“Should we invest in AI for our contact center?”
Here’s how I answer that question:
AI for AI’s sake isn’t a strategy.
You need measurable results that impact your bottom line.
Here are the metrics that actually matter for the top 2 use cases:
1) For AI handling customer interactions:
Track two things:
1. Containment rate (Did AI resolve the issue without human escalation?)
2. Customer satisfaction (How did customers rate the AI interaction?)
Example:
You answer 1,000 calls per day.
AI starts handling 5% of them (50 calls).
If those 50 calls are resolved and customers rate them highly?
That’s 5% less labor cost with no quality drop.
Scale that, and the ROI becomes clear.
2) For AI supporting agents:
The goal here is efficiency, not replacement.
Track: Average handle time reduction.
Example:
Your agents average 6 minutes per call handling 1,000 calls daily.
AI assists them and drops handle time to 5 minutes (16.7% reduction).
That’s 16.7 hours of freed capacity per day.
Compare that labor savings against your AI cost.
If the savings exceed the cost? Scale it.
The framework I use:
Every AI investment should improve at least one of these:
→ Quantity (more interactions handled)
→ Quality (higher satisfaction scores)
→ Cost (lower cost per interaction)
Ideally, it improves two or all three.
The trap I see founders fall into is implementing AI because everyone else is.
That’s not strategy. That’s fear.
Start with the outcome you want. Then find the tool that gets you there.
Not the other way around.
I’m Mark Danielson, and I help healthcare leaders reduce support costs while improving patient satisfaction.
Follow me for practical insights on cutting contact center costs, improving service quality, and modernizing operations without the tradeoffs.
