AI employee · Operations
An AI operations employee that keeps systems and records in step.
Monitors workflows, moves information between systems, reconciles records, generates reports, and flags exceptions — so operations teams spend their time fixing problems instead of finding them.
AI Operations Employee
Operations
- Task
- Reconciling orders against warehouse records
- Tools
- ERPDatabaseSlack
- Approval
- Required for changes outside defined rules
- Last action
- Matched 412 records, flagged 3 exceptions
The role
What this AI employee is for
The connective work of operations: checking that things happened, keeping records consistent across tools, compiling status, and spotting what has gone wrong. It is essential, repetitive, and rarely anyone's favourite task.
Repetitive work it can handle
- Monitor queues, workflows, and scheduled processes
- Move and transform data between systems
- Reconcile records across two or more sources
- Resolve clear mismatches using agreed rules
- Generate daily and weekly operational reports
- Chase missing inputs from internal teams
- Flag exceptions with evidence for review
Systems
Tools it can connect to
Connected through scoped, revocable access. Examples below; your actual stack is confirmed during scoping.
- ERP or operations platform
- Spreadsheets and databases
- Project management tools
- Email and chat
- File storage
- Reporting tools
Approval model
Clear lines between doing, asking, and never
allow
Handles on its own
- Data transfers between systems
- Report generation
- Reminders and chasers
- Rule-based corrections
require approval
Asks for approval
- Corrections outside defined rules
- Changes to financial records
- Bulk updates
deny
Never does
- Deletes records
- Changes system configuration
- Overrides locked periods
Example workflow
Daily reconciliation run
- 1
Schedule
Run starts at a set time.
- 2
Pull data
Records from each system.
- 3
Match
Compare by agreed keys.
- 4
Fix
Apply rule-based corrections.
- 5Human
Review
Exceptions to the owner.
- 6
Report
Summary posted to the team.
Implementation
How we put it to work
Process mapping
01Document data flows, rules, and exception types.
Read-only phase
02The AI reports discrepancies without changing anything.
Supervised corrections
03Proposed fixes approved by the team.
Rule-based autonomy
04Well-defined corrections applied automatically.
Expected benefit
What changes for the team
Earlier detection
Problems surface daily instead of at month end.
Less manual re-keying
Data moves between systems without copying.
Reliable reporting
Reports are produced the same way every time.
Clear exception ownership
Issues arrive with evidence and an owner.
Limitations
What it does not do
- Reconciliation rules must be agreed and documented.
- Systems without data access limit what can be automated.
- Root causes of recurring exceptions still need human attention.
FAQ
Frequently asked questions
01Can it work with spreadsheets?
Yes, including shared spreadsheets, as long as access is reliable. We often recommend moving critical data to a database over time.
02What if the rules change?
Rules are kept in configuration, version-controlled, and tested against past data before changes go live.
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Next step
Design your AI operations employee.
A strategy call is a working conversation about one workflow: what it involves today, what could be automated, and what a first build would look like.
What happens next
- 1Tell us about one workflow
- 2We review it before replying
- 3A working session, then a first-build proposal