Retail AI Agents: Types, Examples, How To Automate

Artificial intelligence (AI) is reshaping the retail environment, and retail AI agents are at the center of the transformation. These intelligent systems can act, learn, and make decisions on behalf of retailers, driving automation, personalization, and operational efficiency across every part of the business. 

Whether you're a small business owner, a team leader in operations or marketing, or a new data analyst trying to explore AI’s impact on retail, understanding how AI agents work is the first step to using them effectively.

In this guide, we go into what retail AI agents are and how they work. We’ll take a look at the different types available and how you can begin to implement them in your business today.

What is a retail AI agent?

An AI agent is a software program, powered by AI, that performs tasks traditionally done by humans. These agents can operate on their own, make decisions based on data, and improve their performance over time. In retail, AI agents are used across areas like customer service, inventory management, product recommendations, and pricing optimization.

Unlike basic automation tools, AI agents don't just follow static rules. They learn from context, adapt to changes, and respond intelligently to inputs. They’re like digital coworkers that can scale to handle thousands of interactions and decisions simultaneously.

How retail AI agents work

Retail AI agents combine several technologies:

• Machine learning (ML) models analyze past data to make predictions (e.g., demand forecasting).
• Natural language processing (NLP) helps agents understand and respond to human language (used in chatbots and virtual assistants).
• Computer vision powers visual capabilities (e.g., scanning shelves or analyzing customer behavior).
• Rules engines and decision frameworks ensure the agent acts within business goals and constraints.

These components work together to process input (from data, cameras, or user interactions), decide what action to take, and execute that action through connected systems like CRMs, ERPs, or customer-facing apps.

Benefits of retail AI agents

Retail AI agents offer significant value across operations, marketing, and customer service. Their biggest advantage is their ability to act autonomously and intelligently at scale, handling tasks that would otherwise require large teams, extensive hours, or expensive systems. Here's a closer look at the core benefits:

Operational efficiency and cost savings

AI agents can automate repetitive, time-consuming tasks like demand forecasting, stock reordering, and customer support ticket routing. This reduces reliance on manual labor, cuts operational overhead, and improves consistency.

Always-on customer support

AI agents—especially those using natural language processing—can handle thousands of customer interactions simultaneously, 24/7. This reduces wait times, ensures consistent service, and allows human agents to focus on complex cases.

Personalization at scale

Recommendation agents analyze user behavior in real time and deliver individualized suggestions across web, mobile, and in-store experiences. This drives higher engagement, greater customer loyalty, and increased average order values.

Improved forecasting and inventory accuracy

By continuously analyzing historical trends, external factors (like seasonality or weather), and live sales data, AI agents improve forecast accuracy and reduce surplus inventory and markdown costs.

Smarter, faster decision-making

AI agents can analyze millions of data points across sales, operations, customer feedback, and marketing to deliver real-time insights that would take teams hours or days to compile manually.

Agility in a dynamic market

AI agents can detect shifts in consumer behavior, competitor pricing, or supply chain status and act accordingly. This gives retailers the ability to respond in near real time—something traditional reporting tools can’t support.

The compounding effect

These benefits don’t work in isolation. When AI agents are deployed across multiple functions—customer service, pricing, supply chain, and marketing—they begin to reinforce each other. Inventory efficiency improves recommendations. Faster decision-making accelerates campaign pivots. Together, they create a more adaptive, intelligent retail operation that grows more valuable with use.

Types of AI agents in retail

Retail AI agents come in many forms, each designed for a specific purpose. Below is a quick reference guide:

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Examples of retail AI agents in action

Retail AI agents aren’t just theoretical; they’re already producing measurable results across some of the world’s leading retail brands. From personalized shopping experiences to autonomous inventory control, companies are using AI agents to solve real business problems and gain competitive advantages. 

The following examples illustrate how different types of AI agents are being deployed in practice, what they do, and the outcomes they drive.

Sephora: Virtual Artist

To assist online shoppers, Sephora launched an AI-powered virtual artist that lets customers try on makeup digitally. Powered by facial recognition and computer vision (via a partnership with ModiFace), customers can virtually apply lipstick, foundation, and eyeshadow in real time, using their device’s camera. This solves the in-store trial barrier and reduces purchase uncertainty.

Amazon: Dynamic pricing AI

Amazon uses AI agents that update product prices in real time, it monitors demand signals, competitor pricing, seasonal trends, and customer behavior to adjust prices multiple times a day based on demand, competitor pricing, and customer behavior. This dynamic pricing approach helps Amazon stay competitive in real time while protecting profit margins during high‑demand periods or promotions.

Lowe’s: AI-powered search assistant

Lowe’s integrated a natural language agent into their mobile app and spatial intelligence agents, including systems using generative AI (via OpenAI GPT‑3.5) and computer vision to power in-app voice or conversational search and dynamic store layout optimization. Voice agents help customers find products more easily within the mobile app, reducing discovery friction. Spatial AI analyzes store traffic and local trends to adjust product placement and inventory in-season—cutting implementation time from months to days and improving customer satisfaction and conversion in store

H&M: Demand forecasting agent

H&M uses AI agents to analyze purchase patterns and environmental data to predict demand more accurately. The AI‑based demand forecasting systems combine machine learning models with historical sales, regional preferences, weather, and macro factors to predict what customers will buy—and where. Integrated with Google Cloud infrastructure, the deployment began in pilot markets and expanded globally. Results include reductions in overstock, lower waste, improved sustainability, and better alignment between product availability and regional demand.

How to automate with retail AI agents

Successfully automating with AI agents in retail doesn't require hiring a team of data scientists or reinventing your tech stack. It does require a structured, outcome-driven approach. Below is a phased roadmap to help your team get started:

Phase 1: Assess your readiness

Start by identifying the best-fit areas for AI. Look for processes that are repetitive, data-rich, and have a clear business goal.

Checklist:

• Where are you losing time or accuracy today? For example, your manual inventory tracking or generic email campaigns.
• Do you have clean, accessible data? For example, a product catalog, sales history, or support tickets.
• Are your tools integrated or siloed?

Tip: Focus on one domain first, such as customer service or product recommendations, where you can show quick wins.

Phase 2: Choose the right tools and approach

Retail AI agents can come prebuilt (via platforms like Domo or Salesforce) or be custom-built using internal teams or third-party vendors. Your choice depends on resources, use case complexity, and integration needs.

Options include:

• Plug-and-play tools: Ideal for smaller teams or fast pilots, like prebuilt chatbots or recommendation widgets
• Low-code/no-code platforms: Let you customize logic without deep technical expertise
• Custom development: Best for proprietary needs or unique workflows

Questions to ask:

• Does this tool integrate with my CRM, ERP, or e-commerce platform?
• Can we control the logic or retrain the models?
• Does it provide transparency into how decisions are made?

Phase 3: Launch a pilot

Select one AI agent type and test it with a small user group or a single store/channel. The goal here is proof of concept, not perfection.

Pilot steps:

1. Define success metrics. Think CSAT, conversion rates, and hours saved.
2. Monitor performance and gather feedback.
3. Make refinements before expanding,

Sample pilot use case:
A retailer launches a product recommendation agent on its website. The team tracks click-through rates, cart adds, and purchase conversion before rolling it out to the app and in-store kiosks.

Phase 4: Integrate, learn, and expand

Once the pilot shows positive results, deepen the impact by connecting your AI agent to more data sources and workflows. This is where AI becomes part of everyday operations.

Ideas to expand:

• Feed in loyalty program data to improve recommendations.
• Use chat interactions to train future customer service responses.
• Connect inventory agents with supplier systems for auto-replenishment.

Important: Build feedback loops. AI agents get better over time—but only if they learn from outcomes (e.g., did a recommendation lead to a purchase?).

Phase 5: Measure and optimize

AI automation is not “set it and forget it.” As you scale, continue to monitor performance and retrain models as your products, customers, and goals evolve.

Key practices:

• Regularly review performance dashboards.
• Adjust KPIs as you expand into new areas.
• Involve frontline teams for qualitative insights.

How AI agents are changing the retail industry

AI agents are not just enhancing how retail works—they're redefining it. From decision-making to customer engagement, these agents are shifting the industry from reactive operations to predictive, intelligent systems that can act in real time. Here's how retail is evolving under the influence of AI agents:

From reactive to predictive operations

Traditionally, retailers made decisions based on past sales reports or manual analysis. AI agents flip this model by enabling real-time and forward-looking decisions. Inventory agents forecast demand weeks ahead. Pricing agents adjust offers dynamically. Marketing agents optimize campaigns mid-flight based on live engagement data.

This shift empowers retailers to move faster, reduce guesswork, and stay aligned with consumer behavior as it changes.

From static experiences to dynamic personalization

Retail used to offer broad, one-size-fits-all experiences. With AI agents, personalization is no longer a luxury—it’s embedded into every touchpoint. From home pages that adapt in real time to customer support that remembers past issues, AI agents allow every interaction to be smarter, faster, and more relevant.

This doesn’t just improve satisfaction; it builds customer loyalty and increases lifetime value.

From siloed teams to connected intelligence

AI agents are breaking down functional silos by serving as connective tissue between departments. A pricing agent can use data from marketing. An inventory agent can pull insights from sales trends. This interconnectedness creates a more responsive and aligned organization.

In effect, AI agents become a layer of cross-functional intelligence that accelerates collaboration and execution.

From manual scaling to autonomous growth

As retailers grow, the challenge has always been scaling people, processes, and technology in lockstep. AI agents make it possible to grow operations—customer support, demand planning, pricing, personalization—without growing headcount at the same rate.

This is particularly impactful for mid-size and digital-native retailers who want to scale quickly but efficiently.

From data-rich to decision-ready

Retailers have no shortage of data—but turning it into timely, actionable insight has always been a hurdle. AI agents remove that barrier by embedding decision-making into the flow of work. They don’t just report what happened—they respond to it, and in some cases, act on it.

The result is a retail organization that’s not just informed by data but powered by it.

A look at Domo's AI retail agent

Domo provides a robust platform of AI and data tools tailored for retail organizations looking to operationalize intelligence across the business. Its AI retail agents are designed to work seamlessly with existing data sources, operational tools, and decision-making workflows—making it easier for non-technical teams to implement automation that delivers measurable results.

Key capabilities of Domo's AI retail agents include:

• Inventory intelligence
  Automate stock tracking and replenishment using real-time sales, inventory, and supply chain data. Domo’s agents detect anomalies, forecast shortages, and trigger actions automatically, helping reduce lost sales due to out-of-stocks.
• Retail performance optimization
  Domo’s platform can surface trends in-store performance, customer behavior, and sales patterns using AI-powered dashboards. These insights allow store managers and merchandizers to take action immediately without waiting for static reports.
• AI-assisted customer experiences
  Connect data from e-commerce, CRM, and loyalty systems to personalize customer engagement across channels. Domo’s agents can recommend products, automate segmentation, and optimize outreach based on behavior and history.
• Flexible deployment across roles
  Whether you’re a retail executive, data analyst, or store operations lead, Domo’s no-code/low-code tools make it easy to configure, test, and scale AI-driven solutions without deep technical expertise.
• Unified platform integration
  With over 1,000 data connectors and built-in governance features, Domo allows retailers to integrate and activate AI agents within their existing architecture—streamlining data access, compliance, and insight delivery.

Retailers looking for a fast, flexible way to implement AI in their business without rebuilding their stack will find Domo’s retail agents particularly valuable. To learn more or explore use cases, visit domo.com/ai.

Retail AI agents aren’t just a trend. They’re becoming foundational tools for modern retail businesses. Whether you’re just starting to explore data or already managing teams with digital goals, understanding and adopting AI agents can open the door to more efficient, intelligent, and responsive retail operations.

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