it_support · services · workflow
Grant Thornton achieves 84% IT auto-resolution rate with Aisera Conversational AI on Microsoft Teams
During the shift to remote work, Grant Thornton faced inflated IT ticket volumes and initially added outsourced agents, but found that approach unsustainable; the team needed effective employee self-service and automation of routine IT tasks.
How it works
Common implementation structure
How this type of workflow is generally built, generalized across documented cases — not tied to any one vendor's stack. Click any stage to read what happens there. Specific products that implement these stages appear in “Tools commonly seen” below.
Stage 1 · Employee submits IT request
Employees access IT self-service for support requests on MS Teams.
Tools used
AiseraAlyx BotRPA
Outcome
Aisera delivered an auto-resolution rate of 84% (headline) and 75% (body text), an 85% improvement in employee satisfaction, and a 90% improvement in mean-time-to-resolution, freeing agents from cumbersome manual tasks.
What failed first
Adding outsourced agents to handle inflated ticket volumes was not a sustainable solution for Grant Thornton's IT support scaling needs.
Results
Time saved90%
Volume84%
Grounding & classification
Source type: vendor customer story
30 fields verified against source quotes.
agentic workflowconversational aisupport agentsupport tickethuman review describedmetric backednamed customerproduction runtime claimedtools describedvendor confirmedworkflow describedprofessional servicesautomation ratecustomer satisfactiondeflection rateemployee productivityresolution time reductionvendor customer storyit supportticket triageautonomous resolutionintake to triage