AI for operations
AI for operations: order intake, back-office queues and handoffs
AI helps an operations team by clearing routine files the moment they arrive and handing people a short queue of exceptions with the analysis already done. Native builds those agents inside the systems your team works in. At TIE, a transportation insurance MGA, 74% of endorsement decisions now need no underwriter, and the queue went from hours to under a minute.
Featuring insights fromShaun Devan · Founder & CEOUpdated · 3 min read
Where AI has an impact in operations
Order entry
Orders from email, PDF and spreadsheets drafted in your system, ready for a one-click review.
Document pack review
Every file in a submission or application identified, read and checked against the rules.
Rules-based decisions
Routine approvals settled by the agent, with the reasoning recorded for audit.
Missing-information requests
Gaps found on arrival and the ask drafted before anyone starts work.
Reorders
A one-line reorder rebuilt from the actual past order, with the requested changes applied.
External database checks
Registries and safety records queried mid-process, with each result kept on file.
Status and tracking answers
Where-is-my-order questions answered straight from the system of record.
Shift and queue reporting
A daily view of what cleared, what waits and why, without anyone building it by hand.
Operations work is mostly waiting
In most back offices the task itself takes minutes and the file waits hours or days for someone to reach it. Orders sit in an inbox until an account manager has time to key them, document packs wait behind newer work, and handoffs between teams lose context along the way.
Agents remove the wait. They pick up each item when it lands, read every attachment, run the checks your team runs, and either complete the step or pass it on with the reasoning written out.
| Work | Agents | Operations team |
|---|---|---|
| Order intake from email, PDF or spreadsheet | Extract the order, check it against the catalogue and draft it in the system | Review and approve |
| Routine requests with clear rules | Decide and record why | Spot-check and override |
| Incomplete files | Flag what is missing the same day and draft the request | Send it |
| Exceptions and edge cases | Refer with the analysis attached | Decide |
| Status questions from customers or colleagues | Answer from the system of record | Handle escalations |
Where to start
- The queue with the most volume and the clearest rules. At TIE that was mid-term endorsements; in a manufacturer it is often order entry.
- Order intake. At USCAPE a forwarded email becomes a review-ready order in about five minutes, with every uncertain field flagged.
- Missing-document chasing. Catching an incomplete file on arrival saves days of back and forth later.
- Handoffs between teams. Agents write the summary the next team needs, so nobody starts from the raw email.
Built on the systems you already run
Operations data lives in old systems, and the agents work with them as they are. At USCAPE that is a FileMaker database with hundreds of scripts; at TIE it is Surefyre plus a federal safety database queried mid-process. Writes are idempotent and logged, so a retried step never creates a duplicate order.
What an engagement looks like
We map the queues first: what arrives, from where, which checks are made and where items wait. That ranks the workflows by impact. The first agent goes live within weeks, and within months the department runs with agents on every routine file. Your team is trained to run them and to add new rules as the business changes.
How to measure it
- Queue-to-completion time per item type
- Share of items completed with no manual touch
- Items returned for missing information, and how fast they come back
- Error rate in the system of record
- Backlog at the end of each day
Case study · TIE
TIE decides transportation insurance endorsements in under a minute.
Native helped TIE, a US transportation insurance MGA, put agents on every submission and endorsement pack. They check each one and either decide it or hand the underwriter a pre-filled decision, and 74% of decisions now need no underwriter.
Frequently asked questions
How can AI help an operations team?
AI picks up routine items as soon as they arrive, reads the documents, runs the checks and either completes the step or refers it with the analysis done. The team spends its time on exceptions instead of the queue.
Can AI handle back-office work in legacy systems?
Yes. Agents work through whatever the system offers, whether an API, a database or a monitored inbox. USCAPE's order agent writes into a FileMaker database in its exact naming.
Which operations process should we automate first?
The one with high volume, clear rules and a visible queue. Endorsements, order entry and document intake are common first choices because the impact shows up within weeks.
How do you stop an AI agent from making a bad decision?
Its authority is written down and narrow. Anything outside the rules goes to a person, every action is logged with its reasoning, and anyone on the team can reverse a decision.
