Identification and Analysis of Artificial Intelligence Implementation Risks in Improving Financial Performance Using an Interactions-Based Approach (Case Study: Amir Kabir Petrochemical Company)
Keywords:
Artificial intelligence risks, financial performance, petrochemical industryAbstract
The aim of this study is to identify and analyze the risks of implementing artificial intelligence in improving financial performance using an interactions-based approach in Amir Kabir Petrochemical Company. This study employed a mixed-method research design consisting of qualitative and quantitative phases. In the qualitative phase, Systematic Content Screening (SCS) based on Critical Appraisal Skills Program criteria was used to identify AI implementation risks. Subsequently, a two-round Delphi technique was conducted to validate and reach consensus on the identified criteria using mean scores and agreement coefficients. In the quantitative phase, an interpretive structural modeling and cross-impact analysis approach was applied. Data were collected using pairwise comparison matrix checklists from 26 financial, accounting, and IT managers of the company, leading to the construction of self-interaction and reachability matrices for analyzing interrelationships among variables. The results confirmed ten key dimensions of AI implementation risks in financial performance improvement. Delphi analysis indicated a strong level of expert consensus. Cross-impact analysis revealed that managerial risk is the most critical and influential dimension, playing a central role in the network of relationships among risks. Systemic and governance risks ranked next, showing significant influence on other dimensions, while supply chain and operational risks demonstrated comparatively lower levels of influence. The findings indicate that effective management of AI-related risks, particularly at the managerial level, plays a crucial role in enhancing financial performance and provides a strategic foundation for technological and financial decision-making in petrochemical firms.
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Copyright (c) 2025 Elnaz Ghaseminezhad (Author); Hossein Jannat Makan (Corresponding author)
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