Received:
2025-09-03 | Accepted:
2026-01-27 | Published:
2026-03-30
Title
Use of artificial intelligence for business decision-making
Abstract
The Decision Model and Notation (DMN) standard provides a unified framework for the representation of business decisions, with decision tables constituting its central construct. Despite the advantages offered by the manual creation of DMN decision tables from textual descriptions in terms of transparency and analysis, the process is time-consuming and prone to errors. This results in a discrepancy between the textual rules and the formal structures required for automation. The proposed framework integrates Large Language Models (LLMs) with Retrieval-Augmented Generation (RAG), facilitating the automated conversion of natural language descriptions into decision tables. The framework utilises a modular pipeline comprising schema injection, retrieval of relevant domain-specific data, prompt composition, generation, and validation. This design guarantees that the resulting decision tables comply with DMN's rigorous XML-based specification while also capturing the contextual meaning of the original rules. A significant challenge in relying exclusively on LLMs is their proclivity to hallucinate or deviate from the required format. The integration of RAG has been demonstrated to mitigate these issues by grounding generation in reliable sources, enhancing both factual accuracy and structural consistency. The experimental results demonstrate that the LLM-RAG configuration significantly enhances the precision and robustness of generated DMN decision models, producing valid and contextually appropriate outputs with minimal human intervention. The advancement of automation in the field of decision modelling has the potential to enhance the efficiency, transparency and adaptability of decision-making processes. The findings indicate a pathway towards scalable, explainable, and verifiable decision automation across diverse business domains.
Keywords
ICT for business, Decision Model and Notation (DMN), decision-making, Large Language Model (LLM), Natural Language Processing (NLP), Retrieval-Augmented Generation (RAG), Artificial Intelligence (AI)
JEL classifications
C02
, C40
, D81
, M19
URI
http://jssidoi.org/jesi/article/1410
DOI
Pages
345-359
Funding
This research was funded by the EU NextGenerationEU through the Recovery and Resilience Plan for Slovakia under the project No. 09I03-03-V01-00078
This is an open access issue and all published articles are licensed under a
Creative Commons Attribution 4.0 International License
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