Introduction: The growing application of artificial intelligence in the renewable energy industry has increased the need to address the organizational, technological, and cultural requirements for the successful implementation of this technology. Companies operating in this field have increasingly relied on AI-based solutions to improve decision-making, forecast energy production and consumption, manage assets, optimize operations, and enhance productivity. However, the effectiveness of artificial intelligence is not limited merely to access to advanced technologies; rather, it depends on organizational readiness for the systematic use of data, the quality of evidence-based decision-making, data governance, data literacy, and the cultural acceptance of data-driven practices. From this perspective, a data-driven culture is one of the key prerequisites for the successful adoption of artificial intelligence in organizations. Nevertheless, integrated models for assessing and improving the maturity of data-driven culture aimed at enhancing AI effectiveness particularly in renewable energy companies have received limited attention. Therefore, the present study aimed to develop a data-driven culture maturity model for enhancing artificial intelligence effectiveness in the renewable energy industry.



Methods: In terms of purpose, this study is applied, and in terms of approach, it follows a sequential exploratory mixed-methods design. In the first phase, a qualitative method was used to identify the dimensions, components, and concepts related to data-driven culture maturity and AI effectiveness. In this phase, a meta-synthesis was conducted based on the seven-step model of Sandelowski and Barroso, and relevant scientific sources were reviewed and analyzed. The extracted data were then coded and categorized using thematic analysis. MAXQDA software was used to organize and analyze the qualitative data. In the second phase, the fuzzy Delphi method was employed to refine, validate, and finalize the extracted components and concepts. The expert panel consisted of academic and executive specialists familiar with the fields of artificial intelligence, data-driven culture, digital transformation, and renewable energy. At this stage, the opinions of 15 experts were collected and analyzed using Excel software.



Finding: The findings showed that the data-driven culture maturity model for enhancing artificial intelligence effectiveness in the renewable energy industry comprises 2 main dimensions, 10 components, and 71 validated concepts.. The two main dimensions of the model are: “Development and implementation of the artificial intelligence ecosystem in renewable energy” and “Organizational maturity and institutionalization of data-driven culture.” The first dimension focuses on the capacities and mechanisms that enable the effective implementation and utilization of artificial intelligence in the context of renewable energy, including technological infrastructure, analytical capabilities, data integration, and the alignment of technology with the organization’s strategic objectives. The second dimension emphasizes cultural, managerial, and organizational readiness for accepting and institutionalizing data-driven practices and includes components such as data governance, data quality, data-driven leadership, data literacy, evidence-based decision-making, change management, and organizational learning.



Conclusions: The results indicate that enhancing artificial intelligence effectiveness in the renewable energy industry depends not only on technological investment but also on the maturity of organizations operating in this industry in the strategic use of data. Accordingly, a data-driven culture acts as a mediating link between AI capabilities and organizational outcomes. The absence of data governance, poor information quality, low data literacy, cultural resistance to evidence-based decision-making, and the lack of data-driven leadership can limit the effectiveness of artificial intelligence. By integrating the two domains of the “AI ecosystem” and “data-driven culture maturity,” the proposed model provides a comprehensive view of the requirements for the successful implementation of artificial intelligence in organizations operating in the renewable energy industry. This model can help managers assess their organization’s data-driven readiness before making extensive investments in AI projects and identify cultural, managerial, and infrastructural gaps.