AI in retail stores: the frontline insights WFM leaders can’t ignore

To understand what’s really happening in UK stores, we partnered with Retail Week on Talking Shop 2026, a survey of 500 frontline retail workers across multiple sectors and role types.

In this article, we explore what frontline workers told us about:

• How they view the technology already in their stores.
• What they think about AI and its potential.
• Where they most want digital tools to help.
• What needs to change for digital projects to land better on the shopfloor.

This piece is part of our chapter-by-chapter analysis of the report, designed to help workforce management leaders understand what the findings mean in practice for workforce planning, store execution, technology adoption and communication.

The full Talking Shop 2026 report gives the wider research picture. This article takes a closer look at the operational implications for workforce management leaders, focusing on AI and tech. 

Let's take a closer look... 

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Why AI frontline insights matter now

The promise of retail technology vs store reality

Where frontline retail workers most want technology to help

Retail AI on the shopfloor: cautious optimism

When digital change is driven from the top

Examples of technology and AI working well in stores

What successful AI in retail looks like in practice

What frontline teams say they need from digital tooling and AI

How this chapter connects to the wider Talking Shop report

Why AI frontline insights matter now

Walk into almost any store today and you’ll see more technology than you did a few years ago. Self‑service checkouts. Click‑and‑collect counters. Handheld devices. Digital screens.

But technology in stores isn’t just about hardware. AI has changed the game, shifting attention from the tools themselves to how they support decisions, workflows and execution on the shopfloor. In retail, that means looking at how digital tools, AI systems and AI tools actually feel for the workers who use them every day - and how they affect retail operations in physical stores.

Across the retail industry, businesses are exploring how artificial intelligence, machine learning and data analytics can improve retail operations, customer experience and customer satisfaction in physical stores.

The promise of retail technology vs store reality

Across the retail industry, businesses are experimenting with a wide range of AI tools - from inventory management and demand forecasting to pricing support and data analytics. But for most retailers, the challenge isn’t just access to data.

It’s making those tools useful in physical stores, turning inputs from different data sources into actionable insights that support better decision-making on the shopfloor. That includes analysing data from sales data, customer data and other relevant data sources to improve retail operations without adding complexity for store teams.

How retail workers feel about technology

Overall, frontline teams are positive about the idea of better technology and how it could improve the overall retail experience.

• 62% agree that having the right technology in place would improve store operations.
• 87% expect the amount of technology in stores to grow in the future, rising to 92% in fashion.

At the same time, many feel that current digital change is patchy and slow.

• 40% say there isn’t much digital change in their store, or that it’s happening too slowly.
• 50% say the pace of digital change is reasonably fast but manageable.
• Only 12% say they feel uncomfortable with digital change.

For many retailers, the opportunity isn’t just introducing more AI and technology, but helping store teams see how it can improve the retail experience and support more effective retail operations.

The adoption gap on the retail frontline

Most frontline retail workers are open to technology, but they still see an adoption gap. They feel that while the head office is moving quickly on digital transformation, many stores are still working with older tools, manual processes and uneven support.

For WFM leaders, that matters because digital investment only creates value if store teams can actually use it, trust it and see how it improves day-to-day operations. A useful next step for WFM leaders is to focus AI investment on areas where frontline teams can see immediate value, such as forecasting, scheduling and labour visibility. 

Where frontline retail workers most want technology to help

Practical priorities for store technology

When asked where technology could make the biggest difference to retail store operations, frontline staff focused on very practical needs.

• 19% chose stock availability and ordering.
• 18% chose staff training and learning.
• 13% chose the ability to secure hours.

These priorities reflect daily realities on the retail frontline:

• Getting the right products on shelves at the right time.
• Keeping up with new ranges, procedures and systems.
• Knowing when they’re working and being able to plan around that.
• Having a reliable number of hours to live on.

These are practical pain points, and they show that frontline teams want technology that helps stores function effectively, improves visibility and supports better execution in physical stores.

For many retailers, that means focusing less on abstract AI systems and more on AI tools that improve efficiency, support inventory management, and give store associates relevant information they can use in day-to-day retail operations.

AI tools in retail that improve efficiency on the shopfloor

Frontline staff aren’t asking for futuristic AI technology or abstract retail AI concepts. They’re asking for AI tools that help them do the basics well:

• Tools that make core tasks and routine tasks faster and more accurate.
• Tools that help them feel better prepared for their roles.
• Tools that give them more stability and control.
• Tools that help teams improve efficiency without adding complexity.

For WFM leaders, this is one of the clearest signals in the report. Frontline teams aren’t asking for abstract innovation. They’re asking for technology that improves execution, visibility, training and control over working life. 

Retail AI on the shopfloor: cautious optimism

How retail employees see AI today

AI is moving up the agenda for retail leaders. On the shopfloor, the mood among retail employees is more measured. This reflects how AI in retail is still being understood and evaluated by frontline teams.

When we asked frontline workers how they see AI helping daily store operations:

• 30% said they believe AI would help their store’s day‑to‑day operations.
• 41% said they’re unsure about AI’s impact.

Much of this uncertainty comes from a lack of visibility into how AI models, machine learning, and other AI systems actually work in practice. AI systems can analyse large amounts of data quickly, but unless frontline teams can see how that leads to better decisions, the value of those AI models remains hard to trust.

There are also clear concerns:

• 34% worry that technology could replace parts of their role.
• 17% say the technology introduced so far hasn’t meaningfully improved how they do their job.

For WFM leaders, the message is clear: AI will land better when it solves a real operational problem, reduces friction and gives frontline teams a benefit they can see for themselves. 

Building trust in AI on the shopfloor

Yet when AI is applied to a concrete problem that colleagues care about, trust looks very different.

• 76% of frontline staff say they would trust AI or intelligent scheduling in some form to manage their store rota.

This contrast suggests that frontline workers aren’t opposed to AI in principle. They’re wary of abstract AI projects that feel distant, opaque or focused mainly on monitoring. They’re much more positive when AI is used in specific, understandable ways that clearly help them.

Practical questions for WFM leaders

For WFM leaders, this means the real questions are practical ones:

• Which frontline problems are we choosing to solve with AI first?
• How do we explain what AI is doing and why?
• How do we show colleagues the benefits in their day‑to‑day work?
• How can we use better data, relevant data and real-time data to support more informed decisions at store level?
• How can AI help retailers use high-quality data to provide insights that improve customer service, customer experience and store execution?

For WFM leaders, the message isn’t to avoid AI. It’s to start with visible, practical use cases that make work easier, improve execution and build trust on the shopfloor. 

When digital change is driven from the top

Who drives digital change – and how

One of the strongest messages in the data is about who drives digital change and how.

• 44% of frontline workers say digital change in their store is driven from the top, without their input.
• Among workers aged 16-35, this rises to 51%.

This is particularly striking because younger colleagues have grown up with advanced consumer technology and are often very comfortable with digital tools in their personal lives. They’re not resistant to tech. They’re uncomfortable with being left out of decisions that shape their work.

For WFM leaders, this matters because top-down rollout decisions can quickly become execution problems at store level. The less involved frontline teams feel, the harder it becomes to build confidence, consistency and uptake. It also matters for future talent, because many younger retail workers will become tomorrow’s leaders, and how change is handled now will shape whether they stay, grow and engage. 

Why involvement and communication matter

How AI is introduced matters as much as the technology itself. When a new AI initiative lands on the shopfloor, frontline trust in leadership, in the purpose of the change and in the technology itself shapes how it’s actually received. Clear communication and transparency are critical to building that trust.

Combined with the earlier findings on technology not yet improving some colleagues’ day-to-day roles, this creates a clear message:

• Involvement and clear retail communication matter as much as the tech itself.
• When digital projects are done to the frontline rather than with them, adoption is slower and trust is harder to build.

Without this, even well-intentioned AI initiatives can struggle to gain traction on the frontline.

For WFM leaders, this is the operational risk. If new systems are introduced without enough frontline input, even useful tools can create friction, slow adoption and reduce confidence at store level.

That’s why choosing a partner with strong implementation and support matters just as much as choosing the technology itself. Lakeland, for example, wanted a close partner, intuitive tools and reliable support to drive adoption - and had its first stores up and running in one month, with 100% adoption across store leaders and teams. 

Examples of technology and AI working well in stores

The Talking Shop 2026 report also highlights concrete examples where technology already supports frontline teams effectively.

The Entertainer – intelligent workforce management

Toy retailer The Entertainer uses Rotageek - an intelligent workforce management platform that combines AI‑powered demand forecasting with smart scheduling.

This gives managers real‑time visibility over labour spend and demand alignment.

This kind of demand forecasting gives managers better visibility into labour needs and supports more consistent decision-making at store level.

It gives colleagues a mobile self‑service app to swap shifts, request leave and submit preferences.

The result is:

• Less manual rota building and rebuilding.
• More transparency and control for staff.
• A stronger connection between senior teams and the frontline.

This reduces human error and improves consistency across scheduling.

Currys – making data usable for store leaders

Currys launched Action AI, a tool that makes business‑critical data easier for store managers to access and act on.

Instead of sifting through spreadsheets, fragmented sales data and other disconnected data sources, a sales manager can:

• Log in and see which product categories are underperforming, with relevant information surfaced in real time.
• Drill down into root causes using real‑time transaction trends and other relevant data.
• Compare performance with top‑performing stores across the estate.

This allows managers to turn complex data into actionable insights quickly, using relevant information surfaced in real time. In practice, that means giving managers relevant information they can use to improve customer service, support customer satisfaction and make more confident decisions on the shopfloor.

As Currys’ Chief Operating Officer, Lindsay Haselhurst, explained, the tool:

• “Cuts through the mountain of data”,
• And frees managers to focus on what they do best – helping customers with their tech needs, improving the overall customer experience and driving higher customer satisfaction.

This is AI‑enabled analytics as a practical enabler, not an abstract project:

• It gives store leaders insight that was previously locked up in head office systems.
• It helps them take targeted, local action.

JD Sports – RFID to support availability and speed

JD Sports is rolling out RFID and automated inventory management software across its store estate, starting in Europe.

These types of solutions also help retailers optimise inventory, manage inventory levels more effectively and respond to supply chain disruptions. By analysing real-time data across store performance, businesses can reduce waste and improve efficiency across the retail sector.

During the pilot phase, the retailer reported:

• A 12% increase in on‑shelf availability.
• A 20% improvement in restocking speed.

This also helps teams adapt stock placement and store layouts more effectively. Better inventory management also gives teams more confidence when adjusting store layouts and responding to changing demand.

Tools like RFID and automated inventory management also provide real-time visibility into stock levels, helping retailers optimise inventory, improve inventory accuracy and reduce waste when demand shifts.

Store teams and the technology partner noted that the platform:

• Was easy to use.
• Was quick to deploy.
• Was simple to adapt to what staff and the wider business required.

This also supports better use of customer data to improve availability, service and the in-store customer experience.

The focus is clear:

• Improve a core operational metric – availability.
• Make store work more efficient and less manual.
• Support the wider business with better data, without adding friction for colleagues.

Boden and Debenhams – AI‑powered pricing

Fashion retailer Boden, and Debenhams’ online business, have both used AI‑powered pricing to move beyond blanket markdowns.

By analysing data including real-time demand, sell-through and margin data, AI helps:

• Identify which products need deeper discounts.
• Protect margin where demand is still strong.
• Reduce waste and stock build‑up.

In the wider retail sector, this is one example of how pricing strategies and dynamic pricing can become more precise when supported by high-quality data.

AI can also help retailers adapt pricing strategies by analysing competitor pricing, local demand and the impact of promotions, helping businesses protect margin while staying competitive.

For WFM leaders, these examples point to a clear pattern. Successful AI in retail starts with a specific operational problem, gives store teams a clear benefit, and helps managers make faster, better decisions at local level. 

What successful AI in retail looks like in practice

The strongest examples in the report aren’t the most futuristic. They’re the ones solving clear operational problems in ways that frontline teams can actually use.

In practice, successful AI in retail helps stores make better decisions, faster. That might mean giving managers clearer visibility of performance, improving stock availability, building smarter rotas, supporting more accurate pricing decisions or using better data to provide insights that improve day-to-day retail operations.

In the wider retail sector, similar AI technology can also support assortment planning, optimise inventory levels and help retailers stay ahead by turning massive amounts of data into more useful, local decision-making.

These examples share common traits:

• They start with specific, high‑impact problems.
• They’re designed to be usable for store teams.
• They show a clear link between digital investment and day‑to‑day work on the retail frontline.

They also show that the best AI initiatives start with clear operational needs, not just massive amounts of data for their own sake.

For WFM leaders, that means focusing less on AI as a headline initiative and more on where it can reduce friction, improve visibility and support more confident decision-making in stores. 

What frontline teams say they need from digital tooling and AI

Looking across the statistics and comments, several needs emerge.

Involvement in decisions

Employees want to:

• Be consulted before major tools or process changes arrive in stores.
• Contribute insight on what will and won’t work in their specific formats.
• See their feedback used to refine systems during pilots and roll‑outs.
• Incorporate regular customer feedback and frontline insight into decision-making.

Tech that solves real store problems

The priorities are clear:

• Better stock management.
• Easier access to training and guidance.
• More stable, transparent rotas and hours.

When digital tools address these areas directly, colleagues are:

• More willing to engage.
• More likely to see value in the change.

Clear, honest communication

For AI and digital tools to gain trust, frontline workers need:

• Plain language explanations of what a system does and why it’s being introduced.
• Clarity on what will change in their day‑to‑day work – and what won’t.
• Reassurance around job impact, backed up by visible behaviour from leaders.

Training and support, not just roll‑outs

For new systems to function effectively, frontline workers also need:

• Time and space to learn new tools, not just instructions.
• Support materials in formats that work on the shopfloor – for example, short videos and mobile‑friendly content.
• Access to help when something doesn’t work as expected.

Together, these elements turn technology from something imposed on the store into something that supports it. They also make it more likely that AI initiatives will function effectively in real-world stores, where customer feedback, frontline insight and day-to-day usability matter more than headline innovation.

For WFM leaders, these findings are a reminder that digital adoption isn’t just a systems question. It is a workforce question. Involvement, communication, training and day-to-day usability all shape whether technology improves execution or simply adds another layer of complexity.

How this chapter connects to the wider Talking Shop report findings

Digital and AI adoption doesn’t sit in isolation. In Talking Shop 2026, it connects directly to wider themes around communication, frontline wellbeing and the leadership choices that shape how change lands in stores.

Taken together, the findings show that AI adoption isn’t separate from workforce strategy. For WFM leaders, success depends on how well technology, communication, scheduling and frontline involvement work together.

Download the full Talking Shop 2026 report

This chapter is one part of a wider look at the state of the retail frontline in 2026.

In the full Talking Shop 2026 report, created in partnership with Retail Week, you’ll find:

• All core chapters, including frontline voice, digital and AI adoption, wellbeing and leadership actions.
• Detailed statistics and year‑on‑year comparisons.
• Case studies from retailers including M&S, Asda, Primark, JD Sports, Currys, The Entertainer, Lush, Holland & Barrett, Boots, John Lewis, Sephora and more.

You can read the full Talking Shop 2026 report, in partnership with Retail Week here.

Alongside the report itself, this series takes a closer look at the findings chapter by chapter, with a particular focus on what they mean in practice for workforce management leaders. That includes the operational implications, the patterns behind the data, and the actions leaders can build into workforce strategy, communication and planning.

If you want the complete research view, start with the full report. If you want a more detailed look at what the findings mean for WFM leaders, explore the rest of the chapter-by-chapter analysis across the campaign hub.

FAQs

How is AI being used in retail stores?

Retailers are using AI to support areas such as workforce scheduling, demand forecasting, inventory management, pricing and store performance analysis. In many stores, AI tools are designed to improve operational efficiency, reduce manual tasks and help managers make faster, more informed decisions.

What do frontline retail workers think about AI?

The Talking Shop 2026 research found that many frontline retail workers are cautiously optimistic about AI. While some employees are concerned about job impact or unclear implementation, many are positive about AI tools that solve practical problems such as scheduling, training, stock management and workload visibility.

What helps digital and AI adoption succeed in retail?

Successful retail AI adoption depends on clear communication, frontline involvement, practical training and technology that solves real operational problems. Retail employees are more likely to engage with digital tools when they understand the benefits and can see how the technology improves day-to-day work on the shopfloor.