[2606.00051] Business Utility of Large Language Models as Exploratory Data Analysis Agents
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Computer Science > Computers and Society
arXiv:2606.00051 (cs)
[Submitted on 8 May 2026]
Title:Business Utility of Large Language Models as Exploratory Data Analysis Agents
Authors:Rafał Łabędzki, Patryk Miziuła, Hubert Rutkowski, Szymon Betlewski, Cezary Depta, Szymon Janowski, Jarosław Kochanowicz, Jan Kanty Milczek<br>View a PDF of the paper titled Business Utility of Large Language Models as Exploratory Data Analysis Agents, by Rafa{\l} {\L}ab\k{e}dzki and 7 other authors
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Abstract:Large Language Models (LLMs) are increasingly used in analytical workflows, but their suitability as exploratory data analysis (EDA) agents in business settings remains uncertain. In practice, a deployable EDA agent must provide not only useful average performance but also sufficient repeatability to support trust in its outputs. We evaluate this requirement in a controlled, business-relevant benchmark built on an agent-based supply chain simulation. The task is to identify supplier-product combinations responsible for low quality and downstream sales loss by reasoning from indirect operational traces rather than from explicit labels. Fifteen model-variant configurations from eight model families were evaluated under four experimental conditions that varied data representation, prompt clarity, and signal strength, with five trajectories per condition. Outputs were scored against deterministic ground truth using the Jaccard index and assessed through a framework that combines mean score (ms), coefficient of variation (CV), exploratory cross-condition significance tests, and Business utility, a risk-adjusted metric that we propose to summarise quality and repeatability in a single operational measure. The results show that most configurations are not reliable enough for autonomous EDA use, even when their average scores appear acceptable. GPT-5.4 with extra-high reasoning effort achieved the strongest overall profile, with an experiment-averaged ms of 0.8748 and an experiment-averaged Business utility of 0.6952, while the next-best configurations lost substantially more utility after variability discounting. Our findings suggest that evaluation of EDA agents should treat average quality, repeatability, and condition sensitivity as complementary dimensions of operational trustworthiness.
Subjects:
Computers and Society (cs.CY); Artificial Intelligence (cs.AI)
Cite as:<br>arXiv:2606.00051 [cs.CY]
(or<br>arXiv:2606.00051v1 [cs.CY] for this version)
https://doi.org/10.48550/arXiv.2606.00051
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arXiv-issued DOI via DataCite
Submission history<br>From: Rafał Łabędzki [view email]<br>[v1]<br>Fri, 8 May 2026 08:23:10 UTC (695 KB)
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