Artificial intelligence techniques, including machine learning (ML), have shown remarkable test results over the past decade but struggled with the transfer to practical application. The present study applies action research to investigate this last stage of a project to implement an ML algorithm for predicting no-shows at a Danish hospital. We approach the implementation of the no-show algorithm as an innovation process and identify 14 tactics that were employed to provide the innovation necessary at the implementation stage. The tactics span three analytic levels - organization, project, and practice - and alternate between efforts to train the algorithm and to establish trust in its predictions. These efforts are interdependent, highly sociotechnical, and hence blur the boundary between technical development and organizational implementation. They also show the intricacies involved in innovating during ML implementation. Despite sustained support at the organization level, the implementation of the no-show algorithm at the practice level remained unsettled.
