Agentic AI in Retail, From Supply Chains to Shoppers

Why Retailers Are Turning to Agentic AI

Retail has long been a high-volume industry with a continuously changing market landscape. Buyers expect real-time personalization, with the right products at the right price and the right offer. Meanwhile, managing inventory and hundreds of other multi-location needs requires proven expertise and tight coordination to handle competing priorities efficiently.

This is the reason agentic AI investments are rising, with businesses moving beyond pilots to production deployments across supply chains, revenue operations, and customer experience functions. For instance, in NVIDIA’s 2026 survey, 90% of retail and consumer packaged goods (CPG) respondents planned to raise their AI budgets in 2026.¹

With their capability of managing complex, multi-step retail workflows with minimal to no human intervention, AI agents are helping retail businesses globally cut costs and move faster. They make the biggest difference wherever several teams, systems, and partners have to work in sync. That shows up most in the supply chain, supplier management, promotions, and customer data. Let’s look at each in more detail.

Hyper-Efficient Supply Chain Management

Modern supply chains involve several omnichannel operations and complex workflows that require coordination, communication, and decision-making between multiple teams, systems, and external partners. Over the past year, challenges in this domain have grown, as 64% of retail and CPG respondents noted in NVIDIA’s 2026 survey.¹ Geopolitical instability, labor constraints, rising expectations of speed and transparency, and complex rules across global markets all play a part.

Agentic systems can reduce much of the friction and miscommunication involved in manual tracking and coordination. When a port delay or labor shortage hits, the system can adjust plans without waiting for every team to catch up.

For instance, agents can analyze data from multiple sources in real time, including market trends, product performance, and supplier performance. They can then provide accurate recommendations and even trigger actions that would enhance operations, such as reordering stock or rerouting shipments.

Further, collaboration between agents dedicated to demand forecasting, inventory optimization, supplier risk management, logistics and route optimization, reverse logistics, and other functions maximizes cross-functional outcomes. A good example can be a reverse logistics agent that coordinates collection with transporters, delivery personnel, and inventory teams to complete product returns.

The same coordination challenges extend beyond the retailer’s walls to the suppliers who keep their shelves stocked.

With supply chain pressure rising for nearly two in three retail and CPG firms, every manual hand-off has become a risk worth removing

Smarter Supplier Onboarding and Negotiation

Supplier onboarding involves more than just evaluating vendors. Retailers need to assess factors such as supplier capabilities, purchase patterns, prices during specific market cycles, and dependencies. This is where agentic systems come in. From managing contracts to analyzing past purchases, AI agents speed up the work and help retailers make more cost-effective supply-related decisions. An analyst quoted in NVIDIA’s 2026 retail survey report expects agentic AI to make its first big impact in supply chains and operations, including vendor negotiations, because that is where returns are easiest to measure.¹

For example, to support data-driven supplier negotiations, agentic AI can:

• Simulate negotiation scenarios
• Assess onboarding-specific metrics such as budget and risk tolerance
• Learn from live scenarios to improve future negotiations
• Suggest counteroffers and discounts during talks with suppliers
• Create supplier-specific strategies

Once the right suppliers are in place, the next test is whether promotions land as planned in every channel.

Consistent Promotions, Merchandising, and Branding

Managing promotions, merchandising, and branding consistently across POS, e-commerce, pricing, inventory, and promotion-tracking tools can be challenging. Maintaining consistency across the board involves several teams and variables and often creates data and execution gaps. Traditional monitoring and auditing methods take a long time to detect such gaps, which ultimately lowers the possibility of campaign success. Agentic automation helps avoid that.

Here’s an example: A dedicated agentic AI can monitor promotions across multiple systems, check for anomalies, and identify reasons why a campaign may not be working with customers. It can also detect execution gaps, trace their root causes, and suggest corrective actions. If a product with a promotional discount is not sold in certain stores, the agent can identify the discrepancy and initiate remediation. Promotion data is not the full story. Retailers collect lots of customer information that also needs to be part of the master data.

A promotion that stalls in a handful of stores can go unnoticed far longer than any campaign can afford.

A Connected View of Customer Behavior

Retailers collect customer data across multiple touchpoints, such as product searches, browsing activity, purchases, returns, reviews, and service interactions. This information helps them create personalized marketing programs, targeted selling strategies, and attractive promotions that drive purchases. However, the details are often analyzed separately, making it difficult to decipher broader patterns in customer intent and behavior.

Retailers recognize the problem. In Salesforce’s 2025 Connected Shoppers Report, 86% said they had unified commerce initiatives underway to connect their channels, customer records, and operations.² AI agents help them build a more holistic, dynamic customer profile by connecting data and analysis across multiple data points.

Post-purchase behavior shows how this works. Dedicated agents assess customer actions like repeat purchases, return history, product reviews, and service interactions to build a dynamic customer profile. Once they have studied these signals, the agents can feed insights into their product R&D, marketing, and pricing departments for prospective future improvements.

Use cases like these are already within reach. The bigger shift ahead is in how agents work with each other.

Some of the most useful customer insight arrives after the sale, in returns, reviews, and repeat orders.

What Comes Next for Agentic AI in Retail

So far, most AI systems work inside a single retailer’s walls, but the two capabilities that can take them further are agent-to-agent commerce and AI-led reverse logistics.

Soon, many retail conversations may happen between agents alone. Today’s AI tools mostly work on websites and apps built for people, but that is likely to change. Agents acting for retailers, suppliers, customers, and carriers could coordinate autonomously and negotiate with minimal human intervention. McKinsey estimates that, in a moderate scenario, agentic commerce could orchestrate $900 billion to $1 trillion of US consumer retail revenue by 2030.³

Reverse logistics may be the toughest proving ground. Its highly variable, time-sensitive operations need models and agents that can operate across several workflows at once with fewer errors. US retailers expected 15.8% of 2025 sales, about $849.9 billion, to come back as returns, according to the National Retail Federation.⁴To prepare for that, the latest AI systems are trained using vast amounts of transaction data. Case in point, an agent trained using a million receiving events will make faster, better decisions for retailers than one trained with 50,000 events.

Retailers can lay the groundwork for both now. The sensible place to begin is a use case like promotion monitoring. When a discount fails to show up in a few stores, both the problem and solution are easy to spot. Results like that make a wider rollout much easier to justify.