Retail has undoubtedly recognized the great potential of artificial intelligence (AI), but targeted investments are still required to introduce AI-controlled solutions. This is the result of the study AI in Store by EHI and Microsoft.
Dynamic pricing and predictive analytics are core areas of application for AI but are rated differently by retailers: not every industry has simply the necessary requirements for dynamic pricing; predictive analytics, however, will be the most important area for AI applications in the coming years, according to two selected results of the survey. Smart applications that translate large and diverse amounts of data into real customer insights are particularly in demand.
Retailers see great potential in the AI-controlled analysis of large amounts of data. All those surveyed believe that predictive analytics - the forward-looking analysis of sales and inventory changes and the resulting effects on the distribution and placement of goods - have been comparatively well positioned through investments in recent years. AI-based solutions in this area are confirmed to have a high development potential for the coming years, because much larger and diverse amounts of data, that have so far often been disregarded, can now be used for analyzes. For fashion retailers, for example, the distribution of goods is often more important than the predictive analysis of sales, since large parts of the assortment are not reordered. AI-based solutions can also be used in this context. All of this will result in substantial investments.
There is a very different picture among retailers about dynamic pricing. Some of them are very open to dynamic, AI-controlled pricing, since they see it as imperative to be able to face the online competition. However, not all assortments of goods are equally suitable for dynamic pricing. For example, digital price labeling, a prerequisite for dynamic pricing, is not used in fashion retail and some managers react negatively because they discourage the risk of possibly unsettling or upsetting customers through intraday price changes.