Right now, in terms of conversations around generative language models, I heard a really interesting stat that I heard the other day, I think it was from, QuantumBlack, one of the expert firms out there, right? They mentioned that only about 11 percent of mentioned that they saw only about 11 percent of, um, AI applications within an enterprise made it out of sandbox and into production. And within that subset, with generative only about 3 to 5% of generative applications made it out of the sandbox environment.

Human mind is, is limited in our ability to imagine what other people, how other people might interact, the language that other people might use.

And so that is a huge challenge getting conversational solutions out of sandbox. They might perform well in a closed test environment, but they falter when they’re put into production. It’s especially difficult in voice, right? So, you know, you might have a sandbox chatbot that works really well, very clearly, you know, you’re typing to it, the spellings largely okay, the language model has a clear signal of what you’re requesting. Now you add to that speech recognition, you add to that speech recognition errors, you add to that, you know, a telephony line that might have background noise that might have static. There’s a whole order of magnitude in complexity in a production environment that is not as evident in a sandbox unless you’ve had that experience before.

With natural language software, there is just an inherent level of probability now that has not been evident in previous software paradigms, like deterministic software.

Um, now it’s, you know, I asked the model this and it provided me a certain question. I might ask it the same exact question again, and it might provide just a slightly different response, right? Now, how do you determine whether, and some of those responses might be subjective, right? So, um, or those answers, right.

And it becomes harder and harder to evaluate how to figure out when something has improved or performance is improving.

I have a tremendous amount of empathy for a contact center or a customer experience leader right now, right?

It is a difficult job to start right whether you’re a BPO or whether you’re doing it for an in-house call center, right? It is a difficult job. It is one that people have a high turnover rate, right? Like 30-40 percent is pretty normal. And part of it is because one, you know, consumers are willing to pay a certain price, right?

And, it’d be great if we were all willing to pay a lot more for our products and therefore the service could increase, but that’s not like reality, right? Things get pushed down over time. And so, um, contact center leaders are having to do and being asked to do more and more and more with less and less and less.

Right. And that affects not just the contact center leader, but I imagine BPR outsources as well. Right. Like they have to also do more, they also have to manage all of this retention. And one, one of the threats of conversation I hear in the industry is we need to create more sustainable career paths for a customer service or a customer support agent.

And I think the way to do that, the starting point of doing that is getting them into more value added troubleshooting, value added tasks. And that means, you know, we joke internally, right, Damien about like, Hey, we just want to reset passwords. Right. That’s like the stuff we want to do.

But that is part, that is one thread of the challenge in this industry is that is not interesting work, right? Being asked to clock in, clock out every day and help people reset passwords, right? Now, if AI can take that away, we can, we can resolve that. Then, you know, we can help, whether it’s an enterprise, like their in-house support teams, or whether it’s a BPO processor elevating into higher value work creates more meaningful careers for these roles. I think that’s part of the solution because the trends are there are just fewer and fewer people who want to do customer support jobs. The complexity of products keeps increasing or the complexity of our world keeps increasing and there’s more support inquiries.