Innovation in artificial intelligence applied to predictive software for business decision-making.
Keywords:
artificial intelligence, predictive software, decision-making, business optimization, digital transformation.Abstract
Objective: The development of predictive software using artificial intelligence (AI) has brought about a significant shift in business environments: procedures have been refined, operating costs minimized, and decision-making processes made more precise. Methodology: This review examines 38 studies published in prestigious academic databases between 2020 and 2025, analyzing how the use of predictive models and machine learning algorithms influences contemporary business management. Results: Findings indicate that integrating intelligent systems helps forecast market behavior, fine-tune supply chains, identify risks, and create personalized, data-driven strategies. Furthermore, the use of predictive software is shown to enhance organizational competitiveness by increasing efficiency and adaptability in changing contexts. Discussion: However, challenges regarding data privacy, algorithmic transparency, and technological dependency are also identified. Conclusion: AI-driven predictive software innovation is a key tool for digital transformation and business sustainability, provided its implementation is accompanied by appropriate and ethical regulatory policies. Contribution: Investment in digital infrastructure, continuous training, and data management are established as fundamental pillars for ensuring the effective, secure, and sustainable adoption of these technological innovations within the global business landscape.
Downloads
References
Agility, P. A.-T. (2023). Predictive Analytics and Machine Learning for Real-Time Supply Chain Risk Mitigation and Agility. MDPI, 15(20), 15088. https://doi.org/https://doi.org/10.3390/su152015088
Akour, M. A. (2025). AI-Driven Innovations in Software Engineering: A Review of Current Practices and Future Directions. MDPI, 15, 1344. https://doi.org/10.3390/app15031344
Arsha, P. P. (2024). Artificial Intelligence and Business Models from the Perspective of Innovation and Operational Efficiency of Companies: Systematic Literature Review. International Journal of Research and Innovation in Social Science (IJRISS). https://doi.org/https://dx.doi.org/10.47772/IJRISS.2024.8110147
Catalina Gomez, S. M.-M. (2023). Human-AI collaboration is not very collaborative yet: A taxonomy of interaction patterns in AI-assisted decision making from a systematic review. arXiv, 25. https://doi.org/https://arxiv.org/abs/2310.19778
Chebrolu, S. K. (2025). AI-powered business intelligence: a systematic literature review on the future of decision-making in enterprises. American Journal of Scholarly Research and Innovation, 4(1), 33-62. https://doi.org/https://doi.org/10.63125/gq69nv41
Chenlei Ye. Zongxue Xu, W. L. (2025). Capturing Urban Pluvial River Flooding Features Based on the Fusion of Physically Based and Data-Driven Approaches. MDPI, 17(6), 2524. https://doi.org/https://doi.org/10.3390/su17062524
Harfizar, Wicaksono, M. W., Hakim, M. B., Wijaya, F., Saleh, T., & Sana, E. (2024). Análisis de la influencia de la inteligencia artificial en la innovación digital: un enfoque SmartPLS. IAIC Transctions on Sustainable Digital Innovation, 5(2), 108 - 116. https://doi.org/https://doi.org/10.34306/itsdi.v5i2.659
Hettikankanamage, N., Shafiabady, N., Chatteur, F., MX Wu, R., Ud Din, F., & Zhou, J. (2025). Inteligencia Artificial Explicable (XAI): Una revisión sistemática para desvelar los modelos de caja negra y su relevancia para la imagen y la detección biomédicas. MDPI, 25(21), 6649 https://doi.org/https://doi.org/10.3390/s25216649
Italo Paul Yaranga Vite, L. W. (2025). Integración de la inteligencia artificial con big data para la toma de decisiones en las empresas: un estudio bibliométrico. Scielo, 5(4). https://doi.org/https://doi.org/10.5281/zenodo.14783686
Jaakko Sauvola, S. T. (2024). Future of software development with generative AI. Springer Nature Link, 31, 26. https://doi.org/10.1007/s10515-024-00426-z
Jorzik, P., Sascha Kraus, Sascha P. Klein, & Dominik K. Kanbach. (2024). AI-driven business model innovation: A systematic review and research agenda. ELSEVIER, 182. https://doi.org/https://doi.org/10.1016/j.jbusres.2024.114764
Kirenia Maldonado Zuñiga, R. V. (2025). Inteligencia artificial aplicada en la optimización de procesos para toma de decisiones. SCielo, 18(1), Kimberly Lisseth Alcivar Loor. http://scielo.sld.cu/pdf/sc/v18n1/2306-2495-sc-18-01-305.pdf
Lakpriya Udayanga Gunasena Ekanayaka Gunasinghalge, A. A. (2025). Artificial intelligence for energy optimization in smart buildings: A systematic review and meta-analysis. SpringerOpen, 8, 135. https://doi.org/https://energyinformatics.springeropen.com/articles/10.1186/s42162-025-00592-8?utm_source
László Vancsura, T. T. (2025). Navigating AI-Driven Financial Forecasting: A Systematic Review of Current Status and Critical Research Gaps. MDPI, 7(3), 36. https://doi.org/https://doi.org/10.3390/forecast7030036
László Vancsura, T. T. (2025). Navigating AI-Driven Financial Forecasting: A Systematic Review of Current Status and Critical Research Gaps. MDPI, 7(3), 36. https://doi.org/https://doi.org/10.3390/forecast7030036
López-Solís, O. L.-J.-J.-S.-J.-V. (2025). Effect of Generative Artificial Intelligence on Strategic Decision-Making in Entrepreneurial Business Initiatives: A Systematic Literature Review. MDPI, 15(2), 66. https://doi.org/https://doi.org/10.3390/admsci15020066
MK, D. P. (2025). Artificial Intelligence in Business Decision-Making: A Comprehensive Review. International Research Journal on Advanced Engineering and Management, 3(5). https://doi.org/https://doi.org/10.47392/IRJAEM.2025.0323
Moloney, G. P. (2024). Responsible AI-Based Business Process Management and Improvement. Springer Nature Link, 3, 23. https://doi.org/10.1007/s44206-024-00105-2
Nadeesha Hettikankanamage, N. S. (2025). eXplainable Artificial Intelligence (XAI): A Systematic Review for Unveiling the Black Box Models and Their Relevance to Biomedical Imaging and Sensing. MDPI, 25(21), 6649. https://doi.org/https://doi.org/10.3390/s25216649
Nicky Gilang Wicaksono, B. G. (2024). Systematic literature review: The implementation of artificial intelligence (IA) sophistication in facilitating the employee recruitment process. Heliyon, 13, 2674–2679. https://doi.org/https://doi.org/10.30574/ijsra.2024.13.1.1952
Ortiz, R. M. (2025). The Impacts of Artificial Intelligence on Business Innovation: A Comprehensive Review of Applications, Organizational Challenges, and Ethical Considerations. MDPI, 13(4), 264. https://doi.org/https://doi.org/10.3390/systems13040264
Pablo Olivares, F. R.-R. (2025). Inteligencia artificial como servicio: Potenciando la innovación y eficiencia en la industria y las metodologías ágiles. Scielo, 32, 32. https://doi.org/http://dx.doi.org/10.4067/s0718-33052024000100234
Parikh, N. A. (2023). Empowering Business Transformation: The Positive Impact and Ethical Considerations of Generative AI in Software Product Management -- A Systematic Literature Review. A Systematic Literature Review - arXiv, 24, 24. https://doi.org/https://arxiv.org/abs/2306.04605
Poliana Gomes, L. V. (2022). Artificial Intelligence-Based Methods for Business Processes: A Systematic Literature Review. MDPI, 12(5), 2314. https://doi.org/https://doi.org/10.3390/app12052314
Saber, A. M., Youssef, A., Svetinovic, D., Zeineldin, H., & El-Saadany, E. F. (2024). Unmasking Covert Intrusions: Detection of Fault-Masking Cyberattacks on Differential Protection Systems. IEEE Access, 54(12), 7683 - 7696. https://doi.org/10.1109/TSMC.2024.3456810
Sanjai Vudugula, S. K. (2023). Integrating artificial intelligence in strategic business decision-making: a systematic review of predictive models. International Journal of Scientific Interdisciplinary Research, 4(1), 15. https://doi.org/https://doi.org/10.63125/s5skge53
Schmitt, M. (2023). Automated machine learning: AI-driven decision making in business analytics. ELSEVIER, 18. https://doi.org/https://doi.org/10.1016/j.iswa.2023.200188
Shakev, S. S. (2023). Artificial Intelligence for Management Information Systems: Opportunities, Challenges, and Future Directions. MDPI, 16(8), 357. https://doi.org/https://doi.org/10.3390/a16080357
Shiyu Liu, O. L. (2023). A Review on Business Analytics: Definitions, Techniques, Applications and Challenges. MDPI, 11(4), 899. https://doi.org/https://doi.org/10.3390/math11040899
Sugandha Agarwal, Q. L. (2025). How does artificial intelligence shape the productivity and quality of research in business studies? A systematic literature review and future research framework. Technological Forecasting and Social Change,, 6, 718. https://doi.org/10.1007/s43621-025-01480-7
Yashashree Mahale, S. K. (2025). A comprehensive review on artificial intelligence driven predictive maintenance in vehicles: technologies, challenges and future research directions. Springer Nature, 7, 243. https://doi.org/10.1007/s42452-025-06681-3
Zschech, C. W. (2025). Prescriptive analytics systems revised: a systematic literature review from an information systems perspective. Springer Nature Link, 23, 279-353. https://doi.org/10.1007/s10257-024-00688-w
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2026 Luis Orlando Albarracín Zambrano, Damarys Vanessa Briones Muñoz, Luis Javier Molina Chalacan, Enrique Villalta Jadan
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
