Multi-Case Study on Challenges and Enablers of AI in Product Development within Swedish Manufacturing Industry

Sammanfattning

Purpose: The purpose of the study is to assess if Swedish manufacturing companies have adopted AI within product development and evaluate what has enabled/challenged their adoption Research questions: “What are the challenges with implementing or attempting to implement AI in product development?” “What are the enablers for implementing AI in product development?” Methodology: The methodology followed a qualitative multi-case study approach using semi-structured interviews with large Swedish manufacturing companies. For data collection and analysis, an abductive approach was used, which combines theory with empirical insights to explore challenges and enablers of AI adoption in product development. Conclusion: To answer RQ1: key challenges include a lack of strategic alignment, organizational silos, incompatibility with legacy systems, fragmented data storage, resistance to change, and difficulty in justifying ROI. These align with existing theoretical models and prior studies on AI implementation barriers. To answer RQ2: main enablers include strong leadership support, a culture of continuous learning, digital infrastructure readiness, and larger organizational size, which allows for risk-taking and innovation investment.