Retail operations teams spend significant time investigating order exceptions: backorders, inventory shortages, fulfillment delays, sourcing failures, and delivery risks.

Traditionally, resolving these issues requires navigating multiple systems, reviewing data, and manually determining the next action.

But what if the system could investigate the problem itself and recommend the best resolution? That is the promise of agentic AI. 

Automation vs. agentic AI

Retailers have automated processes for years: 

• Low inventory triggers an alert 

• Order failures create exceptions 

• Shipment delays generate notifications 

Automation follows predefined rules.

Agentic AI goes further. Instead of simply executing a rule, it analyzes the situation, understands the objective, and recommends the next best action.

The focus shifts from task execution to outcome optimization.

A simple backorder example

Consider a backordered item.

Traditionally, an analyst investigates inventory availability, alternate locations, fulfillment options, and customer impact before deciding what to do.

With agentic AI, the system can: 

• Detect the issue 

• Analyze likely root causes 

• Evaluate available alternatives 

• Recommend corrective actions 

Instead of spending time gathering information, operations teams can focus on making decisions. 

Why OMS matters

An AI recommendation is valuable only if it can be executed.

If the system recommends rerouting an order, it must understand inventory, sourcing rules, fulfillment capacity, customer promises, and business constraints.

This is why agentic AI is most powerful when embedded directly within the OMS, where it has access to the data and workflows needed to support execution.

This is where platforms like Manhattan Active Omni (MAO) become particularly relevant. Because MAO unifies order management, inventory visibility, fulfillment, and customer promise capabilities on a single platform, AI agents can access the real-time data and workflows required to investigate and resolve order exceptions. Rather than operating as a standalone assistant, agentic AI can work within the same environment where operational decisions are executed.

From investigation to decision-making

Today's support teams often jump between multiple screens to understand a problem.

Future experiences may look like this:

"Why is this order at risk?"

AI response: 

"The selected fulfillment location has limited capacity. Another location can fulfill the order within the promised delivery window at minimal additional cost. Recommended action: reroute.

" The system becomes a decision assistant rather than just an information provider. 

The road to autonomous commerce

The evolution of OMS is moving through four distinct stages: 

Automation → Execute predefined rules 
AI assistance → Explain what happened 
Agentic AI → Investigate and recommend actions 
Autonomous execution → Act within approved business guardrails

As organizations move toward the latter stages, the benefits extend beyond operational efficiency. Retailers can expect: 

• Reduced exception handling effort 

• Faster order resolution 

• Improved fulfillment efficiency 

• Lower operational costs 

• Better customer promise performance 

• Enhanced productivity for support and operations teams 

While human oversight remains essential, agentic AI allows teams to spend less time investigating issues and more time focusing on strategic decisions and customer outcomes.

The intelligent OMS imperative

The future of Order Management Systems will not be defined by processing more orders. It will be defined by making better decisions for every order. 

Retailers should not ask, "Where can we use AI?" Instead, they should ask, "Which decisions consume the most effort today, and how can the system help resolve them?" 

Agentic AI helps organizations move from automation to autonomy by turning order exceptions into intelligent decisions. The result is faster resolutions, more efficient operations, and better customer outcomes at scale.