The impact of artificial intelligence on organizational decision-making processes

Baoyu Huang () and Eksiri Niyomsilp ()

Edelweiss Applied Science and Technology, 2025, vol. 9, issue 4, 794-808

Abstract: Using a mixed-methods approach, data was collected through quantitative surveys (N=258) and qualitative interviews with AI practitioners and decision-makers across multiple industries. Findings indicate that AI significantly improves decision efficiency by automating analytical tasks, reducing human cognitive biases, and enabling real-time insights. However, challenges persist, particularly in algorithmic transparency, ethical governance, and compliance with regulatory standards. Key findings reveal that AI integration positively influences decision effectiveness (β=0.156, p=0.031), but human oversight (β=0.381, p<0.001) and regulatory compliance (β=0.314, p<0.001) play crucial mediating roles. Ethical and security challenges necessitate stronger AI governance frameworks, as organizations struggle with bias mitigation, legal accountability, and AI explainability. Industry experts emphasize the need for a hybrid Human-AI collaboration model, ensuring AI remains an augmentation rather than a replacement for human decision-makers. This study contributes to AI governance literature by highlighting the importance of ethical AI deployment, transparent decision systems, and regulatory adherence. Future research should explore AI’s impact in high-risk sectors, develop proactive AI compliance strategies, and examine cross-national AI regulatory frameworks to enhance responsible AI adoption globally.

Keywords: Artificial intelligence; AI-driven decision-making; Ethical AI; Human oversight; Organizational strategy; Regulatory compliance. (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
https://learning-gate.com/index.php/2576-8484/article/view/6081/2197 (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:ajp:edwast:v:9:y:2025:i:4:p:794-808:id:6081

Access Statistics for this article

More articles in Edelweiss Applied Science and Technology from Learning Gate
Bibliographic data for series maintained by Melissa Fernandes ().