Artificial intelligence (AI) has made it from theory into everyday practice in retail. This is also confirmed by two current studies German trade association (HDE) and the University of St. Gallen. While just five years ago almost eight out of ten (79 percent) of retail companies rejected the use of artificial intelligence, almost two thirds of the companies surveyed are now using intelligent applications or are planning to use them. Find out where retailers are already benefiting from AI and what it takes for AI to further develop its potential in trading.

Dynamic price optimizations, cashier-free supermarkets, intelligent procurement agents – there are plenty of application scenarios for artificial intelligence in retail. “It can increase sales, reduce costs and help to make processes more sustainable – provided it is used courageously, responsibly and strategically,” says the Swiss retail expert Andy Baldauf convinced.

It’s no wonder that AI is increasingly seen as an indispensable key technology in the industry – and has already become an integral part of the everyday life of many retail companies. “Our current study shows: AI has definitely arrived in retail,” confirms Stephan Trump from the German Trade Association (HDE). “Companies for which AI was not an issue at all two years ago are now in the concrete planning or piloting of intelligent processes.” Overall, according to the HDE study, retail companies are currently using AI primarily to accelerate processes, gain knowledge and improve the quality of results.

Top 5 AI application scenarios in retail

An important reason for the resounding success of AI in retail is likely to be the wide availability of Large Language Models (LLMs) such as ChatGPT. Many of the use cases implemented and planned according to the HDE study are based on this. “Retail companies are currently focusing primarily on AI applications with quickly visible results, while more complex scenarios such as cashier-free supermarkets, intelligent counters or predictive baskets remain the exception,” says Tromp. According to the study, the most popular use cases in retail include:

• Generation of article texts
  AI automates the creation of product descriptions and delivers content quickly and consistently. This saves costs and increases SEO visibility in online shops.
• Evaluation of customer feedback from social media
  AI-powered sentiment analysis makes it easier to identify trends, sentiment and issues. Product and service improvements can be controlled in a targeted manner.
• Bulk verification of data
  Smart analysis tools identify errors, redundancies or compliance issues in large amounts of data in no time. Manual effort is reduced and data quality increases.
• Smart shelves for inventory monitoring
  Intelligent shelves automatically report stock-outs or incorrect placement, helping to ensure product availability and minimizing out-of-stock situations.
• Sales forecasts
  AI improves sales prediction through more accurate models. Retailers benefit from optimized inventory and lower markdowns.

Sustainable value creation requires strategic AI alignment

And these are just a few examples of how retail has left the AI ​​experimental stage behind and is already successfully using the advantages of intelligent processes in many areas – although usually in the form of isolated solutions and only rarely across process chains.

According to a current survey by the University of St. Gallen, retail experts assume that intelligent tools will significantly change category management, goods requirement forecasts and personnel deployment planning in the near future. “However, sustainable value creation will only succeed if AI is strategically anchored, data-intensive core processes are automated and employees are relieved.” Prof. Dr. Thomas Rudolph from the Institute for Trade Management at the University of St. Gallen summarizes the key findings of the expert study. The seamless integration of AI into all areas of the company as well as the interlinking of different systems and data sources are the prerequisites for this – but according to Rudolph, these are currently still a thing of the future.

The problem: Heterogeneous, historically grown IT landscapes, fragmented data and inadequate data quality are slowing down the use of AI, especially in small and medium-sized companies. “Uniform data architectures, cloud-based platforms as well as early trust and know-how building could help here,” Rudolph is convinced.

SAP sets the course for intelligent trading processes

With the new SAP Business Suite and the SAP Business Data Cloud (SAP BDC) it contains, SAP delivers everything it needs: The consistently integrated system of applications, data and artificial intelligence (AI) connects all areas of the company and thus creates the basis for intelligent decisions, well-founded real-time insights and automated processes, including in retail. “AI only brings real added value in retail if it is not thought of as an isolated tool, but as part of the corporate strategy,” sums up Stefan BinkowskiVice President Retail & Wholesale Advisory, Middle & Eastern Europe (MEE) at SAP. SAP’s portfolio and especially the solutions around SAP Business Data Cloud and SAP Business AI pave the way for this.