Use of Artificial Intelligence in the Automation ofOrganizational Processes
Keywords:
Inteligencia Artificial, IA, Automatización de procesos, Eficiencia operativa, Aprendizaje automático, Transformación digital, Análisis masivo de datosAbstract
This work investigated the use of artificial intelligence (AI) in the automation of organizational processes, identifying its
main benefits and challenges. The review of related work confirmed that technologies such as machine learning and deep
learning are key to digital transformation, as they optimize operational efficiency and enable the handling of large volumes
of data. Methodologically, a mixed, documentary, and descriptive approach was adopted, combining qualitative literature
analysis with a theoretical simulation of the improvements. Through a theoretical simulation based on the synthesis of the
literature, the projected results suggest notable increases in performance, with an estimated improvement in operational
efficiency between 50% and 80% and a reduction in human errors from 70% to 90%. The conducted analysis indicates
that AI has the potential to transform information processing from limited to massive and real-time, facilitating strategic
decision-making and the optimization of specific processes in areas such as Human Resources and logistics. The study
theoretically supports the role of AI as a pillar for competitiveness and the freeing up of resources, highlighting as future
work the need to perform an empirical validation of the model and address the ethical challenges of transparency and bias
mitigation.
Downloads
References
S. Russell y P. Norvig, Artificial Intelligence: A Modern Approach, 4th ed. Hoboken, NJ, USA: Pearson, 2020.
F. Chollet, Deep Learning with Python, 2nd ed. Shelter Island, NY, USA: Manning Publications, 2021.
M. Iansiti y K. R. Lakhani, Competing in the Age of AI: Strategy and Leadership When Algorithms and Networks Run the World. Boston, MA, USA: Harvard Business Review Press, 2020.
I. H. Sarker, "Machine learning: Algorithms, real-world applications and research directions," SN Computer Science, vol. 2, no. 3, p. 160, 2021.
M. Mitchell, Artificial Intelligence: A Guide for Thinking Humans. London, UK: Pelican Books, 2020.
A. Géron, Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow, 3rd ed. Sebastopol, CA, USA: O'Reilly Media, 2022.
R. Sharma et al., "Artificial intelligence in business: State of the art and future research agenda," Journal of Business Research, vol. 129, pp. 84-93, 2021.
T. H. Davenport, The AI Advantage: How to Put the Artificial Intelligence Revolution to Work. Cambridge, MA, USA: MIT Press, 2020.
A. Agrawal, J. Gans y A. Goldfarb, Power and Prediction: The Disruptive Economics of Artificial Intelligence. Boston, MA, USA: Harvard Business Review Press, 2022.
G. James, D. Witten, T. Hastie y R. Tibshirani, An Introduction to Statistical Learning, 2nd ed. New York, NY, USA: Springer, 2021.
T. Brown et al., "Language models are few-shot learners," en Advances in Neural Information Processing Systems, vol. 33, pp. 1877-1901, 2020.
C. C. Aggarwal, Neural Networks and Deep Learning: A Textbook, 2nd ed. Cham, Switzerland: Springer, 2023.
I. H. Sarker, "AI-based modeling: Techniques, applications and research issues towards automation, intelligent and smart systems," SN Computer Science, vol. 3, no. 2, p. 158, 2022.
M. Haenlein y A. Kaplan, "A brief history of artificial intelligence: On the past, present, and future of artificial intelligence," California Management Review, vol. 63, no. 1, pp. 5-14, 2020.
T. Hagendorff, "The ethics of AI ethics: An evaluation of guidelines," Minds and Machines, vol. 30, no. 1, pp. 99-120, 2020.
Y. Bengio, Y. LeCun y G. Hinton, "Deep learning for AI," Communications of the ACM, vol. 64, no. 7, pp. 58-65, 2021.
R. Bommasani et al., "On the opportunities and risks of foundation models", 2021.
S. Russell, Human Compatible: Artificial Intelligence and the Problem of Control. New York, NY, USA: Penguin Books, 2020.
J. Han, M. Kamber y J. Pei, Data Mining: Concepts and Techniques, 4th ed. Burlington, MA, USA: Morgan Kaufmann, 2022.
F. Provost y T. Fawcett, Data Science for Business: What You Need to Know about Data Mining and Data-Analytic Thinking, Updated ed. Sebastopol, CA, USA: O'Reilly Media, 2021.
Downloads
Published
Issue
Section
License
Copyright (c) 2026 International Journal of Information Science and Technological Applications-UAS IJISTA
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
