An AI customer service pilot judged on one day-30 resolution rate can fail the wrong vendor. Take three readings and decide on where the rate is heading.
148 of 195 published AI resolution rate benchmarks never say how many tickets they cover. We cut the 47 that do, and high volume shows no sign of a drop.
The old customer support staffing model counts agents per thousand tickets. At 60-80% AI resolution, forecast four lines instead. Only one of them hires.
Customer support capacity planning forecasts volume and staffs the peak. A four-hour spike breaks that. The gap in absorbed share, 26% to 90%, set each roster.
Most benchmarks quote one cost per contact. Yours is a band, set by two prices and how much of your volume the AI finishes. Bands by vertical, at 30k+ tickets.
Most AI customer service proposals stall on the ROI number. The business case that gets signed is a document, not a number. Five parts, one page you forward.
An AI support migration checklist: keep the helpdesk, tickets and macros, rebuild the escalation rules and tone, then run both agents for one to two weeks.
Ticket deflection counts the contact that never happened, helped or not. How it works, why the median sits near 70%, and when a high number is a warning sign.
Most teams measure ROI of AI customer support in week one. That reading is noise. Five baselines to take before go-live, and the three gates it must clear.
Improve first response time by naming your delay: queue, routing or answer. Label a month of your tickets, then test one change for a week and read the p90.
Cutting agents saves this quarter and bills you later in churn. To reduce customer service costs, cut demand and unit cost first: every lever, ranked.
Every guide lists the same 10 customer service metrics. Most are diagnostics. The 3 scoreboard numbers to report weekly, with real benchmarks.
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