This might sound like a contradiction: how can the growing use of AI and tech-led advances lead to more personalised shopping experiences? asks Matt Sherwen, Managing Director of Sherwen Studios.

Over the last three years, retailers have integrated artificial intelligence into their technology stacks. While 2023 saw routine automation streamline tasks, 2024 marked the start of a new era of personalisation, which evolved into hyper-personalisation in 2025. But will this change in 2026?

While many op-eds have suggested the AI bubble is about to burst, we believe the opposite. As AI has become mainstream and quick wins have been achieved, we predict that 2026 will see retailers prioritising the use of AI to enable smarter decisions, deeper customer engagement, and operational resilience.

Voice activation tells us more than we realise, but only if we’re listening to the correct data

Voice assistants like Alexa, Siri and Google Home have become mainstream tools used by customers at home and on the go. It’s easy to build a shopping list by simply asking your voice-activated device to add an item, so you never forget those must-have items. However, those lists reveal more than just what a customer needs. That data tells us their brand preferences, signal timing and purchasing intent.

In 2026, AI analytics will decode these signals to identify when purchasing intent becomes purchasing action.

For example, if a customer walks into a store with their smartwatch or phone, it can sync with geo-location data and trigger a personalised reminder to buy their saved item. That reminder can be paired with an offer for their preferred brand or align with a loyalty bonus. This real-time prompt creates a frictionless experience that delivers exactly what the customer is looking for, when they need it.

Predictive commerce will extend beyond customers to support the wider supply chain

AI has transformed predictive commerce, allowing retailers to analyse past purchasing history, identify purchasing patterns, and predict future trends.

It’s now time to focus more on using predictive analytics to understand a user’s intent rather than purchasing history. That way, behaviour signals suggesting an imminent purchase will become much clearer. Those signals can automatically apply time-sensitive loyalty points or complementary offers, reduce cart abandonments and boost conversions.  

A better understanding of customer intent will have knock-on effects elsewhere in the supply chain. Insights will make forecasting demand easier, reduce returns, and minimise waste. For senior leaders, this means predictive analytics will become an essential part of inventory planning and operational efficiency, and personalisation will no longer be just a marketing tool.

Phygital retail will facilitate geo-specific, real-time promotions

We want to see generic promotions phased out. In their place, we want to see tech used to create geo-specific, hyper-targeted offers responsive to an individual’s real-time shopping environment.

Too often, technology is used in siloes when it should be combined to deliver the best possible experience, which redefines a customer experience. For example, we welcome a retail environment where wearable technology, push notifications, geo-tracking capabilities, and even digital signage are working together to create context-aware, real-time phygital experiences.

Imagine a scenario where a sudden weather shift occurs in a key retail location. Instead of static promotions, an integrated system could automatically update in-store signage and geo-specific notifications to promote weather-appropriate products. This gives the opportunity to maximise transactions during high-purchasing intent moments. This combined technology could increase basket size by recommending context-aware products (e.g., an umbrella) and guiding shoppers to relevant aisles. It also reduces product markdowns by prioritising seasonal inventory and combining it with real-time loyalty incentives.

That holistic experience aligns technology cohesively, while also providing shoppers with a dynamic and helpful experience that intuitively meets their needs at the right moment.

AI in retail is maturing into purposeful, business-led innovation

Voice data, predictive commerce, and hyper-personalisation aren’t just trends. These are strategic functions which have changed how we work, and when managed correctly, will be core components for long-term growth and efficiency.

The challenge for 2026 is to stop viewing AI as an isolated pilot project. To reap the benefits, it must be embedded across every part of your business, allowing holistic data analysis.   

Voice and geo-data are sources of insight that are just as valuable as behavioural and purchasing histories. Predictive analytics are not just about supporting marketing and sales teams; they also need to be used to guide merchandising and supply chains.

AI only works if systems are agile and responsive to changing customer needs. This isn’t just about your tech stack; it’s about recognising whether your workplace culture is ready to integrate those insights across different functions and create an agile system which prioritises better customer experiences, wherever they are in your sales funnel.