Nine out of ten retailers worldwide are already testing or using AI - but only five percent are achieving a clearly measurable ROI. The gap between pilot project and productive added value is real. Current studies and projects from the retail sector show how it can be closed.
According to a recent McKinsey study (2025), 89% of retail and CPG companies worldwide are actively using or testing AI applications. At the same time, BCG's study "Closing the AI Impact Gap" (2025) shows that around 60 percent of companies are not generating any measurable added value despite investments. Only five percent create substantial value across the board.
The picture is similar in Germany: according to Bitkom (February 2026), 36% of German companies use AI - almost twice as many as in the previous year. In retail, 25% are already using AI in customer service, 18% for personalized recommendations and 18% in inventory management. A further 47% are planning or discussing the introduction.
The paradox: the technology is available, the use cases are known - but only a few are making the transition from pilot to productive value creation. How prodot is using AI productively in retail
AI in retail is not an abstract concept. There are four areas in which its use is measurable and quickly effective:
1. demand forecasting and stock optimization
AI models analyze historical sales data, seasonality, local events and weather patterns to accurately predict demand at the store and SKU level. According to recent studies, hybrid AI models improve forecasting accuracy by an average of 23% compared to traditional methods - with a corresponding reduction in excess stock and shortages. One multi-channel retailer improved its forecast accuracy from 67 to 91 percent at SKU/location/day level. The average reduction in storage costs is 15 to 25 percent.
2. personalization and next-best-offer
Personalized product recommendations based on purchase history and browsing behavior demonstrably increase the average order value. McKinsey puts the increase in sales through AI personalization at 10 to 15 percent. Sessions in which customers interact with recommendations show an average order value that is around 3.7 times higher. Amazon generates 35% of its purchases via personalized recommendations - a benchmark that is achievable for any retailer with the appropriate database.
3. dynamic pricing
AI-supported pricing engines react in real time to stock levels, demand, competitor prices and the remaining season. Retailers with AI pricing report profit increases of between 5 and 25 percent because markdowns are controlled in a targeted manner instead of being applied across the board.
4 AI in customer service
According to McKinsey (2025), AI-supported customer service systems deliver an average return of USD 3.50 for every USD 1 invested. Routine inquiries are processed automatically and service teams concentrate on complex cases. At the same time, availability increases - around the clock, without waiting times.
The figures speak for themselves. McKinsey puts the incremental sales effect of GenAI-supported decision-making systems at up to 5 percent with an EBIT margin improvement of 0.2 to 0.4 percentage points. BCG shows that AI pioneers achieve an ROI 2.1 times higher than the average - and that they concentrate on an average of 3.5 instead of 6.1 use cases. Focus beats breadth.
The global market for AI in retail is growing accordingly: it is estimated to be worth around USD 14.4 billion in 2025 and is set to grow to over USD 123 billion by 2035 - an annual growth rate of around 24% (Research Nester, 2025). According to BCG (2026), four out of five CEOs are more optimistic about AI ROI than they were a year earlier.
Particularly striking: According to BCG (Retail Rewired, 2026), traffic to retail websites via GenAI browsers and AI chat services increased by 4,700 percent in the same period. Those who are not visible here will lose customers in future before they even visit their own website.
According to recent studies, the most common hurdles are not the technology, but structural problems:
According to Gartner, trust in AI systems is the key differentiating factor between success and failure. Retailers who use transparent, explainable AI systems and actively involve their teams are proven to achieve better results.
BCG has investigated what distinguishes the five percent of companies that realize substantial AI value. The result is clear:
The technology is ready. The use cases are tried and tested. And the gap between companies that are using AI productively and those that are still experimenting is growing every day.
91% of retail IT leaders worldwide prioritize AI as the top technology for 2026 (Gartner, 2025). The crucial question is no longer whether AI is relevant in retail - but how to get started without falling into the typical traps.
What is needed: a reliable database, clearly defined use cases with measurable added value and a partner who has both the technical depth and understanding of retail.
Would you like to know which AI use cases are actually feasible for your retail business - and what a realistic pilot would cost? Get in touch with us.