Contabilidad Predictiva e Inteligencia Gerencial para Decisiones Empresariales bajo Incertidumbre
DOI:
https://doi.org/10.53485/rgn.v9i3.764Palabras clave:
contabilidad predictiva, inteligencia gerencial, analítica de datos, toma de decisiones, gestión empresarial.Resumen
Objetivo: analizar las contribuciones, aplicaciones, limitaciones y perspectivas de la contabilidad predictiva integrada en sistemas de inteligencia gerencial para fortalecer la toma de decisiones empresariales bajo escenarios de incertidumbre. Metodología: se desarrolló una investigación cualitativa, documental y bibliográfica, con alcance descriptivo, analítico y reflexivo, diseño no experimental y procedimiento de revisión integrativa de literatura científica e institucional publicada entre 2016 y 2026. La búsqueda consideró bases académicas y organismos especializados vinculados con contabilidad, analítica, inteligencia artificial, gestión de riesgos y dirección empresarial. Resultados: la evidencia revisada muestra coincidencia respecto de la capacidad de la analítica avanzada para ampliar el valor informativo de los registros históricos, anticipar presiones de liquidez, estimar ventas y costos, identificar anomalías y comparar escenarios. También se reconocen diferencias sobre el alcance de la automatización, la explicabilidad de los modelos, la gobernanza de datos y la responsabilidad profesional ante sesgos, errores o cambios estructurales. Conclusiones: la integración entre contabilidad predictiva e inteligencia gerencial mejora la preparación organizacional cuando los datos son consistentes, los supuestos permanecen visibles y los resultados se someten a validación, supervisión humana y retroalimentación continua. Este enfoque no sustituye al profesional contable, sino que amplía su participación en la interpretación, comunicación de riesgos y asesoramiento estratégico, a la vez exige competencias tecnológicas, criterio ético y mecanismos de control capaces de preservar trazabilidad, transparencia y responsabilidad en las decisiones. Asimismo, su adopción debe iniciarse mediante proyectos delimitados, indicadores y protocolos de revisión los cuales permitan comprobar utilidad, estabilidad y consecuencias antes de ampliar el sistema.
Referencias
Appelbaum, D., Kogan, A., Vasarhelyi, M. A., & Yan, Z. (2017). Impact of business analytics and enterprise systems on managerial accounting. International Journal of Accounting Information Systems, 25, 29-44. https://doi.org/10.1016/j.accinf.2017.03.003
Ballou, B., Heitger, D. L., & Stoel, D. (2018). Data-driven decision-making and its impact on accounting undergraduate curriculum. Journal of Accounting Education, 44, 14-24. https://doi.org/10.1016/j.jaccedu.2018.05.003
Bergmann, M., Brück, C., Knauer, T., & Schwering, A. (2020). Digitization of the budgeting process: Determinants of the use of business analytics and its effect on satisfaction with the budgeting process. Journal of Management Control, 31, 25-54. https://doi.org/10.1007/s00187-019-00291-y
IFAC. (2017). Blockchain, AI and accounting. International Federation of Accountants. https://www.ifac.org/knowledge-gateway/discussion/blockchain-ai-and-accounting
IFAC. (2023). AI and intelligent automation: Disrupting business, elevating the work of accounting and finance professionals. International Federation of Accountants. https://www.ifac.org/knowledge-gateway/discussion/ai-intelligent-automation-disrupting-business-elevating-work-accounting-finance-professionals
IFAC. (2024a). Exploring the responsible use of AI in finance and accounting. International Federation of Accountants. https://www.ifac.org/knowledge-gateway/discussion/harnessing-innovation-exploring-responsible-use-ai-finance-and-accounting
IFAC. (2024b). Navigating the Gen AI revolution: Implications for the accounting profession. International Federation of Accountants. https://www.ifac.org/knowledge-gateway/discussion/navigating-gen-ai-revolution-implications-accounting-profession
IFRS Foundation. (2023). IFRS S1 General requirements for disclosure of sustainability-related financial information. https://www.ifrs.org/issued-standards/ifrs-sustainability-standards-navigator/ifrs-s1-general-requirements/
IFRS Foundation. (2024a). Digital financial reporting: Facilitating digital comparability and analysis of financial reports. https://www.ifrs.org/content/dam/ifrs/standards/taxonomy/digital-financial-reporting/digitalreportingarticle-april2024.pdf
IFRS Foundation. (2024b). Using the IFRS digital taxonomies: A guide for regulators implementing the IFRS digital taxonomies in a digital filing system. https://www.ifrs.org/content/dam/ifrs/standards/taxonomy/general-resources/ifrs-taxonomy-regulators-guide.pdf
Issa, H., Sun, T., & Vasarhelyi, M. A. (2016). Research ideas for artificial intelligence in auditing: The formalization of audit and workforce supplementation. Journal of Emerging Technologies in Accounting, 13(2), 1-20. https://doi.org/10.2308/jeta-10511
Jarrahi, M. H. (2018). Artificial intelligence and the future of work: Human-AI symbiosis in organizational decision making. Business Horizons, 61(4), 577-586. https://doi.org/10.1016/j.bushor.2018.03.007
Knudsen, D. R. (2020). Elusive boundaries, power relations, and knowledge production: A systematic review of the literature on digitalization in accounting. International Journal of Accounting Information Systems, 36, 100441. https://doi.org/10.1016/j.accinf.2019.100441
Kokina, J., & Davenport, T. H. (2017). The emergence of artificial intelligence: How automation is changing auditing. Journal of Emerging Technologies in Accounting, 14(1), 115-122. https://doi.org/10.2308/jeta-51730
Kurpiela, S., & Teuteberg, F. (2024). Linking business analytics affordances to corporate strategic planning and decision making outcomes. Information Systems and e-Business Management, 22(1), 33-60. https://doi.org/10.1007/s10257-023-00661-z
Losbichler, H., & Lehner, O. M. (2021). Limits of artificial intelligence in controlling and the ways forward: A call for future accounting research. Journal of Applied Accounting Research, 22(2), 365-382. https://doi.org/10.1108/JAAR-10-2020-0207
Moll, J., & Yigitbasioglu, O. (2019). The role of internet-related technologies in shaping the work of accountants: New directions for accounting research. The British Accounting Review, 51(6), 100833. https://doi.org/10.1016/j.bar.2019.04.002
Munoko, I., Brown-Liburd, H. L., & Vasarhelyi, M. A. (2020). The ethical implications of using artificial intelligence in auditing. Journal of Business Ethics, 167(2), 209-234. https://doi.org/10.1007/s10551-019-04407-1
Nielsen, S. (2022). Management accounting and the concepts of exploratory data analysis and unsupervised machine learning: A literature study and future directions. Journal of Accounting & Organizational Change, 18(5), 811-853. https://doi.org/10.1108/JAOC-08-2020-0107
NIST. (2023). Artificial Intelligence Risk Management Framework (AI RMF 1.0). National Institute of Standards and Technology. https://doi.org/10.6028/NIST.AI.100-1
OECD. (2019a). Artificial intelligence in society. OECD Publishing. https://doi.org/10.1787/eedfee77-en
OECD. (2019b). Recommendation of the Council on Artificial Intelligence. https://legalinstruments.oecd.org/en/instruments/OECD-LEGAL-0449
Oesterreich, T. D., & Teuteberg, F. (2019). The role of business analytics in the controllers and management accountants' competence profiles: An exploratory study on individual-level data. Journal of Accounting & Organizational Change, 15(2), 330-356. https://doi.org/10.1108/JAOC-10-2018-0097
Oesterreich, T. D., Teuteberg, F., Bensberg, F., & Buscher, G. (2019). The controlling profession in the digital age: Understanding the impact of digitisation on the controller's job roles, skills and competences. International Journal of Accounting Information Systems, 35, 100432. https://doi.org/10.1016/j.accinf.2019.100432
Pisoni, G., Molnár, B., & Tarcsi, Á. (2024). Knowledge management and data analysis techniques for data-driven financial companies. Journal of the Knowledge Economy, 15(3), 13374-13393. https://doi.org/10.1007/s13132-023-01607-z
Quattrone, P. (2016). Management accounting goes digital: Will the move make it wiser? Management Accounting Research, 31, 118-122. https://doi.org/10.1016/j.mar.2016.01.003
Richins, G., Stapleton, A., Stratopoulos, T. C., & Wong, C. (2017). Big data analytics: Opportunity or threat for the accounting profession? Journal of Information Systems, 31(3), 63-79. https://doi.org/10.2308/isys-51805
Rikhardsson, P., & Yigitbasioglu, O. (2018). Business intelligence and analytics in management accounting research: Status and future focus. International Journal of Accounting Information Systems, 29, 37-58. https://doi.org/10.1016/j.accinf.2018.03.001
Schnegg, M., & Möller, K. (2022). Strategies for data analytics projects in business performance forecasting: A field study. Journal of Management Control, 33, 241-271. https://doi.org/10.1007/s00187-022-00338-7
Shrestha, Y. R., Ben-Menahem, S. M., & von Krogh, G. (2019). Organizational decision-making structures in the age of artificial intelligence. California Management Review, 61(4), 66-83. https://doi.org/10.1177/0008125619862257
Szukits, Á. (2022). The illusion of data-driven decision making: The mediating effect of digital orientation and controllers' added value in explaining organizational implications of advanced analytics. Journal of Management Control, 33, 403-446. https://doi.org/10.1007/s00187-022-00343-w
Wasserbacher, H., & Spindler, M. (2022). Machine learning for financial forecasting, planning and analysis: Recent developments and pitfalls. Digital Finance, 4(1), 63-88. https://doi.org/10.1007/s42521-021-00046-2
World Bank. (2021). World Development Report 2021: Data for better lives. World Bank. https://www.worldbank.org/en/publication/wdr2021
Descargas
Publicado
Número
Sección
Licencia
Derechos de autor © 2026 Angy Carolina Boscán Boscán
Esta obra está bajo una licencia internacional Creative Commons Atribución-NoComercial-CompartirIgual 4.0.
