Realising the promises of artificial intelligence in manufacturing by enhancing CRISP-DM

To support manufacturing firms in realising the value of Artificial Intelligence (AI), we embarked on a six-year process research and practice to enhance popular widely used CRISP-DM methodology. We extend into continuous, active, iterative life-cycle AI solutions by adding phase ‘Operation Maintenance’ as well embedding task-based framework for linking tasks skills. Our key findings relate difficult trade-offs hidden costs operating maintaining managing drift, ensuring presence domain, data science, engineering competence throughout phases. Further, show how is an essential but often neglected part workflow, provide novel insights trajectory involvement three competences, illustrate enhanced methodology can be management tool projects.