Background: Artificial intelligence (AI) has emerged as a transformative technology, attracting significant attention from businesses aiming to enhance their competitive advantage through integration into various processes. Despite the potential benefits, many AI projects within organizations fail, primarily due to a lack of fundamental research and understanding during the pre-development phase. This study addresses this gap by investigating the factors influencing the success of AI implementation during this preliminary stage. Theoretical Framework: The research is grounded in the Technological-Organizational-Environmental (TOE) framework, which provides a lens to analyze the technological, organizational, and environmental factors affecting AI integration. The TOE framework has been utilized in various prior research to understand the adoption of various technologies, including AI. Methodology: A case study approach was employed, focusing on a large multinational case company within the manufacturing sector. Data was collected through semi-structured interviews, direct observations, and archival records. The thematic analysis method was used to identify and categorize the relevant factors within the TOE dimensions. Empirical findings: The study's empirical data highlights factors within the TOE framework. Technological factors such as data quality, privacy, and scalability were found to be pivotal. Organizational factors included top management support, digital skills, and human acceptance. Environmental factors encompassed stakeholder buy-in, government incentives, and geographical context. Analysis: Two new factors were identified during the analysis with a mix of extended and confirmed factors. Literature gaps could be identified and signs of interrelation between the dimensions emerged. Discussion: The discussion emphasizes the interplay between the dimensions and their impact on AI integration during the pre-development phase. The rather stable technological dimension and the complex organizational dimension emerged as crucial determinants, while environmental factors provided the necessary external support and context for successful integration. Implications: The findings highlight the importance of a strategic and well-planned approach to AI integration in manufacturing. By addressing the identified factors, organizations can better align their AI strategies with their unique contexts and needs, ensuring smoother integration and greater overall success. Limitations: The study is limited to a single case. Future research should consider a broader range of cases and industries to validate and extend the applicability of the results.