For years, customers have relied on search, filter and scroll to discover and buy products. That behavior is now shifting: with agentic AI, shopping is becoming less about finding products and more about expressing intent. Instead of typing keywords or browsing categories, consumers describe what they want — a meal plan for the week, an outfit for a wedding, a gift for someone who already has everything — and AI agents translate that intent into product choices. They compare options, prices and increasingly influence or execute the purchase itself.
A report from McKinsey estimates that by 2030, agentic commerce could drive close to $1 trillion in US retail revenue, outlining a near future where AI agents, not humans, initiate a large share of customer interactions.
As retailers explore how to use agentic AI in their own operations, the challenge isn’t just about adding another digital interface, but about whether their underlying technology can support this new way of shopping.
Below are four ways retailers can adapt their operations and technology to succeed in the era of agentic commerce.
1. Keep online and in-store information in sync
Customers use AI agents both to make purchases quickly and to research and compare options before deciding where and how to buy, often moving between digital and physical channels along the way.
To support this kind of journey, retailers need their online and in-store operations to run on the same information. If systems aren’t connected, AI agents cannot accurately reflect what is actually happening in stores, such as which products are in stock, what prices apply, or which promotions are currently available. Even small inconsistencies can result in misleading suggestions or customers arriving in-store to find that products or offers do not match what they were told.
When online and in-store systems are aligned, retailers can ensure that AI agents have the accurate, up-to-date information they need. Real-time product availability, consistent pricing and promotions and clear product details allow agents to guide customers confidently, whether the purchase happens online or in person.
2. Bring all customer data into one place
Agentic commerce depends on customer context just as much as product information. To recommend the right products or next steps, AI agents need to understand who the customer is, not just what is available.
When customer data is spread across multiple systems that don’t communicate, AI agents can’t see the full picture. Purchase history, preferences and past interactions become fragmented, making it difficult to understand intent or deliver relevant experiences.
By consolidating customer data into a single, connected view, retailers make it easier to personalize every interaction. Real-time access to customer profiles, loyalty status and purchase history allows AI agents to tailor recommendations, apply the right discounts and surface relevant promotions based on each customer’s actual relationship with the brand.
3. Use real-time data to automate decisions
Agentic commerce isn’t just about customer-facing AI. It also depends on AI agents that can support decisions in the background, where availability, pricing and fulfillment are determined.
As customers rely on AI agents to guide their purchases, expectations around speed and accuracy increase. If inventory is low, prices are outdated, or fulfillment capacity is unclear, customer-facing agents can only make limited or unreliable recommendations.
With real-time sales and inventory data in place, AI can support decisions in the background — predicting which items are likely to sell out, automatically sending purchase orders to vendors, or adjusting prices based on demand.
This level of automation allows retailers to respond to customer intent as it happens, rather than after the fact. It ensures that what AI agents promise to customers is actually supported by what the business can deliver.
4. Build on a unified data foundation
Fragmented data is one of the most common barriers to getting real value from AI. When retailers rely on separate systems — for example, one for POS, another for loyalty and an eCommerce platform that operates independently — AI agents are forced to work with incomplete or inconsistent information, which limits their potential.
To support agentic AI effectively, retailers need a unified data foundation: a single source of truth that is accurate, complete and updated in real time. This allows AI systems to base their decisions on the same information across the business and deliver more reliable results.
Platforms like LS Central provide this unified foundation from day one, delivering real-time sales, inventory and customer data across the entire business on a single database. When the AI has access to this information, it can make smarter recommendations, enable faster automation and scale consistently across stores, warehouses and digital channels without your teams having to worry whether the information is accurate.
With a unified data foundation in place, retailers can confidently leverage agentic AI across every part of their business and better support the customer journey.
