Fast forward to 2028 the retail landscape is transformed. Picture this: AI agents orchestrate seamless operations across thousands of physical and online stores, they predict consumer preferences before they’re expressed, optimise supply chains with unprecedented accuracy, and troubleshoot issues before any sort of dent to customer satisfaction. Customer loyalty soars, margins expand, and market share is on a constant upward trajectory.
The alternative reality is far less glowing. Traditional retailers struggle with bloated inventories, customer frustrations and face an uphill battle to stay relevant. Their executives watch helplessly as competitors that have embedded AI agents into multiple areas of the business are capturing the attention of their previously loyal customers. Board meetings are post-mortems on missed opportunities and the gap between leaders and laggards has become insurmountable.
UK retailers must act now to determine what their future looks like.
Transformation isn’t optional, it’s urgent
Although not widely deployed yet, AI agents are already demonstrating a shift in how AI can reason on a retailer’s data to transform forecasting, inventory management, customer engagement and, above all, business decision-making. According to Gartner, nearly 15% of daily operational decisions will be orchestrated by such agents by 2028. However, Gartner also predicts that 40% of agent-based AI projects could fail by 2027. Fears of a speculative bubble, industrialisation challenges and budget constraints all fuel concerns and scepticism of AI agents. But this doesn’t need to be the case.
For retailers to see significant results from agent deployment they must start by identifying the clear business objective they want it to help with. In addition to this it’s vital that strong data foundations are in place for any AI agent systems to be truly impactful.
Unlocking infinite potential: From hyper-personalised experiences to autonomous logistics
The range of benefits for retailers looking to deploy AI agents is endless — both with internal and customer facing operations. Think supply chains where AI agents not only forecast demand with pinpoint accuracy but also reroute shipments in real time to avoid delays, automatically balance stock across stores and warehouses, and flag a risk of potential waste before it happens. Instead of having a reactive approach to shortages or overstocks, these intelligent systems continuously learn from sales patterns, weather data, promotions, and even local events — transforming inventory management into a living, self-optimising network.
On the customer side, we’re starting to see a spike in reliance on AI agent consultancy, moving away from direct interactions with brands. Domino’s “Voice of the Pizza” project to create a more seamless customer experience, is an example of this. By fine-tuning large language models in an interactive AI environment, the Domino’s team could seamlessly analyse and glean insights from feedback on its subreddit. As a result, customer insights are collated at a much faster pace, to quickly identify areas for improvement.
Customer service is another area which AI agents are set to reimagine. A recent Capgemini study indicates that AI agents could resolve up to 80% of customer inquiries in the first interaction. This means reducing customer wait times, creating a more seamless experience and in turn, driving customer loyalty and ultimately, bottom-line profitability.
Start strong: Create a solid data foundation
With clear objectives in mind from the outset, retailers must be confident their data foundations are fit for purpose. The theory that ‘AI is only as good as the data fed into it’ is undeniably valid. AI evolution, much like consumer expectations, is a moving target. New models, tools, and techniques are regularly launched. In order to keep pace with innovation a scalable data architecture must be in place, otherwise efforts and money will undoubtedly be wasted.
Danone, the leading global food and beverage company, is a strong example of a company that took the time to invest in a ‘future-ready’ data platform. The global company is modernising its data infrastructure to a powerful platform built on a lakehouse foundation, which democratises access to analytics, is open source and has robust governance built in. This means all the data is completely within Danone’s control. Alongside accelerated productivity and noticeable cost savings, the team expects this new data platform to get high-quality, fully governed AI prototypes validated and into production at an unprecedented pace.
With the correct foundations in place, retailers can focus on building an AI agent system that is trained on their unique dataset to solve case specific challenges. This needs to go hand-in-hand with proper evaluation so agents aren’t judged on ‘gut feel’ alone, which is a recipe for inconsistent quality and costly mistakes. Data teams should also look to explore creating synthetic data that mirrors real customer information, enabling agents to learn quickly without the risks tied to live data. Take a grocery chain, for example. Synthetic shopper baskets could be used to train AI agents to recommend substitutions when items are out of stock, allowing realistic practice without damaging consumer trust.
Retailers must act now to shape their future
Approached and executed correctly, retail is a prime sector to benefit from AI agents. Many retailers have already made significant investments to better understand their customers and keep pace with rapidly changing behaviours.
It will be those that set clear objectives for the agents and prioritise strong data governance that will reap the rewards.
Stefan Maczkowski
Stefan Maczkowski is Global Retail & Consumer Industry Leader at Databricks.
