The use of artificial intelligence in manufacturing holds a multitude of potentials for improving the performance of a company in the dimensions time, quality, and cost. Many companies have recognized these possibilities, but only a few have already integrated this technology into their production. A major reason for this discrepancy is a lack of knowledge about necessary steps to conduct an AI project in order to solve an existing manufacturing problem. In literature, several models exist that provide structure and standards for the process of data mining in industrial applications (e.g. CRISP-DM, SEMMA, KDD). However, these process models have several shortcomings that prevent the effective usage in the manufacturing industry. The following paper addresses these shortcomings and proposes a holistic process model that shall serve as a standard management model for manufacturing companies to successfully introduce and apply AI as a production-related problem-solving tool. All three levels of the process model are presented, namely the strategic, tactical, and operational level. On the strategic level, an existing set of production problems is evaluated and prioritized concerning their feasibility and suitability for the application of AI. In the tactical part of the model, a solution for a selected problem is designed. Therefore, the problem understanding is deepened, infrastructural requirements are identified, and a financial evaluation of the developed solution is performed. The final, operational level focuses on the implementation of the developed solution to a finished AI application by a project team.