Integration of Artificial Intelligence into Modular Business Systems: Automating Workload Allocation and Demand Forecasting to Enhance Enterprise Manageability
Abstract
This article examines the potential applications of artificial intelligence (hereinafter referred to as AI) within modular business systems. Particular attention is given to managerial tasks related to demand forecasting, workload allocation, time-buffer calculation, fulfillment planning, and the analysis of customer inquiries. The aim of the article is to develop an applied model for integrating AI into a modular business system in order to improve planning accuracy and reduce the risk of overloading individual modules. The methodological foundation of the study is based on an analysis of scholarly publications published between 2020 and 2026, as well as modeling and scenario-based evaluation of calculated data. An AI implementation algorithm is proposed, incorporating data auditing, predictive model training, deployment of a recommendation dashboard, and the gradual transfer of selected decision-making functions to an automated allocation module. The findings demonstrate that the use of AI-driven recommendations can reduce planning-related labor costs, improve capacity utilization, and decrease the frequency of missed deadlines. The study concludes that AI strengthens a modular enterprise system through the effective use of data, forecasting capabilities, and decision-making rules.