It support · Production

Grant Thornton achieves 84% IT auto-resolution rate with Aisera Conversational AI on Microsoft Teams

The problem

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.

First attempt

Adding outsourced agents to handle inflated ticket volumes was not a sustainable solution for Grant Thornton's IT support scaling needs.

Workflow diagram · grounded in source
1
Employee submits IT request
trigger
“Self Service for IT Support Requests on MS Teams”
2
Conversational AI handles request
ai_action
“Aisera offers true enterprise Conversational AI & RPA solutions which can be extended across departments”
3
Supervised Guided Flows customization
human_review
“Aisera's Supervised Guided Flows give Grant Thornton support agents the ability to customize workflows to build out new functionality as need be”
4
Continuous AI learning
feedback_loop
“Continuous AI learning empowers Aisera to quickly and autonomously learn from the resolution of past requests, allowing it to tailor its responses perfectly to their organization”
5
Auto-resolution delivered
output
“issue auto-resolution rate of 75 percent and a 90 percent improvement in resolution time. The speed and accuracy of the resolutions led to an overall improvement in employee satisfaction of 85 percent”
Reported 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.

Reported metrics
Auto-Resolution Rate (headline)84%
Employee Satisfaction improvement (headline)85%
Mean-Time-to-Resolution improvement (headline)90%
Issue auto-resolution rate (body)75 percent
Show all 6 reported metrics
Auto-Resolution Rate (headline)84%
Employee Satisfaction improvement (headline)85%
Mean-Time-to-Resolution improvement (headline)90%
Issue auto-resolution rate (body)75 percent
Resolution time improvement (body)90 percent
Employee satisfaction improvement (body)85 percent
Reported stack
AiseraAlyx BotRPAMicrosoft Teams
Source
https://aisera.com/customers/grant-thornton/
Read source ↗

Frequently asked questions

What did this team achieve with this AI workflow?

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 tools did this team use?

Aisera, Alyx Bot, RPA, Microsoft Teams.

What results were reported?

Auto-Resolution Rate (headline): 84%; Employee Satisfaction improvement (headline): 85%; Mean-Time-to-Resolution improvement (headline): 90%; Issue auto-resolution rate (body): 75 percent (source-reported, not independently verified).

What failed first in this deployment?

Adding outsourced agents to handle inflated ticket volumes was not a sustainable solution for Grant Thornton's IT support scaling needs.

How is this it support AI workflow structured?

Employee submits IT request → Conversational AI handles request → Supervised Guided Flows customization → Continuous AI learning → Auto-resolution delivered.

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