The conversation around AI in retail has reached a curious turning point. Walk into any retail conference, scroll through LinkedIn, or listen to industry podcasts, and you’ll hear the same narrative repeated: large retailers have the resources, the budgets, and the expertise to win the AI race. Smaller retailers, we’re told, will struggle to keep up.

But something interesting is happening on the ground that doesn’t quite match this narrative.

I’ve been learning about AI at quite a pace recently. Over the past twelve months, I’ve taught myself to write better prompts, built custom GPTs, and assembled a small stack of AI tools I use every single day. I’m even starting to understand agentic AI and how different tools can work together more like a team than a single assistant.

None of this happened through formal training or a big organisational rollout. It happened through curiosity, experimentation, and trying things in my own time. That personal experience got me wondering about a fundamental question: how is AI in retail actually being adopted?

If individuals and small teams can move this quickly with readily available tools, what does that mean for smaller retailers? Are they adopting AI faster simply because they can test ideas more quickly? Are they solving real customer problems with practical tools, whilst larger organisations focus on building the right foundations and governance frameworks?

So I went looking, and what I found challenged some common assumptions about AI adoption in retail.

The AI Adoption Paradox: Expertise Versus Implementation

According to recent research from monday.com, 99% of large UK retailers report having AI expertise somewhere inside their organisations. That’s a staggering figure. It suggests the big players have the knowledge, the specialists, and the technical capability to make AI in retail work at scale.

Research also shows that 31% of small retailers already use AI daily. Not planning to use it, not building roadmaps, but actively using it as part of their everyday operations.

On the surface, that sounds like the large retailers are miles ahead. Ninety-nine per cent have expertise. They’ve got Chief AI Officers, dedicated teams, and strategic roadmaps. But having AI expertise isn’t the same as having AI embedded into your business’s daily rhythm.

UK retailers are projected to invest £2.47 billion in AI by 2034, up from £554 million in 2025, according to IMARC Group. That’s an 18% compound annual growth rate. Retailers across the spectrum know this technology matters. Seventy-three per cent of UK retailers are actively increasing their investment in AI over the next year, according to Salesforce research.

But what’s driving adoption varies dramatically by size. Large retailers are investing in AI to gain a strategic competitive advantage. 61% have established dedicated AI leadership roles. They’re building foundations, ensuring governance, managing risk, and thinking about AI in retail at an enterprise level.

Some smaller retailers are using AI in retail differently to solve immediate friction points. One group has the resources and the roadmap. The other has the freedom to try something on Tuesday and see if it helps by Wednesday.

This matters because while 57% of UK retailers are using generative AI for translations and copywriting, and 43% have deployed AI-powered customer service chatbots, the competitive advantage is not in who has the most sophisticated AI in retail strategy, but in who can apply it closest to the moments that matter.

AI in Retail Practice: From Scale to Proximity

Rather than staying at the level of strategy and statistics, I wanted to look at what AI in retail actually looks like on the ground, and how retailers of different sizes are using these tools to solve specific customer problems.

These examples span the spectrum, but they all share a common trait: they use AI in retail to remove friction at critical decision points.

Virgin Wines: Removing Uncertainty at Scale with AI

Virgin Wines is one of the UK’s largest direct-to-consumer wine retailers, operating in a notoriously difficult-to-get-right category. Wine is subjective, emotional, and full of uncertainty. Most customers don’t want to experiment endlessly with different bottles. They just want to choose something they’ll actually enjoy.

The traditional approach to this problem in retail has been broad segmentation (grouping customers by age, spending patterns, or purchase history) and then making recommendations based on what similar customers bought. It’s better than nothing, but it’s still fundamentally guesswork.

Virgin Wines partnered with an AI company, Preferabli, to tackle this in a different way. Their system breaks each wine down into measurable sensory attributes: acidity levels, tannin structure, aroma profiles, body, and finish. These are quantifiable characteristics rather than just product descriptions.

Those attributes are then matched to individual customer preferences, which the system builds up over time based on past purchases, explicit ratings, and responses to guided tasting questions. When a customer browses for wine, the AI platform can recommend bottles based on how that person actually experiences wine, not just what people who vaguely resemble them have purchased.

This is AI in retail, used at scale, embedded into a core customer decision, and applied consistently across a large customer base. Virgin Wines isn’t using AI to impress customers or tick a technology box. They’re using it to remove uncertainty at the exact moment a customer needs help making a decision.

The results matter. When customers trust recommendations, they’re more likely to explore new wines, spend more per transaction, and return for future purchases. But what’s interesting is that the same principle (identify a moment of uncertainty and use AI in retail tools to reduce it) shows up just as clearly when you look at independent retailers.

Abelini: AI in Retail for Highly Personal Decisions

Abelini is an independent jeweller based in Hatton Garden, London’s historic jewellery quarter. They operate in a completely different environment from Virgin Wines: a smaller customer base, higher-value transactions, and decisions loaded with emotional weight.

Buying jewellery, especially engagement rings, comes with enormous pressure. Customers want to know how something will look on them. Not on a model in professional lighting, not in a showroom with mirrors everywhere, but on their own hand, in their own space, in natural light.

That moment of hesitation, “Will this actually suit me?” is where decisions stall. Customers leave the website to think about it, meaning to come back, but often never return. Or they order multiple items, intending to return what doesn’t work, which creates operational headaches and disappointment.

Abelini uses AI combined with augmented reality to step into that exact moment of uncertainty. Customers take a photograph of their hand or wrist using their phone. The AI generates a realistic 3D model of their hand, automatically scaled to the correct proportions. Jewellery is then rendered onto that model so people can see how pieces look from different angles and under different lighting conditions, and they can even share those images with friends and family for feedback.

What matters here isn’t the technology’s sophistication. It’s what it removes. It removes the need to travel to a physical store. It removes the pressure of making a high-stakes decision on the spot. It removes the guesswork from a highly emotional purchase.

That’s the same principle we saw with Virgin Wines. AI in retail, stepping into the moment of uncertainty, just applied in a smaller, more personal context. The technology serves the decision, not the other way around.

ListAid: AI in Retail Supporting Pressured Workplace Decisions

Not all friction points in retail involve customer-facing decisions. Some of the most interesting applications of AI in retail happen behind the scenes, supporting the people who make dozens of decisions every single day.

ListAid is a tool developed by Cybertill specifically for UK charity shops. When a donated item arrives at a charity shop, someone (often a volunteer with no retail background) has seconds to decide what it’s worth. Is this valuable or not? Should it go on the shop floor? Would it sell better online? Is it worth researching further? Does it need to move quickly before it goes out of season?

These decisions often happen under time pressure, and when they go wrong, value gets left on the table. An underpriced designer handbag sells for £10 when it could have generated £200 online. An overpriced item sits on the shelf for months, taking up valuable space.

ListAid uses AI to support those pressured moments. Staff or volunteers photograph the item. The AI identifies the brand or product name using image recognition, suggests a price based on analysis of millions of charity retail transactions, and recommends the best sales channel: shop floor, online marketplace, or specialist auction.

The whole process takes under a minute. What it removes is anxiety and second-guessing. Volunteers don’t need to be experts in fashion, homewares, or collectables. They don’t need to worry about having just massively underpriced something valuable or about putting rubbish out on the shop floor.

This isn’t AI in retail doing something flashy. This is AI in retail supporting a real decision that happens hundreds of times a day, improving outcomes while reducing stress on the people doing the work.

When AI in Retail Creates Space for Human Work

One of the most common concerns about AI in retail is that it strips away the personal touch, making retail feel colder, more automated, and less connected to actual human expertise and relationships.

But some of the most interesting use cases of AI in retail actually do the opposite. They don’t replace the human element. They amplify it by removing the friction that prevents expertise from reaching customers.

Finney’s: Making Three Generations of Expertise Accessible

Finney’s is a family-run jeweller in Aberdeen, trading since the 1950s across three generations. They’ve built an extraordinary depth of knowledge in watches and fine jewellery. The kind of expertise that comes from decades of working with customers, understanding craftsmanship, and caring deeply about quality.

Traditionally, that expertise lived behind the counter. If you wanted to benefit from it, you needed to visit the shop, catch someone at a quiet moment, and hope they had time to talk you through whatever you needed to know.

Instead of keeping that knowledge locked away, Finney’s has used digital tools and AI to make it more accessible. Customers can access detailed guides on watch care, servicing schedules, and jewellery maintenance. They can learn about different gem settings, understand hallmarks, and feel confident about looking after valuable pieces long after the initial purchase.

This isn’t automation replacing expertise. This is expertise being shared more widely, at a time that suits the customer, without losing any of its character or depth. The AI tools handle the distribution and accessibility. The knowledge remains authentically human.

Happy and Glorious: AI in Retail as an Extra Pair of Hands

Kate Thompson runs Happy and Glorious, an independent gift shop in Canterbury. She’s a one-person operation handling everything: buying, merchandising, marketing, customer service, bookkeeping, social media, and everything else that keeps a small retail business running.

She uses AI as an extra pair of hands for time-consuming administrative tasks: proofreading product descriptions to catch typos, drafting email subject lines that might perform better, suggesting social media captions, and generating ideas for seasonal campaigns.

These aren’t the parts of retail she loves. They’re not what makes her business special. But they need doing, and they eat up time that could be spent on the work that actually matters, such as curating interesting products, talking with customers, building relationships with suppliers, and creating the experience that makes her shop worth visiting.

AI tools give her back that time. She still makes all the creative decisions. She still chooses every product. She still writes in her own voice. But she’s not spending two hours agonising over an email subject line when AI can suggest five options in thirty seconds.

In both these cases, AI isn’t taking over human work. It’s creating space for it by handling the friction that prevents people from focusing on what they do best.

The Pattern: Proximity to Decisions Matters More Than Scale

When you step back and look at these AI in retail examples together, a clear pattern emerges.

The advantage smaller retailers are developing isn’t about having more AI expertise or bigger budgets. It’s about where AI tools can show up in their operations. In each case, the tools sit right next to actual decisions, both customer decisions and colleague decisions.

What should I buy?

What does this look like on me?

How much is this item worth?

How do I explain this to a customer?

Where is my time going?

How do I price this confidently?

That proximity matters enormously because when AI sits close to the decision point, it can immediately influence the outcome. There’s no gap between the insight and the action. The tool provides support exactly when someone needs it.

In larger retail organisations, AI often lives deeper in the infrastructure, optimising supply chain logistics, forecasting demand across hundreds of locations, and improving operational efficiency at an enterprise level. All of that work creates genuine value and competitive advantage. But it’s also further removed from the daily moments when customers hesitate, when team members feel uncertain, or when time starts disappearing into administrative tasks.

Research from monday.com shows that 92% of UK retail decision-makers say AI is not yet making key business decisions autonomously. That’s deliberate. Retailers understand that these tools work best when they support, not replace, human judgment.

The competitive advantage of AI in retail adoption isn’t forming around intelligence versus ignorance. It’s forming around distance. How many layers sit between the problem and the person who can try something to fix it?

Smaller retailers often have fewer layers between noticing a problem and testing a solution. They can spot issues sooner because they’re closer to customers and operations. They can experiment faster because they don’t have to navigate complex approval processes. They keep what works and drop what doesn’t because the feedback loop is immediate and clear.

That ability to connect problem, experiment, and outcome quickly is where a real competitive advantage in AI in retail is starting to form, regardless of company size.

Journey Mapping Reveals Where AI in Retail Creates Most Value

This is where customer journey mapping becomes so valuable, not as an academic exercise but as a practical tool for identifying where AI tools can make the biggest difference.

When you look closely at how customers move through your business, getting into the details and the weeds of what really happens, friction points start to become visible. Those are the moments where people hesitate, where your team second-guesses decisions, or where customers drop off and disappear without telling you why.

Once you can see those moments clearly, the use cases for AI in retail often become obvious. You stop having abstract conversations about AI strategy and start having specific conversations about specific problems.

With Virgin Wines, the friction point was choosing wine with confidence amid overwhelming options. With Abelini, it was about visualising what an expensive, emotional purchase would look like without the pressure of a showroom visit. With ListAid, it was possible to price donated items accurately under time pressure when expertise wasn’t available.

Your AI plan doesn’t need to transform your entire business overnight. It needs to support the specific decisions that are causing friction for customers and colleagues right now. That’s a much more achievable brief.

The shift that journey mapping creates is this: you stop thinking “we should probably do something with AI” and start seeing “this exact moment needs help, and here’s what that help might look like.”

When you tackle things moment by moment rather than trying to boil the ocean, you build a roadmap that’s actually implementable. Each small improvement compounds over time. You learn what works in your specific context. You build confidence in your team. And you start seeing measurable results that justify further investment.

According to Salesforce research, 79% of UK retail decision-makers believe AI agents will be essential for competing within a year. But the retailers finding competitive advantage now aren’t the ones with the most sophisticated AI strategies. They’re the ones who’ve identified their most painful friction points and found practical tools to address them.

Where This Leaves You: The Real Competitive Advantage

When you step back from all these examples of AI in retail adoption, I don’t think the competitive advantage is actually about AI at all.

It’s about noticing where customers are hesitating, where teams feel anxious or uncertain, and where you’re losing time to tasks that don’t create value. It’s about having the organisational freedom and the courage to try something quickly. It’s about keeping what works and dropping what doesn’t without getting bogged down in politics or perfectionism.

Some retailers get there through scale, resources, and structured innovation programmes. Others get there through proximity to problems and the ability to move fast. Both approaches work. Both create value.

But in every example we’ve looked at of AI adoption in retail, the meaningful difference shows up in the same place. It shows up when technology is used to support a real decision. When it helps someone choose with more confidence. When it removes guesswork from a pressured moment. When it gives people back time to focus on what actually creates value for customers.

That’s when AI in retail starts to have an impact. That’s when it delivers a competitive advantage that matters. Not because the technology is impressive, but because it makes something important work better than it did before.

---

If this episode resonated with you, I’d love to hear your thoughts. What’s one insight you’re planning to put into practice or one learning you took from today’s episode?

Let’s connect – Find me on LinkedIn

Never miss an episode – please subscribe to Where’s Your Customer? wherever you get your podcasts and get fresh customer experience insights in your ears weekly.

Found this valuable? Please leave me a review. It helps other retail professionals discover these conversations and honestly means the world to me.

Share the knowledge – If someone in your network could benefit from the ideas we’ve talked about today, please share this episode with them.

If you’d like help with Customer Journey Mapping – Let’s Chat.