From AI Insights to Action: Closing the Coaching Gap

This blog summarises key points from a recent article by Derek Corcoran at Scorebuddy on why AI insights don’t always lead to coaching and how contact centres can turn them into action.

AI Insight is Common, But Coaching Doesn’t Always Follow

Each quarter, the QA & CX Intelligence Pulse Report, by ScorebuddyCX, explores how AI is influencing the work of contact centre teams.

In the latest edition, the AI Reality Check, 600 contact centre professionals were asked how much of their coaching activity is prompted by AI-generated insights.

The findings reveal a gap between identifying insights and putting them into practice. Among contact centres that generate AI insights, 15% don’t use them to inform coaching at all. The information is available, but it never makes its way into coaching discussions.

Even where AI insights do influence coaching, their contribution remains limited. On average, 18% of coaching conversations are prompted by AI-generated insights.

Why AI Insights Don’t Always Translate Into Coaching

There are two main barriers preventing AI-generated insights from becoming useful coaching opportunities.

The first is establishing a reliable foundation. Insights depend on the accuracy of the scores behind them. Without checks to validate those scores, or when agents question their credibility, team leaders may hesitate to use them as the basis for coaching.

Many contact centres have yet to establish an automated QA programme with the governance needed to build this level of confidence.

To address this, organisations need to decide how frequently humans should review AI-generated QA scores and consider whether agents trust the scores produced by AI.

The second barrier comes from how insights are managed. Even when the findings are reliable, they often remain on a dashboard unless someone manually turns them into a coaching activity.

Dashboards highlight wider team patterns and trends, whereas coaching focuses on individual agents and the specific behaviours they need to improve.

Bridging that gap takes time and effort, but in many contact centres, nobody is explicitly responsible for making it happen.

When workloads rise and queues become busier, coaching is often pushed aside. QA that simply fulfils a requirement achieves little, and AI insights are just as ineffective when no action follows.

Not Every AI Insight Needs to Lead to Coaching

AI can uncover issues that have little to do with an agent’s performance. For example, conversation analytics might reveal a rise in repeat contacts caused by confusing wording on a monthly bill.

Coaching agents to explain the charge more clearly might help, but the repeat calls are unlikely to stop until the wording on the bill is corrected.

The key is to direct each insight to the people best placed to address it. This might be a team leader working with an agent, or someone in billing, product development or marketing who doesn’t usually have access to QA reports.

Before acting on an insight, establish what it reveals and who is responsible for resolving it. If it highlights an agent’s behaviour, it may be appropriate for coaching. If it points to a wider issue with a process, product or policy, it should go to the relevant owner, supported by enough evidence to understand the problem.

Accessible business intelligence reporting can make it easier to share findings across departments than relying on spreadsheets passed between teams.

How to Put AI Insights Into Practice Through Coaching

When an AI insight highlights something an agent could handle differently, there are several ways to turn that finding into a useful coaching opportunity.

Focus on One Behaviour and Find Examples in the Agent’s Conversations

A finding such as “first-contact resolution is down on the billing queue” is too broad to act on directly. Before the coaching session, identify one specific behaviour, such as ending a chat before checking that the customer’s issue has been resolved.

Then select two or three scored conversations from that agent that demonstrate the issue. Coaching tools can bring these interactions into the same platform, with built-in templates to help structure the discussion.

Build AI Insights Into Existing One-to-Ones

Most team leaders don’t have time to arrange separate coaching sessions for AI findings, and this isn’t usually necessary.

Instead, bring one or two relevant insights into the agent’s regular one-to-one. Keeping the focus narrow makes it easier to discuss each point properly, rather than rushing through a long list.

It’s also important to protect this time by scheduling sessions in the calendar and assigning responsibility for them. A documented coaching plan can help ensure these conversations still happen during busy periods.

Review Subsequent Scores to Measure Progress

AI QA scoring can help track whether coaching has made a difference by checking the same scorecard question against the agent’s conversations over the following weeks.

If the score improves, it’s a sign that the coaching has helped address the issue. If it doesn’t, revisit both the original insight and the coaching approach to understand what needs to change.

The insight may have identified the wrong behaviour, or the agent may need further support. If the underlying issue is a skills gap rather than an established habit, a targeted course delivered through a learning management system can provide additional training.

Making AI Insights Count

AI insights can only improve the customer experience if they lead to action. To improve agent behaviour and the quality of support customers receive, organisations need to turn findings into meaningful coaching conversations or pass them on to the people responsible for the underlying process.

Most contact centres already have the technology to identify these insights. The real difference lies in what happens next: how effectively teams move from identifying a finding to addressing it through coaching or wider operational changes.

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This post has been re-published by kind permission of ScorebuddyCX - view the original article.

Author: Hannah Swankie

Reviewed by: Megan Jones