441
Ribeiro, A., López Rivero, A. J., & Abrantes, J. L. (2025). The impact of artificial intelligence on consumer behavior towards brands: a systematic review. Electronic Commerce Research. https://doi.org/10.1007/s10660-025-10063-7
Rivera-Montaño, S. A. (2023). Impacto de la inteligencia artificial (IA) en la efectividad de las estrategias de marketing personalizado. Revista Científica Anfibios, 6(2), 70–81. https://doi.org/10.37979/afb.2023v6n2.138
Roy, S. K., Tehrani, A. N., Pandit, A., Apostolidis, C., & Ray, S. (2025). AI-capable relationship marketing: Shaping the future of customer relationships. Journal of Business Research. 192. https://doi.org/10.1016/j.jbusres.2025.115309
Sánchez-Chero, J. A., Sánchez-Chero, M. J., & Villegas-Yarlequé, M. (2025). Inteligencia artificial: impactos y desafíos en las contrataciones públicas. Revisión sistemática Universitas-XXI, Revista de Ciencias Sociales y Humanas, (43), 37–63. https://doi.org/10.17163/uni.n43.2025.02
Segura Torres, M. J., Navas Espín, W. R., Zurita Hurtado, H. A., Parra López, R. A., & Zambrano Silva, D. H. (2025). Uso de chatbots inteligentes como herramienta estratégica para la mejora de la atención al cliente en empresas de telecomunicaciones en Ecuador . Arandu UTIC, 12(2), 3910–3919. https://doi.org/10.69639/arandu.v12i2.1198
Terblanche, N., Molyn, J., de Haan, E., & Nilsson, V. O. (2022). Comparing artificial intelligence and human coaching goal attainment efficacy. PLOS ONE, 17(6), e0270255. https://doi.org/10.1371/journal.pone.0270255
Viriyasitavat, W., Da Xu, L., Niyato, D., Bi, Z., & Hoonsopon, D. (2022). Applications of blockchain in business processes: A Comprehensive Review. IEEE Access, 10, 118900–118925. https://doi.org/10.1109/ACCESS.2022.3217794
Ye, Z., Zhang, L., Zhou, Z., Pan, K., Liu, X., Liu, W., & Wu, Q. (2023). Mechanical Properties of the Combination of Surrounding Rock and Primary Support under Impacting Load. Sustainability, 15(5), 4410. https://doi.org/10.3390/su15054410
Yin, J., Qiu, X., & Wang, Y. (2025). The impact of AI-personalized recommendations on clicking intentions: Evidence from Chinese E-Commerce. Journal of Theoretical and Applied Electronic Commerce Research, 20(1). https://doi.org/10.3390/jtaer20010021
Yoo, J. W., Park, J., & Park, H. (2024). The impact of AI-enabled CRM systems on organizational competitive advantage: A mixed-method approach using BERTopic and PLS-SEM. Heliyon, 10(16), e36392. https://doi.org/10.1016/j.heliyon.2024.e36392
Zhang, Z., Jiang, Y., Li, Q., & Han, A. (2025). OTPTO: Joint Product Selection and Inventory Optimization in Fresh E-commerce Front-End Warehouses. (arXiv:2505.23421) [Preprint]. arXiv. https://doi.org/10.48550/arXiv.2505.23421
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.