Contact Centre Performance Improvement Insights

Contact centre AI readiness is almost always framed as a technology question. This post argues it is a structural one — and sets out the four conditions that determine whether deployment will reduce volume or accelerate it, improve customer experience or redistribute effort, and deliver the business case or generate a more complex version of the original problem.

Contact centre AI not reducing volume after deployment is the finding that post-implementation reviews consistently attribute to the wrong cause. Implementation quality, adoption challenges, scope limitations - these are execution explanations for a structural problem. The real cause is either failure demand that the automated channel was never going to eliminate, or effort displacement that has added a layer of friction above the same underlying contact volume. This post nam

Most contact centres begin their AI implementation with a vendor conversation. The step that should come first - a contact centre AI demand diagnosis - is the one that gets skipped. It is the only analysis that establishes whether the contacts being proposed for automation are worth automating, whether the automated journey has the authority to resolve them, and whether the success metric being agreed will actually measure what it claims to. This post sets out the three-step

Chatbot containment rate has become the primary success metric for automated self-service in contact centres. It is also measuring the wrong thing. A contained contact and a resolved contact are not the same - and in operations where the repeat contact rate following automated interactions is high, the containment rate is reporting success while the demand is quietly returning through the voice queue. This post explains why chatbot containment rate is structurally identical t

The highest-volume contact types in most contact centres are not there because customers want to call. They exist because something in the system failed. When AI is deployed against those contacts without first classifying the demand, it automates the failure rather than removing it. This post explains what failure demand looks like in a contact centre, why automating it produces a faster and cheaper version of the same problem, and how a straightforward diagnostic exercise c

Contact centre AI automation conversations follow a predictable sequence: volume data, use cases, pilot, containment target, timeline. At no point does anyone ask what is generating the demand being automated. This post explains why - and why the answer isn't carelessness. It's structural. The vendor's incentive model doesn't require the diagnostic question. The buying side's political dynamics actively discourage it. And by the time the consequences are visible, the vendor r