Unleashing the Power of Decision Science

Over the past five years there has been a massive growth within retailers looking to use Machine Learning and Artificial Intelligence to improve performance, simplify the customer journey and increase innovation. But should we always trust the algorithms and are they even required? Is AI really the next level of retail optimisation, or simply another solution still searching for the problem it’s meant to fix? As the UK opened up after COVID I found myself once again having to defend my preference for explainable machine learning over artificial intelligence within retail. It was a strange conversation (they always are) with a sales rep for a supplier of AI tools which would, they assured me (they always do), increase my speed to production of forecasting and predictive models more accurate than I’d be able to create myself. I asked the usual questions (how does it work, what checks are in place, and is the output explainable) and was a bit surprised by the response - “why do you need to know how the models work or what drives them? If they work, they work don’t they?” I don’t know what concerned me more - the blind faith in processes without any real understanding of how the business I work in operated, or the matter of fact way it was presented; resonating with many pitches I’ve sat through and how this has permeated through many non-analytical (and even analytical) business functions. For despite their apparent sophistication Machine Learning and Artificial Intelligence are not the solution to every problem on their own.. We have identified a set of questions to ask those building the models to make it easy to both sense check how they work, and to make sure they are built at the right level for what’s needed.