Keywords
How to Cite
Abstract
The present article analyzes the relationship between the use of artificial intelligence (AI) tools as a determining factor in the quality of strategic decision-making in small and medium-sized enterprises (SMEs) in the canton of Riobamba, Ecuador. Despite technological advances and the growing interest in adopting AI-based solutions, many SMEs face structural limitations such as a lack of integration in their processes and low employee training levels. In this context, the objective of the study is to examine how the use and integration of AI in strategic processes, along with human talent training, influence the effectiveness of business strategic decisions. The article adopts a mixed-method approach with an exploratory, descriptive, correlational, and cross-sectional research design. A structured survey was applied as the data collection technique. The results revealed that the variable "use of AI tools" is statistically significant (p = 0.000), while the integration of AI into strategic processes and staff training did not show relevant significance levels. This finding suggests that strengthening the effective use of artificial intelligence technologies could be a strategic path to improving decision-making in SMEs, although it is also necessary to reinforce training and the operational alignment of these technologies to maximize their impact.
References
Abreu, J. L. (2012). Hipótesis, método & diseño de investigación. PhilPapers.
Ander-Egg, E. (2003). La ciencia: su método y la expresión del conocimiento científico. Lumen.
Annual Report 2023-2024. (2024). World Economic Forum. https://www.weforum.org/publications/annual-report-2023-2024/
Antonio, R., Rivero, A., Medardo, G., Borja, A., Grimaldo, M.-E., Pérez, H., Luis, T.-P., Bastidas González, D., Pedro, M.-E., & Saltos García, A. (2025). Impacto de la inteligencia artificial en la innovación empresarial en PYMES: Perspectiva desde la educación superior. Revista InveCom, 5(4), 1–7. https://doi.org/10.5281/zenodo.15272784
Anttila, V., Bulik-Sullivan, B., Finucane, H. K., Walters, R. K., Bras, J., Duncan, L., Escott-Price, V., Falcone, G. J., Gormley, P., Malik, R., Patsopoulos, N. A., Ripke, S., Wei, Z., Yu, D., Lee, P. H., Turley, P., Grenier-Boley, B., Chouraki, V., Kamatani, Y., … & Neale, B. M. (2018). Analysis of shared heritability in common disorders of the brain. Science, 360(6395). https://doi.org/10.1126/science.aap8757
Arce, C. G. M., Haro, I. M., Gavilanes, J. A. C., & Arce, E. X. M. (2024). La inteligencia artificial como motor de innovación en los negocios. Magazine de las Ciencias: Revista de Investigación e Innovación, 9(4), 114–129. https://doi.org/10.33262/rmc.v9i4.3263
Atshan, N., Jamaludin, H., Tong, C. S., Al-Abrrow, H., & Abbas, S. (2025). A conceptual framework for exploring the use of a green marketing mix and brand image to attract customers for green services while visiting a hotel: A step toward marketing sustainability. Multidisciplinary Reviews, 8(1). https://doi.org/10.31893/multirev.2025019
Auton, A., Abecasis, G. R., Altshuler, D. M., Durbin, R. M., Bentley, D. R., Chakravarti, A., Clark, A. G., Donnelly, P., Eichler, E. E., Flicek, P., Gabriel, S. B., Gibbs, R. A., Green, E. D., Hurles, M. E., Knoppers, B. M., Korbel, J. O., Lander, E. S., Lee, C., Lehrach, H., … & Schloss, J. A. (2015). A global reference for human genetic variation. Nature, 526(7571), 68–74. https://doi.org/10.1038/nature15393
Azevedo, F. A. C., Carvalho, L. R. B., Grinberg, L. T., Farfel, J. M., Ferretti, R. E. L., Leite, R. E. P., Filho, W. J., Lent, R., & Herculano-Houzel, S. (2009). Equal numbers of neuronal and nonneuronal cells make the human brain an isometrically scaled-up primate brain. Journal of Comparative Neurology, 513(5), 532–541. https://doi.org/10.1002/cne.21974
Branigan, A. R., McCallum, K. J., & Freese, J. (2013). Variation in the heritability of educational attainment: An international meta-analysis. Social Forces, 92(1), 109–140. https://doi.org/10.1093/sf/sot076
Briones, G. (2003). Métodos y técnicas de investigación para las ciencias sociales. Trillas.
Bulik-Sullivan, B., Loh, P. R., Finucane, H. K., Ripke, S., Yang, J., Patterson, N., Daly, M. J., Price, A. L., Neale, B. M., Corvin, A., Walters, J. T. R., Farh, K. H., Holmans, P. A., Lee, P., Collier, D. A., Huang, H., Pers, T. H., Agartz, I., Agerbo, E., … & O’Donovan, M. C. (2015). LD score regression distinguishes confounding from polygenicity in genome-wide association studies. Nature Genetics, 47(3), 291–295. https://doi.org/10.1038/ng.3211
Castillo Martínez, K., Aguilar Rodríguez, J. A., & Madrigal Rentería, A. S. (2024). Desafíos éticos de la inteligencia artificial generativa en las nuevas formas organizacionales. Revista Digital de Tecnologías Informáticas y Sistemas, 8(1), 46–55. https://doi.org/10.61530/redtis.vol8.n1.2024.209
Checkland, P. (1999). Systems thinking, systems practice. John Wiley & Sons.
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(3), 684–701. https://doi.org/10.1007/s11747-024-01044-7
Clarke, R. (2019). Principles and business processes for responsible AI. Computer Law & Security Review, 35(4), 410–422. https://doi.org/10.1016/j.clsr.2019.04.007
Creswell, J. W. (2009). Research design: Qualitative, quantitative, and mixed methods approaches. SAGE Publications.
Dahnke, G. L. (1989). La comunicación humana: Ciencia social. Investigación y Comunicación.
Dastile, X., Celik, T., & Potsane, M. (2020). Statistical and machine learning models in credit scoring: A systematic literature survey. Applied Soft Computing, 91. https://doi.org/10.1016/j.asoc.2020.106263
Davies, G., Lam, M., Harris, S. E., Trampush, J. W., Luciano, M., Hill, W. D., Hagenaars, S. P., Ritchie, S. J., Marioni, R. E., Fawns-Ritchie, C., Liewald, D. C., Okely, J. A., Ahola-Olli, A. V., Barnes, C. L., Bertram, L., Bis, J. C., Burdick, K. E., Christoforou, A., DeRosse, P., … & Deary, I. J. (2018). Study of 300,486 individuals identifies 148 independent genetic loci influencing general cognitive function. Nature Communications, 9(1). https://doi.org/10.1038/s41467-018-04362-x
Dong, X., & McIntyre, S. H. (2014). The second machine age: Work, progress, and prosperity in a time of brilliant technologies. Quantitative Finance, 14(11), 1895–1896. https://doi.org/10.1080/14697688.2014.946440
Dumitriu, D., & Popescu, M. A. M. (2020). Artificial intelligence solutions for digital marketing. Procedia Manufacturing, 46, 630–636. https://doi.org/10.1016/j.promfg.2020.03.090
Esther, S., Vera, M., Jorge, J.-E., Nava, E., Garro, O., César, U., Javier, R., & Cedeño, C. (2025). La participación de la inteligencia artificial en la toma de decisiones gerenciales. Revista InveCom, 5(4), 1–12. https://doi.org/10.5281/zenodo.14816449
Etikan, I., & Bala, K. (2017). Combination of probability random sampling method with non probability random sampling method (sampling versus sampling methods). Biometrics & Biostatistics International Journal, 5(6). https://doi.org/10.15406/bbij.2017.05.00148
García-Vera, Y. S., Juca-Maldonado, F. X., & Torres-Gallegos, V. (2023). Automatización de procesos contables mediante inteligencia artificial: Oportunidades y desafíos para pequeños empresarios ecuatorianos. Revista Transdisciplinaria de Estudios Sociales y Tecnológicos, 3(3), 68–74. https://doi.org/10.58594/rtest.v3i3.93
González-Santiago, E., Acuña-Gamboa, L. A., & Velasco-Núñez, E. (2022). Habilidades digitales en la educación superior: Una necesidad en la formación de ingenieros civiles. Revista Tecnológica-Educativa Docentes 2.0, 15(1), 27–40. https://doi.org/10.37843/rted.v15i1.286
Heath, A. C., Berg, K., Eaves, L. J., Solaas, M. H., Corey, L. A., Sundet, J., Magnus, P., & Nance, W. E. (1985). Education policy and the heritability of educational attainment. Nature, 314(6013), 734–736. https://doi.org/10.1038/314734a0
Hernández Sampieri, R., Fernández Collado, C., & Baptista Lucio, M. del P. (2014). Metodología de la investigación. McGraw-Hill.
Isaías, R., Valverde, M., Hernán, D., Oyola, S., Belén, A., Santillán, M., Héctor, E., Vergara Velasquez, Y., Natanael, A., & León, B. (2024). Análisis del uso de la inteligencia artificial en las microempresas del cantón Guayaquil 2024. Ciencia Latina Revista Científica Multidisciplinar, 8(4), 3887–3902. https://doi.org/10.37811/cl_rcm.v8i4.12612
Jesani, A., & Srinivasan, S. (2019). New Drugs and Clinical Trials Rules, 2019: The market trumps ethics and participant rights. Indian Journal of Medical Ethics, 4(2), 89–91. https://doi.org/10.20529/IJME.2019.020
Kast, F. E., & Rosenzweig, J. E. (1972). General systems theory: Applications for organization and management. Academy of Management Journal, 15(4), 447–465. https://doi.org/10.5465/255141
Kopalle, P. K., Gangwar, M., Kaplan, A., Ramachandran, D., Reinartz, W., & Rindfleisch, A. (2022). Examining artificial intelligence (AI) technologies in marketing via a global lens: Current trends and future research opportunities. International Journal of Research in Marketing, 39(2), 522–540. https://doi.org/10.1016/j.ijresmar.2021.11.002
Kumar, S., Talukder, M. B., & Tyagi, P. K. (2024). The impact of artificial intelligence on improving efficiency in service and implementing best practices in service marketing. En N. Kazancoglu, F. P. García Márquez, & I. Ozturk, (eds.). AI innovations in service and tourism marketing (pp. 57–79). IGI Global. https://doi.org/10.4018/979-8-3693-7909-7.ch004
Lee, J. J., Wedow, R., Okbay, A., Kong, E., Maghzian, O., Zacher, M., Nguyen-Viet, T. A., Bowers, P., Sidorenko, J., Karlsson Linnér, R., Fontana, M. A., Kundu, T., Lee, C., Li, H., Li, R., Royer, R., Timshel, P. N., Walters, R. K., Willoughby, E. A., … & Turley, P. (2018). Gene discovery and polygenic prediction from a genome-wide association study of educational attainment in 1.1 million individuals. Nature Genetics, 50(8), 1112–1121. https://doi.org/10.1038/s41588-018-0147-3
Maître, E., Sena, G. R., Chemli, Z., Chevalier, M., Dousset, B., Gitto, J. P., & Teste, O. (2022). The investigation of an event-based approach to improve commodities supply chain management. Brazilian Journal of Operations and Production Management, 19(2). https://doi.org/10.14488/BJOPM.2022.005
Malekpour, M., Gholami-Kordkheili, H., Yazdani, M., & Méndez-Suárez, M. (2024). Determining of marketing mix components to improve sales performance in startup companies in Islamic culture- Evidence from Iran. Engineering Economics, 35(3), 299–315. https://doi.org/10.5755/j01.ee.35.3.30314
Marioni, R. E., Ritchie, S. J., Joshi, P. K., Hagenaars, S. P., Okbay, A., Fischer, K., Adams, M. J., Hill, W. D., Davies, G., Nagy, R., Amador, C., Läll, K., Metspalu, A., Liewald, D. C., Campbell, A., Wilson, J. F., Hayward, C., Esko, T., Porteous, D. J., … & Benjamin, D. J. (2016). Genetic variants linked to education predict longevity. Proceedings of the National Academy of Sciences, 113(47), 13366–13371. https://doi.org/10.1073/pnas.1605334113
Mendoza, J., & Garza, J. B. (2017). La medición en el proceso de investigación científica: Evaluación de validez de contenido y confiabilidad. Revista Innovaciones de Negocios, 6(11), 1-19. https://doi.org/10.29105/rinn6.11-2
Okbay, A., Beauchamp, J. P., Fontana, M. A., Lee, J. J., Pers, T. H., Rietveld, C. A., Turley, P., Chen, G. B., Emilsson, V., Meddens, S. F. W., Oskarsson, S., Pickrell, J. K., Thom, K., Timshel, P., De Vlaming, R., Abdellaoui, A., Ahluwalia, T. S., Bacelis, J., Baumbach, C., … & Benjamin, D. J. (2016). Genome-wide association study identifies 74 loci associated with educational attainment. Nature, 533(7604), 539–542. https://doi.org/10.1038/nature17671
Okbay, A., Wu, Y., Wang, N., Jayashankar, H., Bennett, M., Nehzati, S. M., Sidorenko, J., Kweon, H., Goldman, G., Gjorgjieva, T., Jiang, Y., Hicks, B., Tian, C., Hinds, D. A., Ahlskog, R., Magnusson, P. K. E., Oskarsson, S., Hayward, C., Campbell, A., … & Young, A. I. (2022). Polygenic prediction of educational attainment within and between families from genome-wide association analyses in 3 million individuals. Nature Genetics, 54(4), 437–449. https://doi.org/10.1038/s41588-022-01016-z
Pers, T. H., Karjalainen, J. M., Chan, Y., Westra, H. J., Wood, A. R., Yang, J., Lui, J. C., Vedantam, S., Gustafsson, S., Esko, T., Frayling, T., Speliotes, E. K., Boehnke, M., Raychaudhuri, S., Fehrmann, R. S. N., Hirschhorn, J. N., & Franke, L. (2015). Biological interpretation of genome-wide association studies using predicted gene functions. Nature Communications, 6(1). https://doi.org/10.1038/ncomms6890
Pomerol, J.-C., & Adam, F. (2005). On the legacy of Herbert Simon and his contribution to decision making support systems and artificial intelligence. En M. B. Mora, G. Forgionne, & J. N. D. Gupta, (eds.). Intelligent decision-making support systems (pp. 25–44). Springer. https://doi.org/10.1007/1-84628-231-4_2
Porter, M. E. (2013). Venta competitiva: Creación y sostenimiento de un desempeño superior. Pirámide.
Rangel Saltos, J. E., Triviño Bloisse, S. Y., Lavayen Yavar, H., & Villamar Piguave, W. G. (2024). Inteligencia artificial: La nueva transformación de la administración empresarial. RECIAMUC, 8(1), 759–767. https://doi.org/10.26820/reciamuc/8.(1).ene.2024.759-767
Ribeiro dos Santos, D. (2025). Printable photovoltaic photoreceptors for the factory of the future and the Internet of Things: Toward energy harvesting and wireless optical communications [Tesis doctoral, Université Paris-Saclay]. https://doi.org/10.34894/VQ1DJA
Rietveld, C. A., Medland, S. E., Derringer, J., Yang, J., Esko, T., Martin, N. W., Westra, H. J., Shakhbazov, K., Abdellaoui, A., Agrawal, A., Albrecht, E., Alizadeh, B. Z., Amin, N., Barnard, J., Baumeister, S. E., Benke, K. S., Bielak, L. F., Boatman, J. A., Boyle, P. A., … & Koellinger, P. D. (2013). GWAS of 126,559 individuals identifies genetic variants associated with educational attainment. Science, 340(6139), 1467–1471. https://doi.org/10.1126/science.1235488
Rodgers, W., Murray, J. M., Stefanidis, A., Degbey, W. Y., & Tarba, S. Y. (2023). An artificial intelligence algorithmic approach to ethical decision-making in human resource management processes. Human Resource Management Review, 33(1). https://doi.org/10.1016/j.hrmr.2022.100925
Sezer, O. B., & Ozbayoglu, A. M. (2018). Algorithmic financial trading with deep convolutional neural networks: Time series to image conversion approach. Applied Soft Computing, 70, 525–538. https://doi.org/10.1016/j.asoc.2018.04.024
Simon, H. A. (1977). The new science of management decision. Prentice-Hall.
Taherdoost, H. (2022). Designing a questionnaire for a research paper: A comprehensive guide to design and develop an effective questionnaire. Asian Journal of Managerial Science, 11(1), 8–16. https://doi.org/10.51983/ajms-2022.11.1.3087
Tidd, J., & Bessant, J. (2021). Managing innovation: Integrating technological, market and organizational change. John Wiley & Sons.
Tsai, C. Y., Lin, Y. T., & Brown, I. K. (2024). Impacts of ChatGPT-assisted writing for EFL English majors: Feasibility and challenges. Education and Information Technologies, 29(17), 22427–22445. https://doi.org/10.1007/s10639-024-12722-y
Turley, P., Walters, R. K., Maghzian, O., Okbay, A., Lee, J. J., Fontana, M. A., Nguyen-Viet, T. A., Wedow, R., Zacher, M., Furlotte, N. A., Magnusson, P., Oskarsson, S., Johannesson, M., Visscher, P. M., Laibson, D., Cesarini, D., Neale, B. M., Benjamin, D. J., Agee, M., … & Pitts, S. J. (2018). Multi-trait analysis of genome-wide association summary statistics using MTAG. Nature Genetics, 50(2), 229–237. https://doi.org/10.1038/s41588-017-0009-4
Vera Salavarría, S. P., & Pico Bazurto, S. P. (2024). Inteligencia artificial en el desarrollo administrativo de la empresa moderna. Revista Científica Arbitrada Multidisciplinaria PENTACIENCIAS, 6(2), 264–282. https://doi.org/10.59169/pentaciencias.v6i2.1046
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.