Show faster, more consistent service inside Salesforce with automation metrics.
You need to show leadership the measurable impact of Einstein for Service and Agentforce on your team's case-handling performance. This guide walks you through collecting automation metrics — cases auto-classified and correctly routed, replies assisted, handle-time saved, and Agentforce-handled volume — and packaging them into a concise executive update that demonstrates faster, more consistent service inside your existing Salesforce stack.
See where the AI reduces effort and where classification or replies need tuning.
Resolve common requests end to end with a scoped AI agent that escalates the rest.
Tune which fields Einstein predicts and the data it learns from for the team's case types.
Collect the case automation metrics for the period. Pull case data for the reporting period. You need total cases opened, cases where Einstein applied a classification, cases routed automatically, and cases where agents used suggested replies or knowledge articles.
Show the share of cases auto-classified and correctly routed. Calculate the percentage of incoming cases that Einstein classified without manual intervention and the percentage routed to the correct queue on first pass.
Show replies assisted and the estimated handle-time saved. Count how many case replies used Einstein-suggested text or knowledge articles. If you have average handle-time data, estimate the time saved per assisted reply and multiply by the number of assisted cases to show total time saved.
Note any Agentforce-handled request volume. If you deployed an Agentforce agent, pull the number of requests it handled end-to-end without human handoff.
Summarise into a short leadership update with the impact and next steps. Draft a summary with the key metrics: cases auto-classified and correctly routed, replies assisted, estimated handle-time saved, and Agentforce-handled volume. Add what you plan to optimise next.
**Einstein for Service — [Period] Automation Impact Summary** **Period:** [Q# YYYY] **Total cases opened:** [value] **Auto-classification and routing** - Cases auto-classified: [value] ([value]%) - Cases correctly routed on first pass: [value] ([value]%) **Reply assistance** - Replies using Einstein suggestions: [value] - Estimated handle-time saved: [value] minutes per reply - Total time saved: [value] minutes ([value] hours) **Agentforce** - Requests handled end-to-end: [value] **Impact:** Faster, more consistent case handling with [value] hours returned to the team for complex work. **Next steps:** [Describe planned optimisation]
For the period, show cases auto-classified and correctly routed, replies assisted, estimated handle-time saved, and any Agentforce-handled volume, as a leadership update.
Get a complete, on-brand draft grounded in the team's knowledge to review and send.
Send each case to the correct queue automatically, removing manual redirection.