Objective: Despite the increasing use of artificial intelligence in organizations, many projects implementing this technology do not achieve the expected results. This has highlighted the need to identify and prioritize key success factors in implementing artificial intelligence. The present study aimed to identify, finalize, and prioritize the drivers that are effective in the successful implementation of artificial intelligence in organizations.
Methodology: This study was conducted with a mixed exploratory approach in three stages. In the first stage, using qualitative content analysis and three-stage coding, the drivers that are effective in implementing artificial intelligence were extracted from the research literature. In the second stage, the fuzzy Delphi method was used to screen and achieve expert consensus. In the third stage, the confirmed drivers were prioritized using the Mabak multi-criteria decision-making method.
Findings: The results of the qualitative content analysis led to the extraction of 96 open codes, 20 axial codes, and 7 selective codes. In the Fuzzy Delphi stage, out of the 20 identified drivers, 10 were approved by the experts. The results of the MABAC prioritization showed that “effective system design” (0.155), “technical infrastructure and capabilities” (0.137), “management leadership and support” (0.079), and “technical skills and competencies” (0.074) were the most important drivers for implementing AI in organizations. In contrast, “ethics and organizational culture” received the lowest priority.