Order management automation with AI Agents
Context
Managing a high volume of low-value orders is hard. Especially when customers rely on non-EDI formats or send incomplete EDI messages. Orders, change requests, and quote inquiries arrive as emails and attachments in various formats, each shaped by customer-specific rules and missing details. Traditional automation assumes standardized. In reality, order flows vary by customer, and for many smaller customers this kind of flexibility is helping them stay competitive. Treating this variation as an exception creates more manual work and less scale. Agentic automation adapts instead. AI Agents determine what action is required (new order, change, quote), resolve gaps, and adapt to each customer’s logic. Requests become ERP-ready records, and every interaction becomes visible and measurable: response times, changes, and recurring problems. Order management scales without backlogs, blind spots, and extra headcount.
Challenge
Order management breaks at intake, visibility, and execution.
Orders, changes, and quote requests arrive in mixed formats: PDFs, spreadsheets, screenshots, forwarded emails, or partial EDI messages. Before anything can move forward, teams must interpret, normalize, and validate input.
This creates a list of distinct problems:
Manual order intake consumes operational capacity
Teams spend their time extracting line items, mapping SKUs, checking quantities, and validating prices – leaving little capacity for higher-value work like complex offers or customer support.
Quote requests interrupt the order flow
Pricing inquiries arrive alongside orders but follow different rules. Simple quotes still require manual interpretation, while complex ones pull senior staff into repetitive work, slowing response times.
Customer-specific rules drive exception
Units of measure pricing logic, MOQs, order confirmation rules, and change handling differ by customer. These rules live outside the system and force repeated manual intervention.
Customer satisfaction pressure
Customers expect their unique requirements to be met flawlessly, making error-free processing a critical KPI for suppliers.
Speed limits growth
Slow confirmations, corrections, or quotes directly affect conversion and repeat business, especially for smaller customers who compete on responsiveness. As volumes grows, manual handling increases costs and error risk, making scale harder to sustain.
Volume makes business more fragile
As order volume grows, manual handling increases error rates and cost, making growth harder to sustain.
Customer behavior data gets lost
When orders are handled manually, changes aren’t tracked systematically. Teams lack visibility into how often customers modify orders, what changes most (SKU, quantity, date, price), or how many orders are entered correctly the first time.
Solutions
To remove manual bottlenecks in order management, Nordoon deploys a suite of AI Agents that take over order intake, validation, and follow-ups across non-EDI orders, incomplete EDI messages, and quote requests. Getting people’s input happens only when judgment is required.
Autonomous processing of non-EDI orders
AI Agents process orders in any format (PDFs, Excel files, emails, etc.). They extract line items, map customer references to internal SKUs, normalize units and quantities, validate prices, and push clear orders to ERP. Customer-specific rules are applied during processing, and orders move forward without manual cleanup. Teams steps in only when inputs conflict with defined rules or require commercial considerations.
Double-checking EDIs
When EDI messages arrive incomplete or unclear,AI Agents detect missing information, request necessary details directly from customers, and fill them in as soon as they arrive. Small gaps are resolved early before they turn into delays, rework, or downstream errors. The team's effort is reduced unresolving discrepancies or deciding on exceptions.
Quotes & estimates automation
AI Agents handle quote requests alongside orders. They map products to price lists and conditions and send responses automatically. Simple quotes are completed end to end. More complex ones are routed for approval directly in the inbox. People step in only on custom pricing decisions, they do not waste time preparing them.
Adaptive business logic integration
Order logic isn’t standardized and doesn’t have to be. AI Agents apply customer-specific rules for pricing, units, confirmations, delivery constraints, and change handling at runtime. This reduces exceptions without forcing customers into rigid processes, keeping the business flexible and the execution predictable.
Integrated, human-in-the-loop execution
AI Agents integrate with ERP systems to read master data and open sales orders. They’re living in Outlook and Teams to resolve exceptions, approve quotes, and clarify inputs where humans are needed. Feedback loops stay inside tools teams already use, keeping response time short and adoption friction low.
See if this fits your operations
Results
20,000 processed orders per month
AI Agents handle high-volume order intake end to end, absorbing format variability and customer-specific logic without manual intervention.
80% less operational time
Order handling drops from hours to minutes as extraction, validation, enrichment, and ERP updates became automated and teams get free from spreadsheet-driven work.
Scalable customer service with 95% automation
EDI orders get processed and non-EDI get handled with minimal human involvement.Teams support growing order volumes without increasing headcount or compromising responsiveness.
Faster response times, 100% quicker replies
Inbox and ERP integrations ensure no request is missed and no clarification stalls. Orders, changes, and quotes move forward as soon as information arrives.
Stronger customer relationships
Faster order fulfilment and accurate processing improved customer satisfaction and retention.
