An AI Voice Insights platform projected to save 1.5M hours a year

Regulated firms record calls because they have to. Almost none of them read what they recorded. Insight about customers, inefficiency and emerging risk sits in an archive treated as a retention obligation rather than as an asset, while staff retype what was said in the meeting they have just attended.

Natural language processing, sentiment analysis and entity recognition applied across that archive change what is knowable: which themes are rising this month, which processes generate the most repeat contact, which conversations went wrong and how.

Consumer Duty moved UK firms from evidencing process to evidencing outcomes, and it expects them to monitor and act on the outcomes retail customers actually get, including customers in vulnerable circumstances. Conversation analytics is one of the few places that evidence naturally lives, which is also why it is a governed use of personal data rather than an analytics side project.

The discipline the NIST AI Risk Management Framework would apply is worth borrowing whatever your regulator: measure the system in the conditions it will actually run in, including accents, line quality and code-switching, document what it is not fit for, and keep a named human accountable for the decisions it informs. A sentiment model performing measurably worse on one customer segment is not a technical footnote under Consumer Duty. It is the finding.

We were not engaged on Consumer Duty compliance, we did not implement the NIST framework, and we claim no regulatory expertise here. We coordinated innovation, operations and risk for adoption readiness, and the platform reached proof of concept.

This is a proof of concept. It demonstrated the capability, it shaped the bank's strategic direction on AI platforms, and the 1.5M hours a year is a projection from that proof of concept rather than a saving anyone has banked.

Getting from a working prototype to a live platform inside a bank is not a modelling problem. It is data protection assessment, retention and consent, access control over recordings, resilience of the pipeline, monitoring for drift, and an owner willing to be accountable for what the system says. That is what most enterprise AI is stuck in front of, and it is a large part of why we now sell an audit before a build.