If you run retail today, you don’t have an order tracking problem—you have a resolution problem. Customers don’t want a parcel ID; they want confidence that the order will arrive, that exceptions are handled before they notice, and that you’ll make it right if things go sideways. The good news: we finally have the tech to deliver that experience at scale.
Why now? Two signals you can’t ignore.
First, agentic AI—software that doesn’t just answer questions but takes actions—has matured fast. Analysts now treat agentic AI as a top tech trend, reflecting the move from passive copilots to proactive teammates built around your business logic and data flows.
Second, operators are voting with budgets. When the world’s largest retailer publicly commits to “AI super agents” for customers, associates, and suppliers, it’s a clear direction of travel for the industry.
The cost of status quo is staggering.
Inventory distortion—out‑of‑stocks and overstocks—still drains around $1.7T globally, even after recent improvements. That’s margin evaporating via split shipments, cancellations, markdowns, and disappointed shoppers.
Returns add a second tax: U.S. shoppers sent back an estimated $890B in merchandise in 2024 (about 16.9% of sales), while a growing share of retailers now charge for return shipping to claw back costs.
Meanwhile, expectations keep climbing: support teams report a step‑change rise in what customers consider “table stakes” for speed, personalization, and channel coverage.
From tickets to outcomes: four AI agents that pay for themselves
Here’s where agentic AI changes the math. Instead of standing up one giant “do‑everything” bot, deploy narrow, high‑impact agents that each own a measurable outcome and integrate with your OMS, inventory, carrier, and CRM stack.
- Promise Protector — Monitors pre‑ and post‑purchase promise (ATP/ATS, cut‑off times, carrier capacity). When risk rises—weather, labor, stuck orders—it auto‑re‑routes, splits, or reprioritizes, and notifies the customer with a revised ETA and make‑good if needed.
- Inventory Analyst — Reads Inventory feeds, deltas, real time updates, understand forecasted demand and variation trends to identify overstock/understock skus, locations. Alerts on suggested next best actions e.g. promotions, just-in-time rebalancing, new POs, alternative sourcing routes etc
- Returns Router — Scores return risk at checkout and post‑delivery; steers customers to exchanges/store drop‑off; dynamically sets policy (fee‑free vs. paid label) based on CLV, fraud risk, and item resale probability. As more retailers introduce paid elements in returns, using AI to target when to waive or charge preserves loyalty without subsidizing abuse.
- Backorder Bodyguard — Watches supplier fills and inbound variability; proposes substitutions, partial ships, or store‑fulfill options; communicates choices in‑flow to customers so a backorder doesn’t become a cancellation.
What it takes under the hood
- Event‑driven OMS as command center: orders, shipments, inventory positions, and exceptions streaming in real time.
- Unified identity & context: link guest orders, loyalty, and prior interactions so agents can reason about CLV and intent.
- Policy as code: appeasements, fees, and service levels encoded in a rules layer the agents can call—guardrails first, then learning.
- Human‑in‑the‑loop & audit trails: agents propose > act > log; supervisors can review patterns and tighten policies.
- KPIs that matter: promise‑kept rate, prevented WISMO contacts, on‑time delivery improvement, avoidable return rate, and net margin after appeasements.
A 90‑day playbook to prove value
- Days 0–30: One journey, one metric. Pick a single pain (e.g., late‑delivery appeasements). Integrate OMS + carrier events; codify “goodwill spend” rules; pilot on 10% of volume.
- Days 31–60: Expand signals, shrink tickets. Add inventory signals and lane performance; auto‑notify customers pre‑breach; measure prevented contacts vs. baseline.
- Days 61–90: Scale and sharpen. Roll to 50–70% of orders; introduce CLV‑aware policies for returns and appeasements; publish a weekly “promise‑kept” score to the exec team.
What great looks like by Q4
- 30–50% reduction in WISMO contacts.
- 10–20% improvement in on‑time delivery for risk‑flagged orders.
- 3–5 point reduction in avoidable returns on targeted categories.
- Positive ROI after accounting for appeasements and incremental shipping costs.
The CEO call to action
Make “resolution” the product. Charter a cross‑functional pod (Ops, CX, OMS, Data) with a single mandate: raise promise‑kept rate while lowering cost‑to‑serve. Fund it like a growth bet, not a tooling project. Your competitors are already moving—some very publicly. The advantage will accrue to retailers who treat agentic AI not as a chatbot, but as a discipline embedded in their order and inventory nervous system.
