In today’s highly competitive environment, artificial intelligence (AI) has become a major driver of productivity improvement; however, its adoption in the industries of developing countries continues to face significant structural challenges. Drawing on the theoretical lens of strategic control, particularly Simons’ levers of control framework, this study examines the factors influencing AI adoption in Iran’s machine manufacturing industries. The research employs an exploratory–explanatory mixed-methods design. In the qualitative phase, key factors were identified through meta-synthesis and expert interviews. In the quantitative phase, fuzzy DEMATEL was applied with the participation of 12 experts to prioritize the causal relationships among these factors. The findings indicate that AI adoption is fundamentally a strategic rather than merely a technological issue. Top management support, competitive pressure, financial resources, and regulatory requirements emerged as the main causal and driving factors, whereas employee resistance and technical competencies were identified as more affected factors within the causal network. The use of strategic control levers—including belief systems, boundary systems, interactive controls, and diagnostic controls—not only helps manage the risks associated with technological transformation but also ensures alignment between organizational objectives and AI capabilities. This study offers practical guidance for managers and policymakers seeking to enhance competitiveness, strengthen data governance infrastructure, and promote sustainable industrial development.