Artificial intelligence has been used in retail for years, but generative applications are giving it new strategic significance. The technology itself is not new, but the question of how retailers can embed AI structurally and sustainably in their organizations is.
Weak data integration
What began as a series of experiments is now evolving into concrete change programs at major retailers. Almost all major players are now approaching AI at a strategic level, although its application often remains limited to specific areas. The introduction of European AI legislation is accelerating this discussion, as transparency and traceability are elevating AI projects above the purely technical level. A survey of 38 IT managers in the retail sector conducted by the EHI Retail Institute for the German retail trade fair EuroShop shows that this shift is well underway, but that fundamental obstacles remain.
In practice, AI appears to be less of an IT issue than an organization-wide change. This is particularly evident in the way data is managed. Despite the abundance of available data, 67 percent of those responsible rate their own data integration as weak or rather weak, while only 13 percent rate it as good. Data is spread across different systems, has quality issues, and often lacks clear ownership.
Addressing barriers
These structural weaknesses have a direct impact on the reliability of AI applications. Without consistent and complete data, predictions and applications remain vulnerable. Initiatives such as data lakes or central platforms do show that targeted investments are effective, but for now they remain exceptions.
The study identifies three barriers that limit the full potential of AI: data availability, technical integration into existing systems, and data quality and usability. Only when retailers systematically address these three elements can AI deliver results.
Price management illustrates how technology and data strategy reinforce each other. Solutions for AI-driven price optimization require a stable and structured data architecture. This is not about having as much data as possible, but about having reliable, consistent, and well-integrated information. Links to product information, ERP, and order management systems, supplemented with external data such as competitor prices, reduce manual intermediate steps and increase the reliability of automatic decisions.
AI and store design
The impact of AI now extends far beyond traditional applications such as inventory management, forecasting, or pricing. At retail trade fairs such as EuroShop, it becomes clear how AI affects all facets of the sector, from store design and energy management to shelf planning.
In store planning, AI-driven design tools make it possible to virtually simulate sales areas and adjust them in real time. With 3D visualizations and augmented reality, retailers can test walking routes, sightlines, and lighting in advance. This significantly shortens design processes and makes it easier to flexibly adjust store concepts.
