Out of all of the tech organizations, frontier labs are the one org you'd expect to be trying out cutting edge forms of support. Out of all of the different things these agents can do, surely most forms of "routine" customer support are the lowest hanging fruit?
I think it's possible for Anthropic to make the kind of experience that delights customers. Service that feels magical. Claude is such an incredible breakthrough, and I would be very interested in seeing what Anthropic can do with Claude let loose.
I also think it's essential for the anthropic platform in the long-run. And not just in the obvious ways (customer loyalty etc). I don't know if anyone has brought this up at Anthropic, but it's such a huge risk for Anthropic's long-term strategic position. They're begging corporate decision makers to ask the question, "If Anthropic doesn't trust Claude to run its support, then why should we?"
I come from a world where customer support is a significant expense for operations and everyone was SO excited to implement AI for this. It doesn't work particularly well and shows a profound gap between what people think working in customer service is like and how fucking hard it actually is.
Honestly, AI is better at replacing the cost of upper-middle management and executives than it is the customer service problems.
Nicely fitting the pattern where everyone who is bullish on AI seems to think that everyone else's specialty is ripe for AI takeover (but not my specialty! my field is special/unique!)
AI, for a lot of support questions works quite well and does solve lots of problems in almost every field that needs support. The issue is this commonly removes the roadblocks from your users being cautious to doing something incredibly stupid that needs support to understand what they hell they've actually done. Kind of a Jeavons Paradox of support resources.
AI/LLMs also seem to be very good at pulling out information on trends in support and what needs to be sent for devs to work on. There are practical tests you can perform on datasets to see if it would be effective for your workloads.
The company I work at did an experiment on looking at past tickets in a quarterly range and predicting which issues would generate the most tickets in the next quarter and which issues should be addressed. In testing the AI did as well or better than the predictions we had made that the time and called out a number of things we deemed less important that had large impacts in the future.