AI is a topic that’s on every retailer’s mind right now. Larger retailers are already implementing AI agents in-store to help with product knowledge, stock levels, and other tasks that customers would typically search for online. In addition to traditional AI the major focus for larger retailers is on generative AI for product descriptions, personalisation and text production.

We’re still waiting to see a truly great example of AI in action. While some examples from larger retailers have been more concrete, showing how AI could be used, the focus is now on how it will be configured and implemented.

We’re seeing bits and pieces of how AI can improve productivity and knowledge, especially in-store,” says Ole Johan, Lindøe, Vice President of Digital Commerce at Columbus.

“Right now, AI and ML is more prominent in personal productivity, and it’s entering marketing workflows as it can help produce a lot of content. This includes everything from public text to ads. Larger companies like Salesforce, Microsoft, Google, and Amazon are at the forefront, but many are using these technologies to enhance and tailor them to specific use cases.”

However, there’s still more work to be done when it comes to overproduction and understanding how AI can help retailers manage this. “While retailers are working to better predict demand and reduce waste, there’s still progress to be made. In fashion, selling 70-80% of inventory at full price means profit, but selling only 60% is problematic. Predicting the right price and quantity can improve profitability and reduce waste, benefiting both the environment and the business.”

When it comes to AI, control over data is crucial before any AI projects can begin. “At D-Congress there was broad agreement among panellists that you need to start with your data first. Industry leaders recognise that gaining control over their data is essential because you need accurate and reliable data about customers, orders, and everything surrounding your products.

“So, it starts with a data strategy before you plan an AI strategy. The same applies to sustainability—consistent and accurate data is needed across all areas, so it’s critical to get it right from the beginning. But before you can develop an AI or sustainability strategy, you need the right data structure in place to ensure everything works together.”

These thoughts were echoed by the panellists at the roundtable Columbus hosted at D-Congress. Alexander Flaig, Lecturer & Associate Professor/Co-Founder at SHAPE Agency, highlighted the need for broader AI education: “From my research, most C-level executives lack AI capabilities, which means there’s a significant need for AI education, especially at the leadership level, because they are the ones who will drive change and lead this experimental culture.”

Anca Lordanescu, VP of Engineering at IKEA (Stores of the Future), also shared her thoughts on AI in retail today: “The biggest barrier to using AI is the data. I don’t think we’re ready from a data perspective to adopt AI on a large scale.”