Lori Schafer in Retail Today: AI’s Greatest Impact Is the Part Consumers Don’t See

Read the article in Retail Today here.

In a recent report from Bain & Company, half of the consumers surveyed said they’re not comfortable with letting autonomous AI agents make their transactions. In the same study, roughly a third of the consumers said they use AI to research products.

Together, these results show that consumers aren’t exactly clamoring for AI to play a visible role in their shopping experiences. They also demonstrate that retailers can take some time developing more consumer-facing experiences powered by generative and agentic AI.

In the year ahead, agentic commerce will be driving conversations. But it’s important to remember that behind the scenes, AI is already delivering meaningful, measurable impact when applied to core operations: AI can help retailers optimize assortments, purchase orders, inventory levels, pricing, and promotions. What consumers often don’t realize is that these improvements directly shape their customer experience, perhaps more so than any innovation they can see.

AI-Native Intelligence: Retail’s Invisible Engine

For the last decade, retailers have invested heavily in developing consumer-interactive, vibrant omnichannel shopping experiences, focusing on mobile apps, dynamic e-commerce sites, curbside pickup, and loyalty programs. But, in the years to come, retailers could be defined more by what consumers don’t see.

AI-native intelligence and agentic systems work behind the scenes, as an invisible engine of modern retail. These autonomous agents coordinate decisions, collaborate with multiple agents across departments, reason, and make decisions that improve outcomes across the business.

Before retailers put too much emphasis on how innovative AI can transform marketing and sales, the tech should first boost operations. By unifying data across the organization and empowering AI to support core workflows, companies can accelerate execution and improve profitability in areas such as:

1. Manage purchase orders

Traditionally, purchase order (PO) management starts with a handshake between retailers and suppliers, settling on product amounts and deadlines. But when AI continuously monitors a PO and updates it in real time, adjusting for changes in available materials, tariffs, or other variables, the accuracy of orders improves and changes don’t interrupt the business.

For instance, if a region of stores receives fewer raincoats than planned because a PO process hasn’t been updated in time, the impact shows up quickly in margins and on store shelves. AI can close that gap before consumers ever notice it.

2. Curate assortments

AI agents that are embedded into a supply chain system with access to enriched, real-time internal and external data sources can autonomously adjust assortments at stores based on recognizable patterns. For example, local weather patterns, returns data, and in-store sales performances might prompt an agent to make broader assortment changes, directly impacting assortments at the store level. The decisions will optimize performance of the products for each location.

3. Reconcile inventory in real time

Much like optimizing a PO and adjusting store assortments, AI agents can dynamically adjust inventory levels. A supermarket anticipating a spike in soda sales tied to a big football game that’s happening can use AI to reallocate stock from neighboring stores, adjust replenishment orders, or recommend immediate markdowns on items to make space for additional cases of soda.

4. Leverage dynamic pricing

A single, trusted layer of data is essential for accurate pricing, but it also enables AI agents to take action on dynamic pricing. Pricing agents can be trained to adjust for margin, competitive positioning, price elasticity, and inventory risk. The system can also create short-term promotions and adjust prices in-store and online, without waiting for a weekly pricing review.

AI agents boost associate engagement

Another hidden benefit of AI agents working in the background is it can free up store associates and staff to handle more customer-facing tasks. Rather than spending time fixing store layout issues, associates can be available to assist shoppers.

Retail operations supported by unified data and an agentic AI layer move faster and operate more efficiently. Decisions are made sooner, overstocks and stockouts decline, margins improve, and consumers experience greater consistency across channels.

Over time, these gains compound, strengthening the AI itself, elevating operations, and building consumer loyalty.

AI empowers a fully coordinated operation

Agentic AI has a clear potential to transform how consumers interact with retailers. Automated, knowledge-rich agents are already handling customer service requests or assisting shoppers with online searches, but it’s still in the early stages.

Behind the scenes, the impact is more immediate. An interconnected network of agents such as merchandising agents negotiating with supply chain agents, and pricing agents coordinating with marketing agents, creates consistency and accuracy across the organization.

Consumers may not see this coordination at work, but they will feel it. The result is less friction, more availability, and a more reliable experience at every point of the interaction.