Abstract:

The research is focused on how companies manage and should manage implementation of artificial intelligence (AI) / machine learning (ML) based predictive analytics solutions. What are the preconditions of successful implementations and lessons learned from failed cases. Research methods used for this paper are literature review, experiments, qualitative and quantitative surveys. As a result, Authors suggests a model / framework to follow to manage a successful implementation of a predictive analytics solution. The main conclusion is that new IT initiatives (predictive analytics to some extent is an IT project) should undergo classical change management steps similarly as any other business process change initiative. Authors recommend the companies to devote enough time, human and other necessary resources for preparation and execution of predictive analytics solutions` implementation. The goal should be clearly defined and measurable, communication, business process change should be planned carefully. Training is another important step as well.