Turning AI insights into agent coaching takes four things: someone to receive the finding, a behavior it points to, time in the week to sit down with the agent, and a check afterwards on whether anything changed.
Getting the insight is the easy part. Most contact centers already have AI telling them something about their conversations. The hard part is getting it into a coaching session, and for some teams it never gets there.
AI insight is widespread, but it doesn't always lead to coaching
Every quarter, the ScorebuddyCX QA & CX Intelligence Pulse Report surveys contact center professionals on what AI is changing about the way their teams work. For the latest edition, the AI Reality Check, we put our questions to 600 people working in contact centers. One of them: what share of your coaching conversations are now triggered by AI-generated insight?
Among the contact centers that generate AI insight, 15% get no coaching out of it at all. The insight is there, but it never reaches a coaching session.
Where it does get through, it accounts for a small share of the work: on average, 18% of coaching conversations start from an AI insight.
The report has the full breakdown, including the few contact centers where AI insight starts more than half of all coaching. Get the AI Reality Check to see where your team sits.
Why AI insight stalls before it reaches a person
Two things get in the way. The first is foundations: an insight is only as good as the scores underneath it. If nobody checks those scores, or the agents on the other end don't believe them, a team lead has good reason to think twice before building a coaching session on them. Most contact centers we talk to haven't yet built an automated QA program that's governed well enough to be trusted that far.
Getting there means settling how often a human reviews AI scores, and whether the people being scored trust what the AI tells them.
The second reason is structural. Even if the insight is sound, it still lands on a dashboard, and nothing moves it from there into a coaching session unless someone does so manually. The dashboard describes teams and trends, while coaching happens one agent at a time, on specific behaviors.
Turning a dashboard trend into a one-to-one takes work, and in a lot of contact centers that work isn't anyone's job. So when the queues get long, it's the part that doesn't get done. Box-ticking QA is ultimately fruitless, and the same goes for insight nobody acts on.
Not every insight belongs in a coaching session
Some of what AI turns up has nothing to do with how an agent handled a call. Suppose your conversation analytics shows repeat contacts climbing, and they trace back to a confusing line on the monthly bill. You could coach every agent on the team to explain that line better. But in reality you're not stopping the repeat calls until you actually fix that line on the bill itself.
So routing insight means sending it to whoever can act on it. Often that's a team lead, with an agent. Sometimes it's someone in billing, product or marketing who never sees a QA report.
Before an insight goes anywhere, decide which kind it is. Agent behavior goes to coaching. A process, product or policy problem goes to the person who owns it, with enough of the evidence attached that they can see it for themselves.
Business intelligence reporting that other teams can open makes that handoff a lot easier than a forwarded spreadsheet. Permanent TSB, a ScorebuddyCX customer, describes its QA reporting as "genuinely insightful data which we use to improve performance across multiple areas of the business."
How to turn AI insights into coaching
When an insight does come down to how an agent handles conversations, here's how to get it into coaching.
- Pick one behavior and pull the conversations that show it. "First-contact resolution is down on the billing queue" isn't something an agent can work on. Before the session, narrow it to one thing this agent does, such as closing the chat before confirming the customer's problem is solved, and find two or three of their own scored conversations where it happens. Our coaching tools let you coach on those scored interactions in the same platform, with built-in templates to give the session a structure.
- Bring the insight into the coaching you already run. Few team leads have time for a separate session just for AI findings, and they don't need one. Take one or two insights into the regular one-to-one, and no more, because a long list gets skimmed. That weekly slot still needs protecting: put it in the calendar with a name next to it, or it disappears in exactly the weeks that need it most. A written coaching plan helps keep that time for coaching.
- Check the next round of scores. AI QA scoring shows you whether the coaching worked, on the same scorecard question across the agent's next few weeks of conversations. If it moved, the insight did its job. If it didn't, look again at both ends: maybe the insight pointed at the wrong thing, or one session wasn't enough. Where the gap turns out to be a skill rather than a habit, a targeted course through our learning management system can pick up where the conversation left off.
Where the insight pays off
If an insight only lives in your dashboard and never travels further than that, it won't change the customer experience. To make a real change to agent behavior and the resulting support quality, you need to bring the insight to life in a coaching session, or get it to whoever owns the process behind it.
Most contact centers already have the technology to find those insights. What they get back from it depends on the route they build from finding an insight to delivering it in a coaching session.
Using AI insights for coaching is one of five key areas in our AI Reality Check report for contact centers. The other four: how often a human checks AI scores, why managers trust those scores more than agents do, whether AI has made agents' work easier (managers and agents don't agree), and what it has done to frontline headcount. Get your copy of the AI Reality Check now.
