How retailers are closing the insight-to-action gap with AI

Key takeaways

- The National Retail Federation (NRF) Innovation Advisory Committee member Liam Buswell sees a growing retail focus on reducing the time between insight and action rather than collecting more data.

- Emerging technologies are helping retailers automate pricing, merchandising, customer service and fulfillment decisions.

- Examples highlighted at NRF 2026: Retail's Big Show Europe show how shorter decision cycles can improve profitability, customer experience and operational efficiency.

- The biggest challenge is often operating model design, governance and decision ownership rather than the technology itself.

- Retailers that successfully compress decision-making cycles may be able to respond more quickly to changing customer demand and market conditions.

Most of my work involves helping consumer and retail businesses get more value out of what they already have — leveraging new technology where possible — and the inefficiency is rarely where the team expects. The assumption going in is usually that they don’t know enough, and that with better data, better dashboards or better segmentation the answers would finally arrive. 

Often, they already have the answer. The missing opportunity sits around the decision itself: how long it takes to get from knowing something to doing something about it. This “intelligence - insight – action” layer is the problem I’ll be paying the most attention to when I’m in Paris for the National Retail Federation’s NRF 2026: Retail’s Big Show Europe next week. 

Why does retail decision-making still take weeks? 

The traditional decision cycle has many steps: Data is collected, reports are run, meetings are held, choices are made, changes are implemented — and each step is defensible. Together they add up to weeks, and weeks are often longer than the opportunity can sustain. The quality of the analysis matters less than whether it arrives while anyone can still use it. 

A new generation of retail tools is being built around that gap rather than around the data, and the results are becoming too strong to ignore. 

How does agentic AI improve retail pricing decisions? 

Take pricing, where the feedback loop is short enough to see clearly. Profitmind runs agentic AI across pricing, planning and merchandising, surfacing decisions continuously instead of in a weekly review. At Batteries Plus, the company said optimizing against live competitor pricing delivered a 240 basis-point lift in profit. The strategy there didn’t change, but the latency did. 

How is AI changing product discovery and merchandising? 

Product discovery is harder, because the inputs move while the customer is on the page. Good Growth reshapes discovery mid-session against live intent, stock position and margin, so the merchandising call happens during the visit rather than in next month’s range review. It’s running with The Home Depot, Kohl’s, M&S and Dunelm, proving that these operational integration problems are solvable at scale. 

How are AI agents improving retail customer service and fulfillment? 

In this day and age, timing is everything, and Nairos puts AI agents in front of shoppers to handle product questions, stock checks and pre-purchase advice at the moment. At Zoommer, one of the largest electronics retailers in the country of Georgia, the company reports that response times dropped from hours to seconds, sales rose around 15% and customer satisfaction held at 98.7%. 

Starship Technologies applies the same logic to fulfillment, using autonomous delivery robots to close the gap between wanting something and having it. The company says it can deliver groceries in around 22 minutes and are live with Co-op in the UK, REWE in Germany and across more than 165 stores with Finland’s S Group, where the service lifted app downloads by 49%. 

What connects these is not that the underlying intelligence is new — the fact is that most of it isn’t. Rather, it’s that the distance between knowing and doing has been cut out. 

That has consequences beyond the technology, and this is the part I’d encourage retail leaders to think about before the NRF Innovators Showcase at NRF 2026: Retail’s Big Show Europe in Paris. 

What operational changes are required to support AI-driven decisions? 

A decision cycle measured in seconds cannot be routed through a weekly trade meeting. Compressing it means being explicit about which decisions a system makes on its own, what boundaries it operates within and who is accountable when it gets one wrong. Those are operating model questions, not procurement questions, and they take longer to resolve than any implementation. 

The businesses that work through them early will get the full benefit. The ones that don’t will end up with faster tools that are just feeding the same slow process, and will assume, incorrectly, that the technology underdelivered. 

What technologies will retailers see at NRF 2026: Retail's Big Show Europe? 

If you’re coming to the National Retail Federation’s NRF 2026: Retail’s Big Show Europe, the Emerging Tech Tour is the most efficient way to see this in practice. The NRF Innovators Showcase spotlights the technologies reshaping how consumer shop and how businesses operate. These companies are solving the same problem across different formats, verticals and markets, and seeing them side by side makes the pattern much clearer than any deck will. 

Liam Buswell is a member of the NRF Innovation Advisory Committee and is a director at True Global.