The use of machine learning (ML) methods is now commonplace in many disciplines and Artificial Intelligence (AI) is on the rise, promising better and smarter solutions to ‘all your problems’. However, despite the hype, there is increasing evidence we have entered the next ‘AI winter’ or the so-called ‘trough of disillusionment’ in the ongoing hype cycle. Benedict Irwin and Matthew Segall at Optibrium Ltd and Alexander Wade of the University of Cambridge explain why there is still a gap in understanding on the route from traditional and well-understood statistical modelling methods to the poorly-defined promises of AI and how the majority of researchers can cross that gap, which is not yet clear.