This study examines the use of artificial intelligence (AI) in organizational decision-making processes (DMPs) related to investments in renewable energy sources (RESs). The research addresses the gap between AI’s technological capabilities and its actual application in investment practice. An empirical two-stage survey was conducted in 2025, and a comparative analysis was conducted to assess the stability of attitudes toward AI adoption. The findings indicate a low level of practical implementation of AI tools in investment decision-making, despite a clear perception of their potential usefulness, particularly for risk analysis and improving decision objectivity. Organizations tend to perceive AI primarily as analytical support rather than an autonomous decision-making mechanism. The results also reveal a persistent level of uncertainty and hesitation associated with trust in AI systems. Comparative analysis confirms that these attitudes remain stable across research stages, suggesting structural rather than temporary barriers to adoption. This study demonstrates that limited adoption of AI in renewable energy investment decisions results mainly from organizational readiness and trust-related factors rather than technological constraints. The paper contributes empirical evidence on the behavioral and organizational determinants of AI implementation in the context of sustainable energy transition.
Salwin et al. (Sat,) studied this question.