TRANSFORMACIÓN DIGITAL E INTELIGENCIA ARTIFICIAL EN LA TOMA DE DECISIONES ESTRATÉGICAS: EL NUEVO PERFIL DEL ADMINISTRADOR EN LA ERA DE LOS DATOS
DOI:
https://doi.org/10.56238/revgeov16n4-045Palabras clave:
Transformación Digital, Inteligencia Artificial, Toma de Decisiones Estratégicas, Perfil del Administrador, Gobernanza de IAResumen
Este artículo analiza la intersección entre la Transformación Digital y la Inteligencia Artificial (IA) en la toma de decisiones estratégicas, con énfasis en el nuevo perfil del administrador en la era de los datos. A partir de una revisión sistemática de la literatura, se identificaron modelos, prácticas y competencias que orientan la integración de la IA en los procesos decisorios. Los resultados indican que la IA, cuando se aplica de manera estratégica y ética, amplía las capacidades humanas, mejora la precisión y la velocidad de las decisiones y fomenta la innovación organizacional. Sin embargo, desafíos como la explicabilidad de los algoritmos, la mitigación de sesgos, la gobernanza de datos y el cumplimiento normativo exigen de los gestores una preparación técnica, ética y adaptativa. Se concluye que el administrador contemporáneo debe actuar como un orquestador de recursos humanos y tecnológicos, integrando capacidades analíticas, visión estratégica y liderazgo ético para generar valor sostenible.
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Referencias
BRAUN, V.; CLARKE, V. Using thematic analysis in psychology. Qualitative Research in Psychology, v. 3, n. 2, p. 77-101, 2006. DOI: https://doi.org/10.1191/1478088706qp063oa.
BRYNJOLFSSON, E.; MCAFEE, A. Machine, Platform, Crowd: Harnessing Our Digital Future. New York: W. W. Norton & Company, 2017.
CONFEDERAÇÃO NACIONAL DA INDÚSTRIA (Brasil). Indústria 4.0 e transformação digital: avanços, desafios e oportunidades no Brasil. Brasília: CNI, 2023. Disponível em: https://www.portaldaindustria.com.br. Acesso em: 12 ago. 2025.
CSASZAR, F. A.; KETKAR, H.; KIM, H. Artificial Intelligence and Strategic Decision-Making: Evidence from Entrepreneurs and Investors. arXiv preprint, 2024. DOI: https://doi.org/10.48550/arXiv.2408.08811.
CRESWELL, J. W. Research design: qualitative, quantitative, and mixed methods approaches. 4. ed. Thousand Oaks: SAGE, 2014.
CUI, J. et al. AI-Driven Digital Transformation and Firm Performance: The Role of Green Innovation and Human–AI Collaboration. arXiv preprint, 2025. DOI: https://doi.org/10.48550/arXiv.2505.11558.
DAVENPORT, T. H.; RONANKI, R. Artificial Intelligence for the Real World. Harvard Business Review, v. 96, n. 1, p. 108-116, 2018.
DELOITTE BRASIL. Tech Trends 2023: tendências tecnológicas que impulsionam a transformação digital e orientam decisões estratégicas. 2023. Disponível em: https://www.deloitte.com/br/pt/about/press-room/release-tech-trends.html. Acesso em: 12 ago. 2025.
DE MEYER, A.; GARG, S. Leading in a Digital Age: How to Engage and Lead Your Team in a Digital World. Global Business and Organizational Excellence, v. 39, n. 6, p. 6-17, 2020. DOI: https://doi.org/10.1002/joe.22056.
DENYER, D.; TRANFIELD, D. Producing a systematic review. In: BUCHANAN, D. A.; BRYMAN, A. (ed.). The SAGE handbook of organizational research methods. London: SAGE, 2009. p. 671-689.
FLORIDI, L.; COWLS, J. A unified framework of five principles for AI in society. Harvard Data Science Review, v. 4, n. 1, 2022. DOI: https://doi.org/10.1162/99608f92.8cd550d1.
GUNTER, D. et al. Towards Responsible Artificial Intelligence for Strategic Decision-Making. AI and Ethics, v. 2, p. 355-367, 2022. DOI: https://doi.org/10.1007/s43681-021-00080-0.
HEIFETZ, R.; LINSKY, M. Leadership on the Line: Staying Alive through the Dangers of Change. Boston: Harvard Business Review Press, 2017.
JORDAN, M. I.; MITCHELL, T. M. Machine learning: Trends, perspectives, and prospects. Science, v. 349, n. 6245, p. 255-260, 2015. DOI: https://doi.org/10.1126/science.aaa8415.
KANE, G. C. et al. Strategy, not technology, drives digital transformation. MIT Sloan Management Review, v. 14, p. 1-25, 2015.
KIM, K.; KIM, B. Decision-Making Model for Reinforcing Digital Transformation Strategies Based on Artificial Intelligence Technology. Information, v. 13, n. 5, 2025. DOI: https://doi.org/10.3390/info13050245.
KITCHENHAM, B.; CHARTERS, S. Guidelines for performing systematic literature reviews in software engineering. EBSE Technical Report, Keele University, 2007.
MARTIN, K. Ethical implications and accountability of algorithms. Journal of Business Ethics, v. 160, n. 4, p. 835-850, 2019. DOI: https://doi.org/10.1007/s10551-018-3921-3.
MITCHELL, M. et al. Algorithmic fairness: Choices, assumptions, and definitions. Annual Review of Statistics and Its Application, v. 8, p. 141-163, 2021. DOI: https://doi.org/10.1146/annurev-statistics-042720-125902.
PAGE, M. J. et al. The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ, v. 372, n. 71, p. 1-9, 2021. DOI: https://doi.org/10.1136/bmj.n71.
RUSSELL, S.; NORVIG, P. Artificial Intelligence: A Modern Approach. 4. ed. Upper Saddle River: Prentice Hall, 2022.
SCHMITT, M. Strategic Integration of Artificial Intelligence in the C-Suite: The Role of the Chief AI Officer. arXiv preprint, 2024. DOI: https://doi.org/10.48550/arXiv.2407.10247.
SNYDER, H. Literature review as a research methodology: An overview and guidelines. Journal of Business Research, v. 104, p. 333-339, 2019. DOI: https://doi.org/10.1016/j.jbusres.2019.07.039.
TEECE, D. J.; PISANO, G.; SHUEN, A. Dynamic capabilities and strategic management. Strategic Management Journal, v. 18, n. 7, p. 509-533, 1997. DOI: https://doi.org/10.1002/(SICI)1097-0266(199708)18:7<509::AID-SMJ882>3.0.CO;2-Z.
VERHOEF, P. C. et al. Digital transformation: A multidisciplinary reflection and research agenda. Journal of Business Research, v. 122, p. 889-901, 2021. DOI: https://doi.org/10.1016/j.jbusres.2019.09.022.
WESTERMAN, G.; BONNET, D.; MCAFEE, A. Leading Digital: Turning Technology into Business Transformation. Boston: Harvard Business Review Press, 2014.