AI Agents in Retail: Your Complete Guide

Traditional retail automation is quickly becoming outdated, as the future of retail points to AI agents. Whether it’s helping a shopper find their size at another store, answering questions after customer service has closed, or flagging a stock shortage before it costs sales, AI agents can help retailers handle everyday demands, giving teams more time to focus on customers and growing the business.

AI agents’ usefulness in retail, however, depends on access to the right data and systems. Siloed systems make it difficult for agents to access the inventory, purchase history, and order details they need to act autonomously and accurately.

This guide covers how AI agents in retail work, where retailers can put them to use, and how connected point of sale (POS), inventory, order management, and clienteling systems support retail AI agents.

What Are AI Agents in Retail?

AI agents in retail are software systems that understand requests, make decisions, and complete tasks for businesses, store associates, or shoppers without someone guiding every step. Unlike a traditional chatbot that follows a fixed script, an agent can determine which information it needs, consult connected systems, and take action within its permissions.

For example, a shopper might ask to exchange a jacket purchased online for a different size. A chatbot could explain the retailer’s exchange policy, while a retail AI agent could retrieve the order, check exchange eligibility, and confirm whether the desired size is in stock. With the right permissions, it could then initiate the exchange, provide return instructions, and let the shopper know when to expect the replacement.

Types of AI Agents in Retail and The Information They Depend On

Retail AI agents can be grouped into five main types based on the work they support:

• Shopping agents help customers compare products, check availability, and find items that fit their needs.
• Customer service agents handle order inquiries, returns, exchanges, and other requests within the retailer’s policies.
• Associate agents retrieve product information and customer context to help store teams offer recommendations and prepare follow-up outreach.
• Inventory and supply chain agents monitor stock and recommend replenishment or transfers to maintain availability across locations.
• Merchandising and pricing agents recommend assortment changes, prices, promotions, and markdowns based on product performance and demand.

Regardless of their role, agents need accurate, accessible data to understand a request and decide what to do next. Each type of retail data helps answer a different question:

• Product data helps agents identify items that match a shopper’s needs. Names, materials, sizes, colors, pricing, and promotions allow them to compare options and make informed recommendations.
• Inventory data tells agents whether an item is available to sell and where it can be found. It needs to account for reservations and other commitments so agents don’t offer stock that’s already spoken for.
• Transaction data tells agents what a customer bought, returned, or exchanged, where, and at what price. In-store purchases add context that online activity alone may miss and help agents verify purchases for service requests.
• Order data helps agents track what happens after a purchase, including fulfillment status, pickup readiness, and delivery progress. It also helps them check whether a requested change is still possible.
• Customer data helps agents make interactions more relevant. Purchase history, preferences, and associate relationships can inform recommendations, while communication consent determines whether a customer can be contacted.

POS, inventory, order management, and clienteling systems provide different parts of this context, so it’s vital for AI agents that the systems are connected.

Use Cases for AI Agents in Retail

AI agents can support retail tasks ranging from product discovery and customer service to inventory planning and personalized outreach. Each use case requires access to different information and tools. The following use cases show how agents can use connected retail data to help customers shop and businesses manage service, inventory, fulfillment, and sales.

Virtual shopping assistants

Virtual shopping assistants help customers find products based on their needs, preferences, and budget. An agent can ask follow-up questions, compare options, and help assemble a basket rather than requiring shoppers to navigate product filters themselves.

For example, a customer looking for a jacket for an upcoming trip could specify the climate, preferred material, and price range. The agent could identify suitable options, check availability in the requested size, and present delivery or store pickup options.

Customer service, returns, and exchanges

Customer service agents can answer routine questions and take permitted actions, such as initiating a return or requesting an exchange. They can also pass complex requests to a person with the relevant context, reducing the information customers need to repeat.

POS data helps extend that service to purchases made in-store. An agent supporting an exchange could retrieve the original transaction, verify the price paid, check the retailer’s policy, and look for replacement stock.

Inventory planning and replenishment

Inventory agents can monitor stock levels alongside sales patterns to flag potential shortages or excess stock. Depending on their permissions, they can recommend replenishment, propose transfers, or submit approved requests.

For example, an agent could identify a jacket selling quickly at one store while the same style remains available at another location. It could recommend a transfer after checking each store’s stock requirements and expected demand.

Order tracking and fulfillment support

Agents can help customers check delivery progress, explore pickup options, or investigate an order that has not arrived. To provide useful answers, they need to understand the order’s current status and what actions remain possible.

An agent handling a delivery inquiry could check whether the order is awaiting fulfillment, partially shipped, or already with a carrier. If a customer requests a change, the agent would need to check whether the order is still eligible before submitting it.

The order management system (OMS) provides visibility into fulfillment progress and the locations responsible for completing the order. Connected inventory information helps agents evaluate alternatives when an item becomes unavailable.

Personalized associate assistance and outreach

Agents can help store associates prepare for appointments, retrieve customer context, and identify relevant follow-up opportunities.

With access to clienteling data, an agent could suggest pieces that complement a customer’s previous purchase, check availability in their preferred size, and draft a message for their associate to review.

Purchase history, stated preferences, and previous conversations make the suggestion more relevant. The associate can refine the recommendation using their knowledge of the customer and continue the relationship.

Pricing and promotion recommendations

Agents can analyze product sales, margins, inventory, and promotion results to recommend pricing or promotional changes. Retailers can require approval before those changes take effect.

For example, an agent could flag a seasonal item with slow sales and excess stock, then recommend a targeted promotion for review. Before applying an approved change, connected pricing and POS systems need to reflect its scope, timing, and eligibility so customers receive consistent information through checkout.

How To Implement Retail AI Agents

Start with a task you want to improve, such as helping customers track orders or helping associates find available stock. Then identify the information the agent needs, the systems it must connect to, and the actions it should be allowed to take. This gives you a practical starting point for evaluating agents and preparing your technology.

Understand what happens when data is incomplete

Imagine a shopper asking whether a pair of shoes is available for pickup. An agent checks yesterday’s inventory and says yes, even though the last pair sold that morning. The shopper arrives expecting to collect it, and the store team has to explain what went wrong.

Preventing that experience requires more than a better answer from the agent. Sales and reservations need to update sellable inventory, and the agent needs to check that availability before making a commitment. If it submits a reservation, it should wait for confirmation before telling the shopper the shoes are held.

That’s why integrated systems are critical. POS, inventory, order management, and customer information need to stay connected so agents can act on what’s happening across the business.

Check whether your systems are ready

Before introducing an agent, assess whether your technology can support the work you want it to do:

• Can the agent securely access the information it needs and submit permitted actions through APIs?
• Do sales, returns, and reservations update inventory quickly enough to support accurate availability checks?
• Can product, order, and customer information be matched across stores and online channels?
• Can purchase history and clienteling preferences be brought into the interaction with the right access controls?
• Can your systems enforce return policies, approval limits, and other business rules?
• Can they confirm that an action succeeded and pass the request to an employee when something goes wrong?

Integration doesn’t mean every system has to come from the same vendor. For example, NewStore connects POS, inventory, order management, and clienteling capabilities while also supporting external integrations.

Example of AI Agents in Retail

Retailers are putting AI to work both online and on the sales floor. For example, Lowe’s introduced Mylow Companion to associates across more than 1,700 stores in 2025. Associates can use it on their sales-floor devices to find product details, get project advice, and check inventory while helping a customer.

The associate stays in the conversation, with information close at hand to answer questions and guide the purchase. Mylow Companion focuses on helping employees serve customers rather than completing transactions independently.

Build a Connected Foundation for Retail AI Agents

Getting value from AI agents starts with the systems your brand already relies on. Accurate inventory, accessible purchase history, and connected order and customer information help agents make useful recommendations and take actions your business can follow through on.

NewStore brings POS, inventory, order management, and clienteling together while supporting integrations with external systems. That gives retailers a foundation to build on as they introduce agents into their operations.

Retail AI Agents FAQs

Can AI agents work across online and physical stores?

Yes, provided the systems behind those channels are connected. Agents need access to consistent product, inventory, transaction, order, and customer information to support requests that cross channels, such as finding an online shopper’s size at a nearby store or helping with an in-store return of an online purchase.

How can retailers protect customer data when using AI agents?

Give agents access only to the information and actions their role requires. Authenticate customers before sharing purchase details, respect communication preferences, and enforce permissions through the connected systems. Retailers should also review how providers handle customer data and keep logs so teams can investigate unexpected activity.

Can AI agents replace store associates?

AI agents can help associates find information, prepare recommendations, and handle routine tasks, giving them more time for customers. But premium service depends on the relationships associates build: remembering a conversation, understanding an occasion, or helping someone discover something they hadn’t considered.

Personal connections remain central to the store experience. Agents can bring purchase history and preferences into the conversation, while associates use that context to offer thoughtful advice and give customers a reason to return.

Do retailers need to replace their existing systems to use AI agents?

Not necessarily. What matters is whether those systems can share accurate information and support authorized actions through secure integrations. Retailers should assess how their POS, inventory, order management, and clienteling systems work together, then address the gaps that would prevent an agent from completing its tasks reliably.