Most contact centers are still using KPIs designed for a human‑only era — even as AI now handles 30–50% of interactions.
That creates a measurement gap.
When AI deflects routine contacts, legacy averages like AHT and service level stop telling the truth. Faster isn’t always better. Cheaper isn’t always sustainable. What matters is whether issues are truly resolved — with minimal customer effort — across both AI and human touchpoints.
In practice, we’re seeing leading teams shift toward:
First‑Touch Resolution (not just FCR)
Customer Effort Score measured across journeys
AI Resolution Quality to catch delayed failures
AHT used for capacity planning, not agent pressure
The most interesting change? Holding AI to the same quality standard as human agents — with audit trails, escalation thresholds, and repeat‑contact detection.
Discussion prompts
Which KPI has become most misleading in customers environment?
How are your customers measuring AI success beyond containment?
Action If they are still measuring AI with human‑era dashboards, it’s time for a KPI audit.
@brocw The metrics mentioned in the white paper was very accurate for AI First world. Nice white paper for measuring CRM’s especially after implementing AI features.
@brocw This is such a needed reset and the framing around AI Resolution Quality as an emerging non-negotiable really lands for me.
A few thoughts from the buyer/operator side that I think add to what you’ve laid out:
The SL goal isn’t sacred. I lived through a modernization period where we shifted our service level target from 90/10 to 80/20 during a platform transition. Counterintuitively, both internal and external satisfaction went up. CSRs had breathing room to actually learn the new system, and patients never noticed. Sometimes the metric we’re defending hardest is the one most worth questioning.
Channel escape rate deserves its own KPI bucket.
You touch on AI Resolution Quality, and I’d push one layer further. What percentage of customers who hit an agentic experience then shift to another channel anyway…but now increasingly frustrated? That’s distinct from whether the AI “resolved” it. I’ve seen orgs hit strong containment numbers while quietly hemorrhaging customers into the call queue angrier than if they’d just called in the first place. Broken workflow and AI-specific failure are different problems with different fixes, and blending them masks both.
The workforce math is a risk nobody’s modeling end-to-end.
If you’re cutting headcount and your agentic solution has meaningful failure rates, you now have fewer people absorbing more frustrated customers. NPS, CSAT, and agent attrition can all move the wrong direction simultaneously and the investment case that looked great on paper starts unwinding. Are we measuring intended impact vs. actual impact?
The question I keep coming back to: how many customers quietly take their business elsewhere before any of this shows up on a dashboard?
Appreciate you putting this out there it’s the perfect time for this conversation.
