The Impact of Artificial Intelligence on Financial Decision-Making in Project Management Evidence from Large Infrastructure Projects
Contributors
- Maleeha Tarique MS Project Management Student, Department of Management Sciences, Bahria University, Karachi Campus, Sindh, Pakistan.
- Naveed MS Project Management Scholar, Department of Management Sciences, Bahria University, Karachi Campus, Sindh, Pakistan.
- Rao Muhammad Haris Manager - Business Finance & OMS | Alkaram Textiles, Pakistan.
- Syed Ahsan Hussain MS Project Management Student, Department of Management Sciences, Bahria University, Karachi Campus, Sindh, Pakistan.
Abstract
Large-scale projects involve numerous decisions under uncertainty, including cost estimation, contingency planning, vendor selection, cash flow planning, and managing emerging cost risks. By analysing project data, artificial intelligence (AI) can identify trends and provide predictions and recommendations. This quantitative study investigates relationships among AI utilization, data quality, data integration, AI capability, trust and transparency, financial decision quality, and project financial performance. A 27-item questionnaire and a synthetic cross-sectional dataset of 320 project professionals were developed to demonstrate an analytical workflow using SPSS. Reliability analysis, exploratory factor analysis, Pearson correlation, hierarchical multiple regression, one-way analysis of variance, and bootstrapping mediation analysis were conducted. All six scales demonstrated good internal consistency (alpha = .849–.899). The KMO value was .927, and Bartlett’s test was significant, χ²(351) = 4,986.53, p < .001. Six factors explained 70.1% of the total variance. Data quality (beta = .287), trust and transparency (beta = .266), AI utilization (beta = .254), and AI capability (beta = .204) significantly predicted financial decision quality. The relevant dimensions explained 53.1% of its variance. Financial decision quality strongly predicted project financial performance (beta = .434) and partially mediated the relationship between AI adoption and financial performance. These simulated findings suggest that AI’s financial value depends not only on tool utilization but also on data integration, competent application, explainability, governance, and human accountability. Since the dataset is synthetic, the results illustrate a research design and do not constitute empirical evidence or support generalizable claims.
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Corresponding Author
Maleeha Tarique
MS Project Management Student, Department of Management Sciences, Bahria University, Karachi Campus, Sindh, Pakistan.
Corresponding Author: [email protected]
