While the proliferation of AI technologies offers opportunities for the workplace and its processes, their implementation in business effectively is still a challenge. Today, companies require strategic guidance in their AI management. Accordingly, there is a need for more research on the topic with a holistic approach including governance of data. Considering the challenges of the private sector and the gap in the IS research, this thesis focuses on the barriers to implementing AI in the private sector. It specifically assesses the sociotechnical mechanisms for AI evolution in the case of the agricultural machinery industry. The conducted case study suggests an overall approach including data governance for AI implementation and an alignment between the digital, it, and business strategies. Based on the research findings, this study suggests a model for AI management with three parts: The opportunities and the new data generation to realize these opportunities lie on the benefit side of the digital transformation while the sociotechnical mechanisms to tackle the barriers stand at the core. By introducing a model for AI management, the thesis offers a roadmap for the case company while bringing a new perspective to the literature and further research.