The Exchanges

Q these are the people who are having the interactions with the end customer. And you're right. Like if you can get the sort of change password questions out of the way, you now take this, this, um, division, which ends up like, which is, has been dealing with problems and you make them sort of the owners of the relationship. With the, with the end customer. Does that sound right?

A Yeah. If you think about it, um, you know, you have a long relationship. We talk about lifetime value in marketing, right? If I'm not mistaken in terms of how much is this dollar of email spend getting you? How much is this dollar advertising getting you? After that initial conversion, the only people who really talk to the customer are the customer service agents. You know, if we look at our own company data, 54% of our customers contacted us for support in 2024, but they represented 95% of revenue. Right. So the most important customers are speaking to you. They're speaking to you in what we would call the moments that matter where they need some help. And I think, you know, AI is, because it gives you this incredible potential to raise the level of every agent up to the level of the best and longest serving customer service agent you have with co-pilot technology and assistance, right? It also means that you can kind of like have an immediate response and kind of deal with things. To a certain extent, customer service is this human powered factory, right? The metrics are ones of throughput and effort. It's like tickets today, per day, time to first response. You know, all of these things where you're measuring productivity, average handle time. In an AI world, you know, especially when you're automating tickets, tickets per day is infinite. You know, I'll just, there will be…

AI assessment note: “After that initial conversion, the only people who really talk to the customer are the customer service agents.”

Q hand window and they get a chance to have a conversation, uh, with that bot. Um, I wonder, you know, because you, you're the chief technology officer of Zendesk. So you'll have a insight into this. Do you think that customer service is going to happen, um, on, let's say, client websites in the future? Or do you think it might migrate into, like, the big, broader bots like ChatGPT?

A I think that if the bots represent, um, our agents, it's clearly, clearly there's going to be some kind of migration, right? Already for a given brand, you know, if you want to know about some companies, you begin with a search in Google, and turns out Google's results are usually pretty good. I think the same is happening with ChatGPT. But when we get into like real service flows, where will I be saying to ChatGPT, I bought these shoes last week. You know, go, go figure out that order, return them, and generate me a, uh, generate me a packing label. Yeah, I think that's probably going to happen as brand short circuit. Now, is that the customer service flow so much as the action flow where we're actually moving towards systems of action that do that? I think it's all becoming integrated, and the nature is changing. On the back of it, though, everyone is still probably going to need to have those moments where Uh, a place to go to actually get contact and talk to a brand. We, um, you know, recently we kind of looked at, we took a sample of. Fifteen million or more, um, customer service conversations across send us customers. And we, we actually use chat GPT to classify the contact reasons or the intents and just kind of group them into cohorts. And you get, uh, something along the lines of 47% are basically, there was some kind of failure in the business. Right. There was someth…

AI assessment note: “clearly there's going to be some kind of migration, right?”

Q able to do this? Like, is that what, is that what's really needed is, I mean, of course some work on, on, you know, the company end, but, um, little better models, like sort of, I don't know, maybe they can solve the captcha so they can log into the system and then handle these requests. I mean, where do you think that that leap is going to be taken?

A I think that, um, better models will certainly be able to do a lot with that. I actually think what's really going to make a difference more likely than computer use models, computer using models is just the improvements in coding models. And that's a little bit basing the future on the immediate past. Coding models have gotten gotten so good recently, and they can make developers so productive that I think that that is going to unlock For a lot of internal builders and a lot of company builders, it's just going to unlock this potential to create, um, so many connections. Along with sort of tools that have agentic AI built in that can interrogate, you know, the API space of systems and create experiences. So I think we're going to see, um, what could be semi-daunting projects for people to take on just to automate another three percent of customer service tickets. Suddenly become things where the ROI is a lot clearer, and they'll be like, yeah, I could do that. I could, I could knock that up in cursor. I could get, you know, opening our codex to do that with me, and it wouldn't take me too long.

AI assessment note: “what's really going to make a difference... is just the improvements in coding models.”

Q And so take us through the continuum of, uh, someone who's adopting this technology. Like what does it look like from when they first get a taste of it to when they're fully deployed?

A I think as, as with a lot of use cases that probably your listeners are seeing, you begin optimizing, uh, human behavior and human potential, right? And in customer support, that's really looking at the human in the loop, um, capabilities and seeing what you can do. The other thing is you look at LLMs, you know, and they, they, they have an incredible world knowledge. They can generate content and they could reason, which makes them super useful for search. Generative search is taking over. You know, Gemini three was recently released. We're seeing the effect at Google of that kind of technology. And so the first thing our customers are doing is they basically deploy, you know, building up their knowledge and getting some of that generated with AI. They're deploying generative search and they're seeing, You know, upwards of 30, 40% of inquiries being handled by generative search. Users who spend, you know, probably two human generations learning to type into a box and process 10 blue links have suddenly pivoted, and they just want the answer and the results, and I think that's table stakes for customer support at this point. And then we look at, um, co-pilot experiences where you can, you know, think about customer service. There's high turnover. You know, you don't necessarily get time to train people. As new things happen in your company, new breaks, new fixes, you know, it's…

AI assessment note: “the first thing our customers are doing is they basically deploy... And then we look at”

Q And information about the client. Um, and, and of course there's going to be some automation of customer service. Um, some of those, uh, you know, easy things I imagine, like the password reset that you spoke about. Uh, how are you finding the models today? Are they enough for what you're looking to do? And what would you sort of wish for, uh, in the models of the future?

A I would say, um, we've spent probably the last two years, or you could think about, I think, all of SaaS and a lot of industry development where people are building apps on top of LLMs, right, has been sort of the, the lumpen proletariat of the developer class, building Guardrails and checks and balances and deployability, basically forcing non-deterministic libraries, and we're not used to do to programming against non-deterministic libraries, to behave deterministically. And for many of us, you know, we have products in market now, like Xendesk has 20,000 people using some kind, 20,000 customers using some kind of AI. I think we've gotten to a point where we're innovating on top of it and moving pretty quickly. So the next Frontier Model release, Kind of comes along and it's sort of like iPhone 16 to 17. It's like, oh, you know, here's a vapor chamber. Ah, great. But we've actually spent a couple of years dealing with hallucinations and dealing with unpredictability. And so it's great that the models have less of that, right? That is, that does make a difference. But, uh, the evolutionary improvements at the moment, the incremental improvements, um, on moving the needle. Like almost frontier use cases right now where it's like only the latest model will do is something like we have a, we have an agent that listens to every conversation that is automated and said, you know, ca…

AI assessment note: “we've gotten to a point where we're innovating on top of it”

Q you, if you had, let's say you could set a large language model loose for customer service, but through every interaction it learned, uh, to get better at what it was doing. Oh, I mean, that seems like it's like, you know, I think Satya Nandela was asked about it and he said, that's game, set, match. Um, do you feel the same way about customer service if that comes?

A I think, um, it's a, it's what we would like is sort of agent getting smarter every day, but what we have today actually, um, which I think is almost just as good. If you think about all of someone's customer support, right? All the human agents, the search, the articles, the workflow, the procedures, and the AI agents. If you think about it all as one unit, this is my, Service estate. Today, right? Things like the, um, Zendesk resolution loop, like we can, and this is, this is something that we do. We can look and say, you need to write this knowledge base article. You've gotten 10 conversations about this. It's probably time that you do something. Or the way the agents respond to this type of problem here, this type of return of this type of item over 30 days, you need to write a new macro, a new repeatable response that deals with it. And so if you think about it, if you think about service as a machine, AI is so good at giving insights, you know, if you kind of set it up and do it in the right way, that you can already do that. What would you do, you know, would it be incredible as an individual customer service agent or one, you know, if I had one AI agent that I think about as the one that talks directly to Alex and tries to automate, if that could learn from every single conversation, That would also be very, very cool. If, if you owned that bot, though, you'd probably b…

AI assessment note: “if that could learn from every single conversation, That would also be very, very cool.”