The Problem
Quality teams can only listen to a tiny fraction of calls — a handful per agent per month, chosen more or less at random. The coaching that comes out of it is based on a sliver of the picture, and the moments that would teach the most often never get reviewed. Meanwhile the sentiment data that is captured usually only looks at the customer, treating the agent as a black box.
The AI Use Case
Have AI measure sentiment on both the caller side and the agent side of every conversation, then use the gap and the trajectory between them to surface concrete learning opportunities. Two sides, tracked over the arc of the call, tell you far more than one score at the end.
- Caller side — where did frustration spike? Did the call recover or end sour? What topic set it off?
- Agent side — did the agent stay calm and warm under pressure, or match the caller's tension? Are there signs of fatigue or burnout across their day?
Learning Opportunities It Surfaces
For agents
Instead of random call reviews, coaching is targeted at the moments that matter: the calls where sentiment dropped and never recovered, or where an agent turned a rough start into a happy ending (worth studying as a good example, not just flagging problems). You can spot patterns across an agent's month — a specific topic that consistently trips them up — and coach the cause, not the symptom.
For the operation
When many callers sour at the same step — a particular policy, a confusing fee, a broken process — that's not a coaching issue, it's a business one. Dual-side sentiment turns thousands of calls into a heatmap of where experience breaks down.
For agent wellbeing
Tracking agent-side sentiment over time can flag rising strain before it becomes attrition — a genuinely valuable, and often overlooked, benefit.
Use it to help, not to police. The fastest way to poison this is to turn agent-side sentiment into a stick. Frame it as "here are your best calls and your toughest moments, let's learn from both" and it becomes something agents actually welcome.
The Value
- 100% coverage — every call analysed, not a random 2%.
- Targeted coaching — time spent on the moments that teach the most.
- Process fixes — recurring frustration points surface as business issues, not just agent issues.
- Wellbeing signal — early warning of strain and burnout.
A Note on Doing It Responsibly
Sentiment models are imperfect and can carry bias, so treat scores as a signal to guide a human conversation, not a verdict. Be transparent with agents about what's measured and why. This sits squarely within the kind of thinking covered in assessing AI risk in the contact centre.
Explore More AI Use Cases
Part of a growing series of practical AI use cases for contact centres. Browse the full set on The AI Use Cases hub, and see the bigger picture in 15 ways AI can transform your contact centre. Got one you'd like covered? Get in touch.