AI-assisted intake and routing for shared services
Australian enterprise shared services function handling HR, IT, and finance requests through fragmented email, portal, and workflow channels.
Experience Pattern
Australian enterprise shared services function handling HR, IT, and finance requests through fragmented email, portal, and workflow channels.
Challenge
Manual triage, inconsistent intake data, and unclear routing were slowing response times and making it difficult for leaders to see where work was blocked.
Approach
- 01
Redesigned the intake model around intent, urgency, data quality, ownership, and approval requirements.
- 02
Introduced AI-assisted classification with human review checkpoints for low-confidence, sensitive, or unusual requests.
- 03
Connected the workflow to operational reporting for queue health, ageing work, rework, and exception volume.
Improvement focus
Before and after pattern
Time to triage
Before: Manual review and inconsistent routing
After: Structured AI-assisted classification with review checkpoints
Misrouted work
Before: Requests moved between teams after initial assignment
After: Clearer routing rules based on intent, urgency, and ownership
SLA attainment
Before: Limited visibility into queue ageing and blockers
After: Operational reporting for queue health and exception volume
This representative pattern is informed by prior enterprise delivery experience and focuses on making intake and routing more consistent without removing human accountability from sensitive or unusual requests.