We’re automatically logging and summarizing calls. I’m in a regulated industry so it’s not an optional activity and there are quite some requirements to how and

We’re automatically logging and summarizing calls. I’m in a regulated industry so it’s not an optional activity and there are quite some requirements to how and what we log. Interesting thing we learned is that agents tend to log during the calls, not afterwards. We now see (qualitative feedback) that they are less busy logging and therefore have more attention for the client, and (quantitatively) we see the calls are getting shorter and people with the tool are doing more calls per day. We do many millions of calls a year, so it sums out to a good number. Similarly, we have processes with 100s analysts with very high standards to their outputs. The traditional way is to have QA teams review and provide feedback for a few rounds. We’re introducing AI for the first round(s) of feedback to shorten the cycle time reduces context switches) and have the QA teams focus on final reviews. But I get your point. RAG knowledge bases for experts are in a hard spot. After a few months of employment, the experts tend to know the general knowledge well. As a result, the RAG-bot mostly gets questions about exceptions and niches, where it doesn’t perform very well, and mistakes might be expensive.