If you run omnichannel fulfillment, you already know the "fun" part: the customer experience is one smooth line… and your operation is a tangled set of handoffs.
A shopper buys online at 9:12 AM. They want to pick up lunch. The store shows 1 left (maybe). The picker can't find it (definitely). Customer support gets pinged. The store manager gets annoyed. The customer gets a "sorry" email. And the post-mortem says something vague like "inventory accuracy issue"—which is technically true and operationally useless.
The fulfillment and ops leaders who handle this well have started treating these moments as workflows, not reports. And workflows that run consistently, at scale, across hundreds of stores—that's exactly where AI agents are starting to prove useful.
Omnichannel Isn't a Channel Strategy Anymore. It's an Operations Problem.
A few years ago, "omnichannel" meant better customer journeys and a consistent brand experience. That's still true.
But operationally, omnichannel has turned stores into mini fulfillment centers (ship-from-store), pickup counters, return depots, and sometimes same-day delivery launchpads. That's a different job than "run a great store." And it's expensive—McKinsey has pointed out that omnichannel fulfillment often runs 10–20% of sales, with grocery-specific picking and delivery costs landing even higher depending on the model.
So the question isn't "How do we report omnichannel performance?" It's "How do we stop omnichannel complexity from eating margin while we're trying to grow?"
Why the Old Toolset Breaks Down
Most retailers have a decent stack: dashboards, order management, inventory systems, store labor tools, customer data platforms, and support ticketing. The failure isn't a lack of data. It's that the work sits between systems:
- "Is it actually in stock, or just on paper?"
- "If we fulfill from Store A vs Store B, do we miss the pickup promise?"
- "Should we substitute, split-ship, reroute, or cancel?"
- "Is the spike in demand real… or just a promo badge misfiring?"
Dashboards can show you the metric moved. They don't run the sequence of checks your best operators run instinctively. The real pain isn't analysis—it's the scramble when something quietly breaks and five teams all assume it's someone else's problem.
Where the Best Ops Teams Are Starting to Use AI Agents
The teams getting value from AI agents tend to start small and operational: fix inventory accuracy, tighten promise management, reduce cancellations and costly exceptions, streamline returns decisions, and route issues to the right owner fast.
Here's where that plays out in practice:
Inventory accuracy becomes a living signal, not a daily report
Omnichannel breaks the moment inventory lies. If your website thinks a store has 1 unit and the shelf doesn't, you get cancelled orders, angry pickup customers, wasted picker labor, and support costs you rarely attribute correctly.
This is why so many retailers lean on RFID for item-level accuracy—Accenture and McKinsey both cite cases where RFID at scale drives 98% inventory accuracy and 25%+ improvements over baseline.
But technology alone doesn't close the loop. The operational shift is treating inventory as telemetry: detect mismatches in near real-time, isolate whether it's shrink, mis-scan, receiving delay, or system sync—and then trigger the right response (recount, hold out-of-stock, shift fulfillment, or flag a potential theft pattern).
An AI agent can run that checklist continuously across hundreds of stores. A human team doing it manually will always be playing catch-up.
Order routing becomes dynamic instead of rule-based
In omnichannel, every order is a routing decision: fulfill from the distribution center, fulfill from store, split the shipment, substitute an item, ship to home, or push to pickup. Each choice changes delivery speed, fulfillment cost, and the probability of cancellation.
The best ops teams treat routing as a constrained optimization problem: preserve margin, protect promise windows, reduce split shipments, avoid stores already overloaded with picks, don't drain safety stock on high-velocity items.
A practical workflow looks like: detect a spike in ship-from-store orders for a specific metro, diagnose why (missed distribution center cutoff + promo driving demand + store pick labor constrained), then reroute a subset to alternative nodes, suppress splits, adjust promise windows, and alert the regional ops owner.
No heroics. Just fast, consistent decisions. An AI agent can run this loop continuously; a war room can't.
Pickup and returns stop being services and become exception factories (unless you automate)
Buy-online-pickup-in-store works until it doesn't: the picker can't find the item, the customer doesn't show up, substitution logic is unclear, the pickup counter is understaffed, an item is sitting "picked" but not staged properly.
Returns add another layer. The National Retail Federation expects nearly $850 billion in merchandise returns in 2025, with about 19% of online sales coming back. That's not just a finance number—it's an operations load.
The teams handling this well automate the boring-but-costly decisions: should this pickup be substituted or canceled? If substituted, what's the best alternative that preserves the basket value? If returned, should it be restocked, sent to refurbishment, liquidated, or held?
Even routing the return correctly matters—returns aren't just reverse logistics, they're inventory truth and availability for the next customer. An AI agent can make these calls in seconds, consistently, across every location.
Pricing and promos get guardrailed so you don't "discount into a stockout"
One of the most common omnichannel mistakes is treating pricing as independent of availability.
If inventory is thin, running a promotion is basically inviting a stockout—and then you pay the double tax: lost revenue plus customer trust damage. Many "pricing issues" are actually inventory issues in disguise. The better move is often to suppress promos and elevate substitutes when availability is thin.
An AI agent can operationalize that: monitor competitive pricing where it matters, check real-time availability (not a daily flag), and enforce a simple rule—don't widen demand when supply can't support it.
A Concrete Example: What This Looks Like in Practice
A 200-store specialty retailer. On a normal Friday, store fulfillment handles about 18% of online orders, buy-online-pickup-in-store is about 22%, and the cancellation rate for pickups is 1.4%.
Then they launch a weekend promo.
Within two hours: pickup orders spike in three metro areas, "picked but not found" exceptions jump, support tickets start climbing.
A dashboard tells you what happened after the fact.
An ops team with an AI agent running the loop does something closer to:
- Detects the spike in exceptions and identifies the products and stores driving it
- Checks whether the issue is true stockouts, inventory record mismatch, or pick capacity
- Reroutes fulfillment for the hottest items away from overloaded stores
- Suppresses the promo for items already below safety stock (or shifts promo to substitutes)
- Alerts the right regional ops owner: "here's what changed, here's why, here's what we already did, here's what still needs approval"
That's AI in retail ops when it matters: less time watching the problem, more time preventing the revenue leak.
The Part Most Teams Skip: Guardrails and Trust
If you want AI agents to actually run ops workflows, you need three things:
- Clear policies: what actions are allowed, when, and who approves
- Auditability: what the agent checked, what it decided, and why
- Reversibility: the ability to undo actions and learn
This is also why a lot of agent projects fail—not because the model can't query data, but because the organization never defined "safe action." Gartner has warned that a large share of agentic AI projects may get canceled because of unclear value, cost, or risk controls. The teams succeeding are the ones who start with tight operational loops and expand from there.
Why This Shift Is Happening Now
Three forces are converging: omnichannel complexity is now the default (not the edge case), returns and fulfillment costs are pressuring margins (making exception reduction valuable), and retailers finally have enough operational data—orders, picks, scans, status events—to let agents operate, if they can connect it across systems.
The online retailer example we've seen work: top-SKU availability improved from roughly 80% to 95%, root cause investigation went from daily-and-manual to real-time, and the fulfillment team stopped firefighting and started preventing.
The Bottom Line
If omnichannel ops feels harder every year, you're not imagining it. Stores are doing more jobs, promises are tighter, and exceptions are multiplying.
AI agents don't "fix omnichannel." They do something more specific—and more useful: they turn repeatable ops playbooks into fast, consistent, always-on workflows that detect issues early, explain what changed, and route or execute the right action before revenue and customer trust take the hit.
That's the transformation the best fulfillment and ops teams are making: less dashboard watching, fewer war rooms, more quiet prevention.
This is what we're building at Bicycle AI agents that handle retail operations intelligence end-to-end—detecting issues, explaining root causes, routing to the right owner, and proposing safe actions. If this resonates, we'd like to hear from you.
