Abstract
In the era of intensive development of artificial intelligence, its methods are increasingly used to solve complex business problems. One such area is the implementation of ERP systems, which are characterized by high variability and the risk of budget overruns. The aim of this paper is to investigate whether AI methods can be used to forecast deviations between planned and actual budget values. To this end, three predictive models are evaluated based on real-world data from 77 implementations carried out over a 10-year period by a company ERP Serwis sp. z o.o. sp. k. The performance of the models was evaluated using standard regression measures and interpretability analysis. The results indicate a limited but meaningful predictive ability of machine learning models to identify the risk of budget overruns.
Paper Type
Poster
DOI
10.62036/ISD.2026.1
AI for Budget Deviation Prediction in ERP Project Management
In the era of intensive development of artificial intelligence, its methods are increasingly used to solve complex business problems. One such area is the implementation of ERP systems, which are characterized by high variability and the risk of budget overruns. The aim of this paper is to investigate whether AI methods can be used to forecast deviations between planned and actual budget values. To this end, three predictive models are evaluated based on real-world data from 77 implementations carried out over a 10-year period by a company ERP Serwis sp. z o.o. sp. k. The performance of the models was evaluated using standard regression measures and interpretability analysis. The results indicate a limited but meaningful predictive ability of machine learning models to identify the risk of budget overruns.
Recommended Citation
Zalasiński, M., Scherer, M. & Zalasińska, J.(2026). AI for Budget Deviation Prediction in ERP Project Management. In M. Valenta, B. Mannová, R. Pergl, A. Przybylek, M. Lang, H. Linger, C. Schneider, N. Iivari, & E. Insfran (Eds.), Making ISD Sustainable: Reloaded with AI and Automation (ISD2026 Proceedings). Prague, Czech Republic: Czech Technical University in Prague. ISBN: 978-80-01-07585-2. https://doi.org/10.62036/ISD.2026.1