Ibrahim KAHYA and Peter MARKOVIC
University of Economics in Bratislava, Slovak Republic
Artificial intelligence (AI) is transforming professional risk management by enabling the processing of vast datasets and structuring complex decision-making environments. However, the successful integration of these systems depends heavily on how effectively human professionals and AI algorithms collaborate in corporate settings. While traditional technology adoption models focus on perceived usefulness and general trust, the specific interplay between user-related demographics, the demand for explainable AI (XAI), and the organizational barriers created by unmet transparency expectations remains insufficiently understood in specialized risk domains.
To address this gap, this study conducts a quantitative survey among N = 71 risk management professionals wich are operating within the Finance/Insurance sector and the Chemical/Pharmaceutical industries across Germany, Austria, and Switzerland. Statistical analyses, including comparative diagnostics and multivariable linear regression modeling, were performed to isolate the key drivers of institutional AI acceptance.
The empirical results reveal that while AI is universally recognized as operationally helpful, but workplace efficiency alone does not automatically translate into institutional acceptance. In the multivariate model, user age was positively associated with AI acceptance with challenging common assumptions about digital familiarity. The transparency gap was negatively associated with AI acceptance, suggesting that unmet explainability expectations may reduce acceptance. No statistically significant differences in AI acceptance were found across the preferred collaboration mechanisms. Ultimately, the study highlights that fostering epistemic trust in human-AI interfaces requires organizations to implement robust, explainable frameworks that make automated outputs professionally defensible and compatible with regulatory requirements.