The true test of any AI tool for service and support is the following: Without human intervention, will the tool:
Reduce ticket volumes, resolve problems more quickly, decrease total cost of ownership, and improve the customer experience?
If it checks all these boxes – and gets smarter over time – then it’s true AI, powered by machine learning.
So, what are the metrics impacted by AI in Service and Support?
Workload metrics such as ticket volume and tickets per user per month
Cost metrics such as Total Cost of Ownership, First Level Resolution Rate, and % Resolved Level 1 Capable
Quality Metrics such as First Contact Resolution Rate, Customer Satisfaction, Net Promoter Score, Customer Effort Score, and the Customer Experience
And Cycle Time metrics such as Mean Time to Identify, Mean Time to Resolve Incidents, and Mean Time to Fulfill Service Requests
As an example, the current average for tickets per user per month is about 1.1 tickets at level 1, and 0.5 tickets for desktop support. Reducing the tickets per user per month by 50% or more is not uncommon with an effective AI tool.
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