Artificial Intelligence: How AI is Reshaping Physical Retail

In-store Technology

Fashion retail is a fast-paced, high-stakes business where success hinges on a retailer’s ability to anticipate demand, keep operating margins down, and exceed the ever-rising expectations for seamless, personalized and convenient shopping experiences. 


A new convergence of digitization, data, and AI-led solutions can drive a transformative shift in market share, operational efficiency and staff and employee experiences. 


In this deep dive, we examine AI in retail: how it’s being used, its advantages, and its increasingly pivotal role in reshaping brick-and-mortar retail while seamlessly bridging the gap between digital and physical realms. 

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What is AI in retail?

Artificial Intelligence (AI) refers to cutting-edge technologies that can process and independently analyze vast troves of data, perform autonomous analysis, make predictions, offer insights, or generate textual or visual assets. 


Many retail solutions that are powered by machine learning and other AI algorithms are used to optimize sales, boost inventory management and accuracy, and automate retail processes.


Note: while Brands are starting to use generative AI across many fields, from designing new collections to generating ads (like the cover image of this blog post) our primary focus here is on AI applications within the physical retail landscape.

Benefits of AI in the retail industry

The global AI in retail market size was valued at $5.50 billion in 2022 and is expected to reach $55.53 billion by 2030.

$10 Billion – Global spending on AI in the retail supply chain by 2025

40% of retailers see decision-making as the main benefit of AI.

44% expect it will boost productivity.

43% think it will boost revenue.

Source: SPD Technology


Advancements in artificial intelligence and machine learning solutions are already helping retailers boost their bottom line through greater quality and precision in inventory placement, pricing, and matching products to customers at the store level. But it’s critical to remember that AI is only as good as the data it feeds on.

And up until now, that’s been just inventory and sales data. AI and other analytics solutions never took into equation what didn’t sell – and why – or what could have sold more. Only by analyzing customers’ entire in-store shopping journey – per store – can we achieve more accurate forecasting. 

For example, when stores can track how often a style is picked up, tried on, or abandoned in the dressing room, track item movement in real time, real-time location tracking software connected to an AI-led data analytics solution, it offers new visibility and insights on a daily level.

Until recently, AI solutions never took into equation what didn't sell - and why - or what could have sold more.

AI-led data collection that can identify item-level tags on inventory can pick up various shopper behaviors that are part of the sales funnel, recording impressions, intent, abandonment, and conversion.


These can be applied to everything from improving store localization and inventory balancing, to trimming operating margins and faster decision making across the chain.

So What do we mean by 'impressions' and 'engagement'?

Online, impressions reflect the number of people who view a web page. In physical stores, it refers how many customers stood in front of a display and for how long.


Real-time tracking AI models, coupled with real-time tracking solutions like NanoBT, can count these in-store “impressions” and retailers can view data, insights, and heat maps. The information can help them optimize sales per square foot and store layout, localize assortment, and ultimately increase sales.


‘Engagement’ with merchandise can mean different things, depending on how sensitive the store’s technology is to item movement.

Measuring a style’s ratio of engagement to sales per store helps retailers pinpoint stages in the customer journey where they can give a ‘push’ that will help lead to a sale: having a store associate walk over to assist, or highlighting a high-value style with low-engagement on display.

The hidden in-store customer behavior data: impressions, engagements, intent, and conversion

Speed up decision-making

AI-based recommendations help decision-makers – from store and regional managers to retail executives at headquarters – speed up decision making and create operational efficiencies that can improve store KPIs.


For example, AI algorithms can use in store merchandise ‘engagement’ data to predict where items will sell, and where they won’t (even before there’s been a single sale) and send recommendations to stores to transfer inventory in or out, well before retailers have the sales data they need to make such a call. 

Optimize Pricing and Demand Forecasting

AI-driven models can quickly integrate new information and predict changes in customer demand to prevent costly gaps between supply and demand.


Pricing – when retailers have in-store customer behavior data, then AI software can increase margins by reducing retailers’ reliance on markdowns.


For example, products with consistently low ‘abandonment’ rates might only need a small discount to push customers to buy them, while high-abandoned items will probably need a larger discount.


Demand Forecasting – Retailers today realize that they can use in-store tech to get the same level of insights they have for the online customer journey and use it to enrich their understanding of in-store customer behaviors.


These additional customer touchpoints coupled with machine learning can give retailers accurate predictions for demand per region and even stores, supporting more targeted assortments and inventory balancing.


Artificial Intelligence and Fashion: How AI is reinventing Retail

Too many brands forget (or don’t have the data they need) to tweak assortments to match local shopping preferences.


Imagine AI as a scale that weighs exactly which products, and how much of them – then spits out detailed recommendations on what to stock per store, how much of each style to stock on each shelf, and which category to display in each zone of the store. 


Real-time item-tracking solutions give retailers visibility into how customers interact with each item and display in the store. An AI-powered platform layered connects the dots within the data to predict what local customers want and need down to the color and size levels.

Money Maps to Monetize Sales Per Square Foot

Where will placing your new collection in the store generate the most sales? Which display should be reserved for season staples?


Some AI tools can generate ‘money maps’ per store. Unlike ecommerce ‘heatmaps’, they don’t just track where your shoppers go, but how your merchandise converts to sales (or doesn’t)  based on location in the store.


Money maps can help retailers understand which styles should be placed together to ‘convert’ more, and help inform visual merchandising strategy by highlighting how different displays or collections contribute to store sales. 

Enhance In-Store Customer Experiences

AI-based solutions in retail stores are helping create winning formulas by localizing item offerings, eliminating checkout lines, and promoting experiential retail. 


Mobile-self Checkout and self-returns


What do younger shoppers hate most? according to one recent report, it’s checkout lines.

Retailers are listening, and they’re getting rid of checkout lines and cashiers with a machine learning that feeds off of in-store shopping data. Capabilities like Amazon’s “just walk out” tech solve this with RFID that scans shoppers or their credit cards at the store exit.  


Other solutions use Bluetooth-enabled tags like NanoBT and AI-based data collection. Shoppers can scan items on their phone to see product details, use virtual store maps to find the style or size they want, and – when integrated with mobile self-checkout solutions with EAS security – kiss checkout lines goodbye.  

RFID vs NanotBT

Phygital, frictionless shopping experiences


Next-gen solutions are using AI and machine learning to pinpoint inventory location in stores with incredible accuracy.

Customers can view product information on their phones, verify authenticity, scan fashion trends, and more.


Enhanced personalization based on AI analysis of customer data can provide curated recommendations, promotions and new experiences across physical and digital stores. 

Supporting the Retail Workforce

AI in retail stores promotes optimized efficiency and overall cost reduction, such as through automated inventory control. 


  • Inventory Automation: data collection methods like Angle-of-Arrival (AOA) use artificial intelligence teamed with item-level tags to pinpoint exact item location. This Real-time inventory accuracy can completely eliminate manual stock counts and make gap investigations a breeze for store staff. 


  • Always-available products – Choosing how many of each style, color and size to have on display at any given time is part statistics, part guessing game. AI solutions that capture how often customers look at, and try on different items, while tracking on-shelf availability, can produce replenishment recommendations that optimize shelf space and make sure a sale is never lost due to missing sizes or colors on display. 


  • Inventory Control: With AI-driven predictive and tracking tools, stores don’t have to carry overstock to avoid stockouts or carry safety stock for omnichannel fulfillment. 
  • Real-time Location and Performance: Item-level tagging enables store staff to instantly see item locations on their app, serving shoppers better and effortlessly fulfilling orders such as BOPIS. Strong Connected REtail platforms offer tools for store and merchandiser teams to optimize everything from merchandise display location in individual stores to on-shelf availability, while providing dashboards for headquarters to monitor compliance.

Personalized Customer Engagement

AI technologies and ML can support brands and their customers in multiple ways:


  • AI-supported interactive tools: Interactive chatbots and generative AI assistants can conversationally answering common questions and handle complaints efficiently and even empathically. 


  • Using data to upsell: AI has the power to identify shopping trends and behaviors and turn them into opportunities to increase basket size. Let’s say AI analysis shows that a specific skirt converts better when tried on with a particular shirt. Store staff can pair them together on display or, when paird with real-time tracking, get automated suggestions on time to upsell to shoppers when they’re in the fitting room.


  • Meaningful interactions: Using AI and automation to cut out repetitive inventory tasks frees store associates to interact with customers and act as brand ambassadors.

Upgrade Store Security

AI-integrated product location and theft patterns can identify suspicious activities and immediately alert store associates.  Some traceability tags like NanoBT have an anti-tamper feature that alerts staff if someone tries to take them off for an additional layer of security.


To learn more about NanoBT tags and Loss Prevention, read our blog on next-gen anti-theft systems for retail.

What Technologies and Solutions Are Used for AI in Retail Stores?

Item-level traceability – RFID has been around for decades and is a hot item with retailers that want a more accurate picture of their local inventory. And it works: today, most RFID solutions tout a 97 or 98% inventory accuracy rate.


But research shows that factors like incoming and outgoing stock, lost goods, and handheld inventory scanning lead to inaccurate merchandise data – and RFID-based inventory management solutions often don’t take this into account.


A new generation of tracking solutions use AI algorithms via fixed readers to pinpoint the exact location of each item, within a meter. Battery-free bluetooth-enabled tags can transfer larger and more frequent packets of data than RFID. This opens a door for accurate, real-time inventory location, and with it endless possibilities for gaining new value with AI. 

The future of AI in retail

When retail stores are limited to outdated and often inaccurate inventory and sales data, no AI technology is enough to digitize physical retail and optimize it. What AI needs is new data points. 


In 2024, expect store investments to focus on gathering real-time data – inventory and customer engagement – and AI-powered solutions that can interpret customer behavior data and convert it into deeper localization, higher margins, operational excellence, and stronger relationships between brands and customers.

AI - a new staple in fashion retail

The retail store of tomorrow is already here. Early adaptors are incorporating AI-driven tools to reinvent their stores and create immersive customer experiences. Patented technologies like Connected Merchandise are already streaming data and producing transformational results that touch store revenue, inventory accuracy, and employee satisfaction.


As brands continuously try to stand out and even reinvent themselves while getting closer to their customers across all channels and storefronts, AI-supported cloud platforms will become a key part of supporting retail initiatives.

How does AI improve the shopping experience for customers?

Innovative AI-powered applications can orchestrate connected shopping experiences across all channels. In physical stores, customers can get detailed information about merchandise and personalized recommendations based on market trends and past purchases, enhancing the shopping experience.

AI-led solutions connected with item-level tracking can support frictionless mobile checkout and returns.

How does AI help in inventory management?

AI can streamline inventory management through accurate demand forecasting, more accurate size curves per store, prioritizing replenishment and other tasks based on the stock value, and helping balance inventory across the chain in real time. Other AI-driven systems are also expected to affect inventory management from production to store, such as AI-based robots in warehouses.

Is AI expensive to implement in retail?

AI solutions vary wildly in pricing, often based on how they collect data. To reduce the impact of an AI solution on operating margins, retailers should first identify all relevant use cases across the organization and select the AI solution that provides the most value across the entire business.