---
schema_version: "1"
id: b9453582404d0c96cab37943f2b4ed352010f1b967cfdb60e4d4ac440f707716
title: KPIs Tab
collection: admin
version: current
locale: en-us
portal_url: https://docs.salesforce.com/admin/current/en-us/service/264-0-0/service_intel_service_ai_adoption_kpi_tab.md
canonical_url: https://help.salesforce.com/s/articleView?id=service.service_intel_service_ai_adoption_kpi_tab.htm&release=264.0.0&type=5
source_site: help.salesforce.com
content_type: documentation
content_hash: 12e4fe1f061b6472023a5c0a5c957c1feecc59a46ad59fccc5363dcab2e72959
description: Analyze the overall usage and adoption trends across all Customer Service Representative (CSR) groups to identify key areas of success and opportunity. Assess service efficiency by reviewing overall average handle time (AHT) and monitor quality by tracking the overall employee satisfaction score (ESAT) and response quality metrics. For example, a service manager can find patterns that explain fluctuations in agent efficiency and customer dissatisfaction. Filter by overall AHT, negative feedback use reasons, and AHT comparison trend to isolate reasons for extended handle times or negative feedback. Implement targeted interventions, such as coaching on complex case types or modifying the AI suggestions. These actions address the underlying issues, and also enhance customer sentiment.
products:
  - Service Cloud
  - Service Intelligence
release: "264"
---
# KPIs Tab

Analyze the overall usage and adoption trends across all Customer Service Representative (CSR) groups to identify key areas of success and opportunity. Assess service efficiency by reviewing overall average handle time (AHT) and monitor quality by tracking the overall employee satisfaction score (ESAT) and response quality metrics. For example, a service manager can find patterns that explain fluctuations in agent efficiency and customer dissatisfaction. Filter by overall AHT, negative feedback use reasons, and AHT comparison trend to isolate reasons for extended handle times or negative feedback. Implement targeted interventions, such as coaching on complex case types or modifying the AI suggestions. These actions address the underlying issues, and also enhance customer sentiment.

### Required Editions

<table class="slds-table slds-table_bordered slds-max-medium-table_stacked slds-m-bottom_small edition"><colgroup><col></colgroup><tbody><tr class=""><td class="slds-cell-wrap" style="vertical-align:top;">Available in: Lightning Experience</td></tr><tr class=""><td class="slds-cell-wrap" style="vertical-align:top;">Available in: <strong>Enterprise</strong> and <strong>Unlimited</strong>&nbsp;Editions with Agentforce 1 Service Edition</td></tr></tbody></table>

Learn about the key metrics on the KPIs tab.

 
| METRIC | DESCRIPTION |
| --- | --- |
| Overall Usage | Shows the total number of cases where agents used the selected AI product. Use this metric to understand the volume of platform adoption. |
| As-is Usage | Shows the percentage of AI recommendations that users accepted without making any edits. Use this metric to evaluate the accuracy and quality of the AI model. |
| Overall AHT | Shows the average handle time for all service cases within the selected period. Use this metric to track key efficiency metrics across the service center. |
| Overall ESAT | Shows the employee satisfaction score calculated from direct agent feedback (thumbs up or down). Use this metric to measure the quality and effectiveness of AI-driven services. |
| AHT Comparison | Shows the average handle time for cases resolved with AI assistance compared to those resolved. Use this metric to quantify the operational efficiency gained by using Einstein for Service. |
| AHT Comparison Trend | Shows the historical change in average handle time for both AI-assisted and non-assisted cases over time. Use this metric to monitor long-term efficiency shifts and validate ROI. |
| Feedback Distribution | Shows the ratio of positive versus negative agent feedback. Use this metric to evaluate the overall sentiment and satisfaction of your team with AI recommendations. |
| Feedback Trend | Shows the volume of positive and negative feedback over a selected period. Use this metric to track how agent sentiment evolves in response to model updates or process changes. |
| Negative Feedback Reasons | Shows the reasons agents cited for rejecting AI suggestions. Use this metric to prioritize and resolve the root causes of dissatisfaction. |