Industry · Retail

AI agents for retail operations.

Retail's fastest-growing agent work sits in three queues that never empty: customer service, replenishment, and returns. Mirai360 AI builds agents that keep those queues moving continuously, while a person approves every decision that moves money or inventory.

75%

of retailers say AI agents are essential to staying competitive by 2026. Salesforce, 2025.^[1]

40%

of enterprise applications will include task-specific AI agents by end-2026, up from under 5% in 2025. Gartner, Aug 2025.^[2]

$849.9B

of merchandise expected to be returned by US consumers in 2025, a 15.8% return rate, with 9% of returns fraudulent. NRF, Oct 2025.^[3]

How every deployment is framed

Each use case below follows the same discipline: name the workflow, define what the agent does, define what a person approves, and measure the outcome against a baseline recorded before go-live. The threshold rule is simple: an agent can draft, route, and recommend on its own, while anything that moves money or inventory waits for a person.

The workflows agents take on

Inventory replenishment

Workflow: Sensing demand across products and locations, and keeping shelves stocked without over-buying.
What the agent does: Forecasts demand, flags stockout and overstock risk, and drafts reorder proposals with quantities and timing.
What the human approves: The buyer signs off on quantities before any purchase order is released.
Measured outcome: Stockout incidents and inventory-holding value against the pre-deployment baseline. The published benchmark: Alibaba's deployment of AI demand forecasting with inventory optimisation reports US$42M in annual shrinkage and inventory savings, in a peer-reviewed study.^[5]

Customer service and order status

Workflow: Handling "where is my order?", frequent questions, and simple changes across channels.
What the agent does: Resolves routine inquiries from order and logistics data, drafts responses, and escalates edge cases.
What the human approves: A person reviews every escalation and any action touching refunds or customer accounts.
Measured outcome: Resolution time and escalation rate; customer service is the top named agent use case in retail.^[1]

Returns triage and fraud screening

Workflow: Deciding whether each return becomes a refund, a replacement, or an inspection, and catching fraud early.
What the agent does: Scores return legitimacy, recommends a disposition, and drafts the customer response.
What the human approves: Staff approve flagged or high-value returns and confirm any fraud hold before action.
Measured outcome: Fraud-loss rate and returns cycle time; 85% of retailers already use AI to detect return fraud.^[3]

Pricing and markdown proposals

Workflow: Adjusting prices and markdowns as demand, competition, and inventory age shift.
What the agent does: Monitors demand and inventory age, and proposes price or markdown moves with a margin-and-volume rationale for each.
What the human approves: The category manager approves price bands and any change beyond them before it goes live.
Measured outcome: Margin and sell-through against the pre-deployment baseline. The published benchmark: Zara's controlled field experiment with optimised markdown pricing raised clearance revenues by roughly 6%, in a peer-reviewed study.^[6]

Marketing and campaign drafting

Workflow: Producing segment-level campaigns, offers, and content variants on a steady cadence.
What the agent does: Builds audiences, drafts copy and offers, and proposes send schedules.
What the human approves: The marketing lead approves offer economics, brand copy, and audience before launch.
Measured outcome: Campaign output per week and conversion against the pre-deployment baseline.

Order operations and disruption handling

Workflow: Detecting delivery and supply disruptions, and re-planning fulfilment before customers notice.
What the agent does: Flags disruptions, proposes reroutes and substitutions, and quantifies the cost and revenue impact of each option.
What the human approves: The operations manager approves reroutes and substitutions above set thresholds.
Measured outcome: On-time fulfilment rate against the pre-deployment baseline.

What end-to-end agentic automation creates

A retail operation is a chain of queues: inquiries, reorders, returns, price reviews, campaigns. Agents on one platform work all of them continuously against the same stock, order, and customer data, and log every action in one audit trail. The caution matters as much as the upside. Gartner expects over 40% of agentic AI projects to be cancelled by end-2027 on cost, unclear value, and weak controls.^[4] Mirai360 AI's discipline (one workflow first, a recorded baseline, human approval on money and inventory, and pre-agreed scale-or-stop numbers) exists to keep your deployment out of that statistic.

One 30-minute call.

We look at your operation, find the one queue worth starting with, and tell you honestly whether it clears the bar.

Book your free 30-minute call

Sources

Salesforce, "AI agent retail trends 2025": salesforce.com/news/stories/ai-agent-retail-trends-2025/
Gartner press release, 26 August 2025: gartner.com/en/newsroom/press-releases/2025-08-26-gartner-predicts-40-percent-of-enterprise-apps-will-feature-task-specific-ai-agents-by-2026-up-from-less-than-5-percent-in-2025
National Retail Federation, "Consumers expected to return nearly $850 billion in merchandise in 2025," 15 October 2025: nrf.com/media-center/press-releases/consumers-expected-to-return-nearly-850-billion-in-merchandise-in-2025
Gartner press release, 25 June 2025: gartner.com/en/newsroom/press-releases/2025-06-25-gartner-predicts-over-40-percent-of-agentic-ai-projects-will-be-canceled-by-end-of-2027
Deng et al., "Alibaba Realizes Millions in Cost Savings Through Integrated Demand Forecasting, Inventory Management, Price Optimization, and Product Recommendations," INFORMS Journal on Applied Analytics 53(1):32–46, January 2023: ideas.repec.org/a/inm/orinte/v53y2023i1p32-46.html
Gallien & Caro, "Clearance Pricing Optimization for a Fast-Fashion Retailer," Operations Research 60(6):1404–1422, 2012 (controlled field experiment, all Zara stores in Belgium and Ireland): lbsresearch.london.edu/id/eprint/472/