# Zendesk CEO on AI for Customer Experience | CXOTalk #866

- **Show:** CXOTalk
- **Published:** 23 Jul 2025
- **Source:** https://podcasters.spotify.com/pod/show/cxotalk/episodes/Zendesk-CEO-on-AI-for-Customer-Experience--CXOTalk-866-e35ug1j
- **Transcript:** https://podcasts.opennash.com/shows/cxotalk/zendesk-ceo-on-ai-for-customer-experience-cxotalk-866-30e0e1/
- **Provider:** OpenNash Podcasts (machine-generated)

## In brief

Zendesk CEO Tom Eggemeyer argues that AI in customer support has moved past hype. Modern AI agents can reason and act on their own rather than follow scripts. They already resolve a large share of customer issues at costs and accuracy levels comparable to or better than humans. He says Zendesk's pricing, which pays only for resolved problems, shows that it believes this.

The main tension is between rapid technical progress and the slower work of making it succeed in practice. Results depend on clean company knowledge and policies, and on getting customers and staff to adopt AI. Eggemeyer says humans will remain for the hardest cases, but metrics, jobs and business models will all change. He recommends transparency, full quality checks and constant learning.

## Key insights

1. **Zendesk only earns money on resolved issues** Zendesk's automated-resolutions pricing charges only when AI actually solves a customer's problem, not when it merely handles an interaction. Eggemeyer says this lines the company's incentives up with its customers' goal of higher automation rates.
2. **AI results depend on clean company knowledge** An Australian e-commerce customer had five conflicting return policies, so the AI could not answer reliably. Eggemeyer says good policies and structured knowledge matter most, and Zendesk now has AI draft missing articles for humans to approve.
3. **Resolution rate claims vary widely by customer** Eggemeyer says some customers reach 85-90% AI resolution while others with complex cases reach only 20-30%. He calls vendor promises of 65-85% within days a bit of hype.
4. **Reviewing every interaction beats sampling one percent** Zendesk's quality assurance scores every human and AI interaction for accuracy, tone and resolution, using a different algorithm than a large language model. Eggemeyer says most companies review only about 1%, which misses confident-sounding AI errors.
5. **AI success can make human handling time rise** Automation removes the easy and medium cases, leaving humans with the hardest ones. Eggemeyer says average handling time will therefore climb, and leaders should expect it and shift toward metrics such as reducing repeat issues.

## Questions this episode answers

### How is today's AI customer service different from old chatbots?

Old chatbots followed fixed decision trees and often got stuck, then handed over to a human without context. Eggemeyer says newer AI agents are autonomous and can reason, which is the fundamental difference.

### Is AI actually more accurate than human support agents?

Eggemeyer says Zendesk's A-B tests (comparing two versions side by side) show AI is as accurate or more accurate on the top 50 to 100 common issues. Humans still do better on messages with several requests and on rare, complex cases.

### What does it mean that Zendesk gets paid only for resolutions?

Zendesk charges when AI fully solves a customer's problem, and nothing if the case goes to a human, even though it costs Zendesk money. Eggemeyer says this replaces seat-based and per-interaction pricing so Zendesk's success matches the customer's.

### Will AI replace human customer service workers?

Eggemeyer predicts that within five years 80% of interactions will be fully automated, while 10-20% stay human-led for complex or upset customers. He is optimistic that people will shift to new jobs such as managing teams of AI agents.

### What does 'white box' AI mean and why does it matter?

Eggemeyer uses 'white box' to mean showing how an AI agent reached each decision, as opposed to a hidden 'black box'. He says this lets companies spot mistakes and improve the AI with plain-language instructions.

## Facts

- **Tom Eggemeyer** [0:01:29]: "we can solve almost instantaneously more than 30% of customers interactions."
- **Tom Eggemeyer** [0:02:45]: "There are some of our customers that are getting 85 or 90% resolutions, but there's some customers with more complex use cases that are getting 20 or 30%."
- **Tom Eggemeyer** [0:04:18]: "they had 5 different policies, for instance, on from different parts of the organization on what constituted a return."
- **Tom Eggemeyer** [0:05:52]: "I remember, uh, when OpenAI launched, I think it was October 2022, um, and there was an amazing amount of buzz about it"
- **Tom Eggemeyer** [0:06:23]: "I remember this vividly in December 2022 with some of our, uh, lead, um, we had a small group of AI, uh, data scientists and engineers, about 10 of them"
- **Tom Eggemeyer** [0:06:23]: "Uh, I just started that month as CEO"
- **Tom Eggemeyer** [0:08:12]: "I worked for a company called Genesis that was 100% prem."
- **Tom Eggemeyer** [0:12:03]: "we have about 30,000 knowledge base customers."
- **Tom Eggemeyer** [0:12:03]: "we've launched something called generative search, and this is free part of our product for knowledge bases"
- **Tom Eggemeyer** [0:14:00]: "usually, an AI agent is as accurate or more accurate than a human agent."
- **Tom Eggemeyer** [0:19:23]: "we actually score every single AI agent or human agent interaction for accuracy, for tone, for resolution."
- **Tom Eggemeyer** [0:19:53]: "Right now, most companies are doing, like, 1% of interactions, they're analyzing."
- **Tom Eggemeyer** [0:28:34]: "We still think ten or 20% of interactions are gonna be human-led, and they're gonna be the most complex interactions."
- **Tom Eggemeyer** [0:32:33]: "we've come out with our new pricing model called automated resolutions. We only get paid if we solve a customer's problem with you, if we resolve it."
- **Tom Eggemeyer** [0:33:53]: "In the past, it was seats. We still have some seats. Some companies made the twit- uh, the switch to interactions."
- **Tom Eggemeyer** [0:34:18]: "they would've gotten charged $2 for an interaction, even though I was more dissatisfied"
- **Tom Eggemeyer** [0:36:19]: "your average handling time is gonna go way up, okay?"
- **Tom Eggemeyer** [0:42:43]: "we do 4.6 billion resolutions a year."
- **Tom Eggemeyer** [0:46:33]: "We've trained, um, over 10,000, um, people, uh, that might not have the same kind of access"
- **Tom Eggemeyer** [0:47:46]: "we've opened up like an enterprise license for ChatGPT. I use it every day."
- **Tom Eggemeyer** [0:48:21]: "We believe at Zendesk that 80% of interactions will be through automated resolutions in five years"
- **Tom Eggemeyer** [0:44:40]: "we just, uh, acquired a company that does some really cool stuff in reporting and analytics"
- **Tom Eggemeyer** [0:31:26]: "some of our best customers don't want their name out and don't want the results out"

## Transcript

**[0:00:00] Michael Krigsman:** Welcome to CXOTalk #866. I'm Michael Krigsman, and we are cutting through the hype around artificial intelligence and customer experience with Tom Eggemeyer, CEO of Zendesk. Tom will share honest, real-world lessons from Zendesk's AI journey, what's worked, what hasn't, and how AI is reshaping customer expectations. Tom, welcome to CXO Talk. I'm delighted to chat with you.

**[0:00:34] Tom Eggemeyer:** Great to be here, Michael, and I'm delighted to be number 886.

**[0:00:38] Michael Krigsman:** Everybody has received messages from Zendesk. We submit a support request, and we get something back from Zendesk. So, just briefly, give us some background, tell us about Zendesk.

**[0:00:52] Tom Eggemeyer:** Zendesk is a AI-first customer support software company. We're all about helping companies resolve their customers, their employees, or their fellow business, uh, issues.

**[0:01:04] Michael Krigsman:** What's going on with AI and customer experience today?

**[0:01:09] Tom Eggemeyer:** When we think about AI from a customer support perspective, we think there's just huge, huge gains for companies, employees, and consumers from an AI perspective. The it's not hype, it's actually real.

**[0:01:22] Michael Krigsman:** So, when you say that AI is disrupting, can you elaborate on that?

**[0:01:29] Tom Eggemeyer:** AI is actually solving customers problems. Uh, the hype, I've I'm old enough to know 20 years ago, we talked about machine learning or predictive analytics. We're gonna be able to solve customers, uh, problems, quite frankly, without human intervention. Uh, and it's been a little bit of a hype cycle for the past 20 years, but we're actually seeing that happen right now. Um, we have one product, uh, called, uh, AI Agents Advanced, where you put a bot, uh, into, uh, one of our customer environments, and with 3 clicks of a mouse, taking some knowledge from the company, we can solve almost instantaneously more than 30% of customers interactions.

**[0:02:07] Tom Eggemeyer:** And what's interesting is the customers are more satisfied, it costs less, it's more accurate, and you can do it with 3 clicks. And that's kind of the promise of AI is always on, fantastic accuracy, lower cost for companies so that they can go put their finite resources into maybe solving the the root causes that are causing them having problems with their customers.

**[0:02:30] Michael Krigsman:** There's so much discussion around AI, and you've just presented, can I call it a a kind of idealized version, or is it totally realistic, or where's the gap?

**[0:02:45] Tom Eggemeyer:** The gap is a couple things. Um, number 1, you know, I see some people in the marketplace, you know, guaranteeing 65 to 85% of, uh, interactions can be resolved with AI within a matter of days. And I think, um, that's hyping a little bit. There are some of our customers that are getting 85 or 90% resolutions, but there's some customers with more complex use cases that are getting 20 or 30%. So I think part of the hype is it kind of depends on the customer type, what kinda interactions they're having with their employees or with their customers on what kinda resolution rate you can get.

**[0:03:19] Tom Eggemeyer:** The second thing, there's a little bit of hype is, um, right now, you need to have something called a knowledge base or a bunch of information or other knowledge, you know, structured data so that you can get resolution rates quickly. And that's not always the case, and so we gotta be honest with customers and honest with consumers about that. And then third, there are still mistakes that AI makes, just like humans. Uh, and we don't want to underestimate that, and we want to make sure that we get as close to 100% accuracy as possible.

**[0:03:49] Tom Eggemeyer:** What's interesting is we've done some A-B testing, and we see less hallucinations from AI than we see mistakes from humans, but it's interesting that we use different nomenclature. We call AI agent, you know, mistakes, hallucinations, and human agent mistakes, mistakes.

**[0:04:06] Michael Krigsman:** When things are set up in the right way, that the data's in the is organized properly and so forth, then the AI does a great job, is essentially what you're saying.

**[0:04:18] Tom Eggemeyer:** Exactly. I was with a customer, um, about a month ago in Australia, for instance, and, um, what they uh, what we found out working with them is they had 5 different policies, for instance, on from different parts of the organization on what constituted a return. It was an e-commerce company. And when you have data like that, of course, the AI is not gonna get it perfect because it's getting input at 5 different, you know, policies that are slightly different from each other.

**[0:04:43] Tom Eggemeyer:** And so, really having good policies, having good knowledge, having good information is really, really key. Now, we can help and, uh, you know, other players in the the market can help create that knowledge. Like, one of the things we do now is we see a bunch of the customer interactions, and we see you have a gap right here. In the past, we would have said, you have a gap, you should go solve it. Now we say, Wait, you have a gap.

**[0:05:06] Tom Eggemeyer:** Here's a recommend it. We write it for you, okay? The AI writes it for you, a recommend it, um, article, a recommend a policy. Do you want to accept? Do you want to decline? Do you want to edit it? So the world is kind of flipping on itself right now. But, like you said, data is really, s you know, structured and unstructured data that's consistent, that's publishable, and that's readable is really important in this AI journey.

**[0:05:32] Michael Krigsman:** You mentioned this phrase, AI journey. I was really interested. At what point did you recognize that AI in customer support was so important that you had to would it be is it too strong a term to say, transform the company around this?

**[0:05:52] Tom Eggemeyer:** Absolutely transform the company because, um, the way we look at it at Zendesk, we can be the disruptor in AI customer support or we're gonna be disrupted. Uh, and we were the disruptor 17 years ago with customer support helps help desk, and we wanna do that again. I remember, uh, when OpenAI launched, I think it was October 2022, um, and there was an amazing amount of buzz about it, and just thinking about the implications of it, uh, in the summer and the fall of 2022, and I was over in our Lisbon, Portugal office.

**[0:06:23] Tom Eggemeyer:** I remember this vividly in December 2022 with some of our, uh, lead, um, we had a small group of AI, uh, data scientists and engineers, about 10 of them, and, uh, I asked for a roadmap. Uh, I just started that month as CEO, and, uh, we had a bunch of great ideas, but we didn't have a roadmap. And I think talking to industry experts in the summer and the fall of 2022, uh, w- you know, I came to conclusion that just like internet, just like mobile, just like premise to cloud, there was gonna be a major, major disruption.

**[0:06:53] Tom Eggemeyer:** I think some people came to that maybe 6 or 12 months ahead of that, and we did not have a roadmap, we did not have enough people on it, we did not we're not thinking about it. And so we spent a couple days, uh, writing on a whiteboard. I wish I would have taken pictures of it in Lisbon, Portugal about what kind of first product that we wanted to launch. Uh, and so that was kind of the aha moment for me.

**[0:07:14] Tom Eggemeyer:** Uh, uh, you know, I would have thought it would have been the Bay Area, but it was actually in Lisbon, Portugal.

**[0:07:18] Michael Krigsman:** You mentioned disrupting yourself, that's a hard thing to do. Most of the time, businesses get disrupted because there's a competitor, there's something going on out there, and they have no choice. But in this instance, you saw the tidal wave coming, and so you took proactive steps and say, Hey, we need to change. It seems like that's what you were just describing.

**[0:07:42] Tom Eggemeyer:** I don't think we had an option, honestly, Michael, you know, if I'm really honest because, uh, I the the customer support, like we have a stat, we think 80% of customer interactions with companies are gonna be through AI within 5 years, and a 100% are gonna involve AI in some way within 5 years, meaning humans will use AI. So, we didn't have a choice, but we have a gr- lot of great Zendeskians employees that saw this wave coming and saw that, Hey, we were the disruptor 17 years ago.

**[0:08:12] Tom Eggemeyer:** Uh, Mickel and the whole Zendesk team disrupted help desk and customer service, and we wanted to be at a, eh, what we wanted to, uh, do it again. So, um, I would say we're fortuitous that this was coming, that we had some of the building blocks and pieces and the foundation, uh, but also, it's just an imperative that if we didn't do it, and I had seen in the premise to cloud contact center world, I worked for a company called Genesis that was 100% prem.

**[0:08:38] Tom Eggemeyer:** It's now, uh, one of the leading cloud providers of contact center. They went we went through that metamorphosis where the other two leaders of contact center prem didn't go through it, and so it's easier to do as a private company, but there's definitely an imperative to do it because there's gonna be winners and losers here, and I I really feel responsible first for our customers to give them value, second to our employees, and finally to our shareholders that we need to disrupt ourselves.

**[0:09:03] Michael Krigsman:** I want to remind everybody that there is a tweet chat taking place right now on Twitter X, and you can ask your questions using the hashtag CXO Talk. And if you're watching on LinkedIn, just pop your questions into the LinkedIn chat. And we have some questions that are coming up. But I'm I'm still really interested in this transition. So, as as you were moving from the pre-AI world into AI, did you get any resistance, did you from from stakeholders, from your board, from employees, from your customers?

**[0:09:44] Michael Krigsman:** Cause it's a big change, and change is always hard.

**[0:09:47] Tom Eggemeyer:** I don't know if it's resistance. I I'll be honest, with our board, it's actually been, How can you go bolder? How can you go bigger? Cause they see the transformation. So, if anything, I we have a great board, and I say they do strategic agitation, and their agitation again is, Go faster, think bolder, think bigger on how you can really give value to your customers. I think there's different levels of maturity within our customer base. Some people are, like, want to be cutting-edge.

**[0:10:10] Tom Eggemeyer:** Some people, um, were, uh, over the last couple years saying, Hey, is this gonna be real? Is this really gonna disrupt? Are we really gonna be having AI agents, co-pilot, generative search for knowledge bases, really transform customer support? I think the vast majority of customers are saying right now, uh, you know, after these two, two and a half years, that AI is real, AI is disrupting customer support. So, I think it's gone from a question mark two and a half years ago with our customer base to a consensus.

**[0:10:37] Tom Eggemeyer:** The board has already been there. And employees, I think, they ask real good questions, is like, How are we gonna be the disruptor and not just be disrupted? How are we gonna do all the core things that we need to do on our core customer service omnichannel platform while adding AI? Um, am I gonna be left behind if I'm not part of the AI revolution? And so, we have, you know, coffee chats, we have, uh, uh, AMAs, uh, where we talk to the employee base, but if anything, I think the employee base is really, really excited about being the disruptor, being number 1 in customer service, and really serving our customers and getting real, true, tangible value to our customers

**[0:11:14] Tom Eggemeyer:** with AI.

**[0:11:14] Michael Krigsman:** Now would be an excellent time to subscribe to the CXO Talk newsletter. Go to cxo talk dot com. We'll keep you updated on upcoming shows. Tell us about the changes that you drove into the product. So, how is customer support rooted in AI different from what came before?

**[0:11:38] Tom Eggemeyer:** There's a couple things. Where we are now and where we're going in the future. Where we're going right now is we think we can automate a lot of customer interactions. So, for the customer, again, that's what it's all our customers customer, it's all about, it's quicker, it's more accurate, and they're able to solve their problem in a really good way. For companies, they're spending less on it, and so, what has happened right now in AI is a little more reactive, and I'll give you a couple use cases.

**[0:12:03] Tom Eggemeyer:** In the future, it's gonna be more proactive, but, in the past, we have about 30,000 knowledge base customers. So, think you go into a, um, a company's website, and you say, How you type in, How do I return these pair of shoes? A knowledge base in the past would come up with five or ten blue links, you know, and you'd say, and you'd go pick one on how you return shoes. What happens now is we've launched something called generative search, and this is free part of our product for knowledge bases where, like a Google Gemini search, you actually get the answer, you know, on how to return the shoes.

**[0:12:36] Tom Eggemeyer:** And it's getting even better with agentic AI, where you might be able to do that whole process, uh, including going back to an ERP, for instance, all, you know, the actions all through an AI agent. So, that is kind of reactive still. You then go to an AI agent, maybe, from the generative search, and an AI agent can act like a human agent, do all the things a human agent can do. Sometimes, generative search and an AI agent's not gonna solve the problem, and so you go to a human agent.

**[0:13:03] Tom Eggemeyer:** But that's all reactive right now. We're kind of flipping that right now in customer service, where it's proactive, where we're taking all this information from the searches you make, Michael, the interactions that you have with an AI agent and human agent, and we're saying, These are seven root cause issues that are going on. We keep sending out black shoes when Michael wanted red shoes, and then we have customer interactions. What do we need to go do?

**[0:13:27] Tom Eggemeyer:** And, like, have an insight into the supply chain, why do we have a problem there? Through AI. And so, it's getting flipped on its head right now, customer support, from reactive to more proactive, from reactive to more root cause analysis, and from reactive to actually actions of solving root cause problems that a company is having interacting with its employees or customers.

**[0:13:48] Michael Krigsman:** And, again, I want to come back to this this comment you've made a couple of times on the accuracy. Can you talk about that? Because, of course, this is a a key dimension of everything here.

**[0:14:00] Tom Eggemeyer:** We've done a lot of A-B testing, and on, let's call it the top fifty or a hundred use cases, usually, an AI agent is as accurate or more accurate than a human agent. When you get to really complex, maybe a customer asks three different things in a query, they say in an email or a chat or a phone interaction where there's voice AI now, you say, How do I return my shoes? I want a credit, I want to order a new pair of red shoes instead of my black shoes, and I want to go order a sweater.

**[0:14:32] Tom Eggemeyer:** When you get three or four queries in a row, uh, like in one message, sometimes AI does not nail that. I think a human does better still, but it's getting better. Um, the other place where I think AI is not quite there is a really, really complex corner case that, again, the data does not, uh, solve where human needs to get involved. On the other hand, I think AI, it's interesting. Some of the data that we've had, some of the research recently, data is doing just as good or better, uh, the data says, on empathy, which is surprising.

**[0:15:02] Tom Eggemeyer:** I would have always thought humans are gonna be more empathetic, uh, but there's some in uh, interesting research piece, and I'm forgetting where I read it recently, where doctors, an AI agent doctor and AI or a human doctor, AI agent doctors are considered more empathetic. And so, we're starting to see, um, more empathy, more caring from AI agents, which is something I never thought we would have had.

**[0:15:25] Michael Krigsman:** We have come a long way from the chatbots, which were simply prescriptive, and, frankly, for a lot of users, honestly, a waste of time. Th- uh, things have th- th- there's a very different universe now.

**[0:15:41] Tom Eggemeyer:** Exactly, so those chatbots that you probably have in your mind that would drive me, uh, you know, we're they they would be frustrating for me, frustrating for you. We're kinda decisions tree, you know, if, then you go, yes, you kinda go down the decision tree. And a lot of times, again, the inquiries were more complex, and you get stuck at part of the decision tree, and then you get handed over a human. The human wouldn't agent wouldn't have the context of that chatbot.

**[0:16:05] Tom Eggemeyer:** What's different now with agentic AI is there are these AI agents are basically autonomous and can reason. And that's the fundamental difference from the chatbot experience everyone had. And so, what I tell everyone, I know you probably dealt with chatbots in the past, but if you drew you deal with a true agentic AI agent right now, I think you're gonna be wowed by the experience. It's not perfect, just like human being AI age or human agents are not perfect, but that's the fundamental difference.

**[0:16:31] Tom Eggemeyer:** You're going from a kind of a decision tree, pre-baked, to something that's agentic, that's something that's reasoning. What we think is really important as part of this is that you need to be white box, not black box. And if I were a company or consumer, like, how did the especially a company, how did the autonomous AI agent, the agentic AI agent, make decisions? What was their thought process? One of the things we do is we lay that all out, you know, for every single interaction an AI agent or a bot has with a cons- customer so that companies can go refine that by giving natural language instructions to it to keep getting better and better.

**[0:17:08] Tom Eggemeyer:** I think that's really, really important because if you're black boxing it as a company, you don't know why the AI agent is doing something, so it's harder to improve.

**[0:17:17] Michael Krigsman:** I use large language models, multiple LLMs, every day, all the time, and for the reasoning models, when they expose their logic, it is so useful.

**[0:17:30] Tom Eggemeyer:** I agree, and I'll give you an example of my personal life. Um, my wife's, uh, fa- grandfather, gr- he was born in the US, but he grew up in Ireland, and my wife wanted to know, Can she get Irish citizenship? I took our ancestry dot com, gave the password to a large language model, connected to an email account that I made up, um, and, uh, created, uh, made up, created, and said, Can you go research and grab records to support the claim that my wife can go get Irish citizenship?

**[0:18:00] Tom Eggemeyer:** It was amazing what it was able to do. It requested parochial grade school records from an Irish parish school. It got me all this information, then wrote a legal memo on why it thought my wife was able to get Irish citizenship. The only problem, kind of to your point, Michael, is it got the legal conclusion wrong. It looks I, uh, it said the legal conclusion was yes. It's probably a fifty fifty corner case, eh, uh, that she got it, but it exposed some of the logic and some of the reasoning, and that's how I caught that it might have been wrong on its conclusions.

**[0:18:34] Tom Eggemeyer:** And that's why I just think it's, first of all, cool business case, right? You can take your tree from ancestry dot com, create an email account, get a legal memo, document supporting that by just giving some prompts. But on the other hand, that's why it's so important to give that white box treatment to really understand, uh, the logic behind it, so you can still see flaws, cause there's flaws and mistakes, just like human beings would make.

**[0:18:55] Michael Krigsman:** When the AI makes a mistake, and a customer service agent is actually working with a customer, do you have ways of trying to trap that, or how how is that get managed? Because the mistakes can be insidious, right? The LLMs speak with the full confidence of quote, unquote, someone who knows they're absolutely right all the time.

**[0:19:23] Tom Eggemeyer:** Totally. So, one of the things we think we differentiate, and we think is important for any player in customer service, is we have something called quality assurance, and quality assurance is using a different a algorithm than a large language model, and we actually score every single AI agent or human agent interaction for accuracy, for tone, for resolution. And we find through that where humans are making mistakes or AI agents are making mistakes, and we surface that.

**[0:19:53] Tom Eggemeyer:** And so, again, the white box, um, and and you're totally right, one of the flaws right now is a AI agent will say with absolute certainty, uh, that my wife can get Irish citizenship, where it's a little murky, and but be 100% confidence. And and so, we have this real strong point of view, you should be, as a company, doing quality assurance, not on a sample size. Right now, most companies are doing, like, 1% of interactions, they're analyzing.

**[0:20:19] Tom Eggemeyer:** We say to inter- a analyze 100% of interactions, and you can get summaries of that now based upon AI, and that's how you're gonna spot issues, whether humans or AI agent. And what's really key about that is sometimes you're gonna see the AI agent is handling a question or a query better than the human agent. But sometimes you're gonna wanna have with, like, my eighty-year-old parents, my mom will be upset, she's not quite eighty yet, but my father my father's eighty, um, you're gonna wanna have them talk to humans, they're not gonna wanna talk to an AI agent, and you want to get that AI agent accuracy into the humans.

**[0:20:52] Tom Eggemeyer:** And so, we really think quality assurance systematically is so important in the AI era.

**[0:20:58] Michael Krigsman:** So, the AI is also enabling you to have a far more granular type of ongoing QA than was possible in the past.

**[0:21:10] Tom Eggemeyer:** Allowing us to have more granular, more ongoing, and what's the great thing is surface those insights, whether our mistakes, and propose actions, whether go build this policy, go change, uh, you have m you have policies that are conflicting with each other, go make sure that you create a policy here or a piece of knowledge to go solve it. So, has more granular insights, it's more personalized, and with agentic AI, can propose actions and make actions to go hit the root causes of customer dissatisfaction.

**[0:21:39] Michael Krigsman:** Let's jump to some questions, and the first question is from Greg Walters. He actually asks a couple, but I'm gonna go to his second one, and he says, Regarding disruption, AI eliminates the middle person. Do you see a time where Zendesk is more than a component of and a tool for customer service, becoming, quote, customer support and service, the department?

**[0:22:07] Tom Eggemeyer:** I know some people, some companies are out there, um, there's something called BPOs that will say, you can outsource a lot of your customer interactions to us, and we'll be in charge of the technology and the people to solve that. I think in the future, um, our platform needs to be reactive, uh, for our companies, but also proactive, and, again, it's gonna be interesting. It's gonna change the dynamic. Right now, we're all about the reactive customer issues.

**[0:22:32] Tom Eggemeyer:** I Greg, I think it's a great question, by the way. Um, in the future, I think we're gonna be solving problems before they even occur, uh, you know, with our companies. I don't know if we're ever gonna be the customer service department, uh, itself. Um, I think we're gonna be a platform to help the customer service resolve issues before they happen, and when they do happen, um, solve them. But we have debates, and I tell everyone this, every week, customer service is massively changing.

**[0:23:00] Tom Eggemeyer:** Every week, there's new AI innovations, and we've have had discussions on how does our platform evolve, and are we gonna be taking more and more of the load from customer service departments in the future? I think it's a really insightful question.

**[0:23:12] Michael Krigsman:** Another question from LinkedIn, and we'll jump to those questions on Twitter shortly. This is from Brandon Byrd, and Brandon says, Are there specific verticals where Zendesk Zendesks plans to double down with AI to gain a first-mover advantage?

**[0:23:32] Tom Eggemeyer:** We think over the next five years that vertical industries, the more deep that you can get into tasks and procedures and expertise, the better you're gonna be able to solve your customers employees or your cust- our customers customers issues. And so, we right now have a really, really good foothold in e-commerce, fintech, manufacturing, um, and I could go on with the list, uh, tech, and so, we are going deeper and deeper and taking our learnings from those verticals and applying them to our customer service AI-driven resolution platform.

**[0:24:07] Tom Eggemeyer:** And so, I think that's gonna be one of the secret sauces where companies help with help their customers get more value by differentiating on a vertical or industry ba- uh, basis, and so, it's already happening now. I think we're in the early innings or the early, uh, part of the match, uh, for your international audience, but I think you're gonna see over the next five years a lot more industry specific solutions and industry industry specific specific knowledge, and industry industry specific AI.

**[0:24:36] Michael Krigsman:** We have a question from FireStar, who says, It feels like we're finally past the AI is coming phase and into actually making it work for customers.

**[0:24:50] Tom Eggemeyer:** The technology works, but I think where we are in the fra- the the, uh, next evolution is getting it adopted. Um, it's interesting, we see customers maybe don't want to adopt it on on one end, and so, one of the things we tell our customers to do, I've seen some and this is learning from other customers, is, you might wanna say, I have an AI agent available right now for you, or do you wanna wait five minutes for a human agent?

**[0:25:14] Tom Eggemeyer:** And so, a lot of people will test the AI agent because they've had the bad experience, Michael, you had with the chatbots in the past, and so, you kinda wanna nudge them to go try the AI agent. We've also have the human agents where there's something called Copilot they're using, and they've been using other tools, and so, how do you go nudge them to go get adopted? And once they get, they see the power of Copilot, they'll start using it more and more, and then just implementing with the customer to make sure that you're getting the data right, you're structuring the policies right, you're really taking agentic AI and starting to run through your business process and workflows and creating

**[0:25:49] Tom Eggemeyer:** new processes and workflows with AI. There is still some adoption, uh, challenges in the marketplace, and just the fact that you can do a lot with three clicks, but it might take you a couple weeks or a couple months to get the full value stack or beyond that, the journey, is another, you know, um, um, problem with adoption right now. So, I think we've gone from, Does the technology work? to how do we get employees, customers, and autonomous AI agents, all three really embracing that technology, the adoption curve, the implementation, the adoption, I think, is the next frontier that, you know, Zendesk is dealing with and other companies are dealing with.

**[0:26:27] Michael Krigsman:** It's a great point, and, of course, across different industries and different companies, as you mentioned earlier, there are varying degrees of maturity, which has a direct impact on that adoption capability.

**[0:26:40] Tom Eggemeyer:** Definitely, and, uh, it's really interesting. Um, we're seeing, um, you know, when you talk about a small customer, um, they're looking for instantaneous, um, impact, and they're looking for the three-click solutions. When you talk to an enterprise customer, they're really looking for embedding AI deeply in tasks and procedures, changing their workflow workflows, inventing new workflows, having actions that, uh, takes a like I use the return example of shoes across the ERP, across the supply chain, to really interconnect it.

**[0:27:13] Tom Eggemeyer:** So, different customers are asking for different things, and I I kinda look at it, there's a maturity curve for customers. There's a segment, whether you're SMB, mid-market, or enterprise, and there also is geographic differences. People in the Bay Area, for instance, are ready to embrace. I'm originally from Ohio, my parents are from Kentucky. Um, when I talk to customers in Ohio and Kentucky, they might be a little more, you know, where the Bay Area was a year ago.

**[0:27:37] Tom Eggemeyer:** So, you have this kind of Rubik's Cube based on geographic region, segment, and maturity of the customer, where they're ready to go, but I think largely we're getting over, is this hyper-real, and people are accepting that it's real.

**[0:27:49] Michael Krigsman:** I have a lot of questions of my own, but I'm gonna I really like to prioritize the questions from the audience. The audience, you guys are I always say this, you guys are really smart. We have a question I'm s still on Twitter X, and we're gonna jump to LinkedIn in a moment. Question from Elizabeth Shaw on Twitter, who says, As AI-enabled customer support functions increase, especially with AI agents and then agentic AI, how important will the human touch be, and when will human touch be the right thing?

**[0:28:27] Michael Krigsman:** So how important is the human touch, and when do you need to loop in the human from the electronic agent?

**[0:28:34] Tom Eggemeyer:** We still think ten or 20% of interactions are gonna be human-led, and they're gonna be the most complex interactions. They're gonna be segments of your customer base. Maybe it's your top customers, uh, and and there could be, um, people that just wanna talk to a human. And so we think there's gonna be two or three areas where humans are gonna be still the central focus point, um, of a of a resolution, and sometimes, even though I said sometimes the AI agents are giving more empathy, if a customer is really upset, a lot of times a human being can do that really, really well.

**[0:29:08] Tom Eggemeyer:** So, I guess that would be the first point that humans, we believe, are still gonna be in the loop. The second thing that we think is really exciting is I know there's been a concern that people are just gonna start cutting human agents, and, um, what we've seen is something different. We call it, uh, there's been a service deficit up until now. I think most people would say when they interact with brands, whether they're a business or consumer, they might not get the best experience, and so they there's this service deficit.

**[0:29:36] Tom Eggemeyer:** We think that AI is gonna give a service dividend where the humans are gonna be able to free up to spend more time on the root cause of some of these bigger issues. They're gonna be able to manage the AI agents. So, you might have a human agent managing a team of AI agents to what we talked about before, Michael, to make sure that there's accuracy using QA and other tools. And we think the service dividend is just gonna go increase customer satisfaction again, whether that's B2B use case or B2C use case or an employee, because you're gonna be dealing with more of the root cause and preventing things before they happen, and you're gonna get more personalized.

**[0:30:11] Tom Eggemeyer:** And so, we think there's gonna be a couple reactive use cases. We believe, you know, people need to remember that there's always a human at the other side, even though and I'm sure we'll talk about it, Michael, in twelve to eighteen months, you're probably gonna be using your own bot to go talk to a company's bot. At the end of the day, there's still that human on the other side of the interaction, and we need to remember that.

**[0:30:32] Michael Krigsman:** And this is from Preeti Narayan, and she says, As AI reshapes customer experience, how do you see industry verticals like retail, healthcare, and financial services evolving their customer engagement models? Are there industries where AI-driven CX has become a strategic differentiator rather than just an operational tool? And how is Zendesk positioning itself to lead in those transformative shifts? I'm just gonna make one request to people asking questions. Keep your questions simple because I'm reading your questions, and I need to make sure I get it, okay?

**[0:31:16] Michael Krigsman:** So, AI-driven CX becoming a strategic differentiator rather than just an operational tool, and how you're positioning to lead in these transformative shifts.

**[0:31:26] Tom Eggemeyer:** What's really interesting is we are seeing some customers see it as a, um, a transformational tool that can really differentiate their business. And I'll give you an example. Um, we have more and more customers that don't wanna be customer references because they don't wanna let their competitors know that they are getting seventy or 80% of their interactions solved by AI agents, and they're having this service dividend that they can go reinvest. And so, some of our best customers don't want their name out and don't want the results out, and to me, that's kind of the proof in the pudding that they are getting amazing automate automated resolution rates.

**[0:32:03] Tom Eggemeyer:** So, the AI is solving the problem before it goes to a human, and they see that as a competitive adv- uh, advantage. The way we're trying to do that is we really focus on the what we call the resolution platform or automated resolutions and the whole system of the resolution platform. So, we are looking with our customers on a day-to-day, week-by-week basis on what percentage of your interactions have you resolved and have you automated? And, again, like we talked about Preeti, this was a good question before.

**[0:32:33] Tom Eggemeyer:** We're getting deeper from a vertical perspective on that, and when we get those high automation rates, we really think you can go into that service dividend land, and so that's how we're positioning ourselves with our customers. We're not perfect, but we're really trying to drive our customers to higher and higher automation rates, and that's why we've come out with our new pricing model called automated resolutions. We only get paid if we solve a customer's problem with you, if we resolve it.

**[0:33:01] Tom Eggemeyer:** But it has to if it goes back to a human, we don't get paid for that interaction, even though it costs us money. And that's a way for us to kinda lead the way, uh, in customer service and put our, you know, put our operating model totally in line with our customers, um, because they care about driving up the automation rate, the automated resolutions, and we're in the exact same boat because about how we price.

**[0:33:24] Michael Krigsman:** I like that, putting skin in the game. Historically, software companies, uh, you know, going back to on-prem, they sold the product, threw it over the wall, and if the customer used it or didn't use it, they still paid. And now you're going to the opposite extreme where Zendesk has a stake in the customer outcome as opposed to the process of getting to that outcome.

**[0:33:53] Tom Eggemeyer:** We talked about transformation and culture a little before and employees. This is a big transformation for our customers, for our employees, and for our shareholders because, um, how we look at, um, you know, uh, operating model. In the past, it was seats. We still have some seats. Some companies made the twit- uh, the switch to interactions. Um, and now the next evolution is to outcome-based pricing or really aligning with your customers. I'll I'll give you an example.

**[0:34:18] Tom Eggemeyer:** I was on an airline, I won't say where in the world recently, and I noticed that I was gonna miss my connection. I got on their app I think they had a beta going on with a AI agents, and I got about 90% done solving my problem of rebooking my connection. Unfortunately, took off, I turned off my phone, no Wi-Fi. Um, I landed, the AI agent hadn't solved my problem. I'm confident that airline maybe they didn't get charged, but they would've gotten charged $2 for an interaction, even though I was more dissatisfied cause I called a human agent then, and the human agent didn't have any of the context from the AI agent, and so, I felt like

**[0:34:55] Tom Eggemeyer:** I was repeating myself. And oh, by the way, I had to wait like eight hours at the airport, uh, because I missed all the connections cause they got booked in the meantime. And so, that's an example where if you're on a seat-based or interaction-based, the software provider probably got more more money for my interaction with that AI agent, uh, even though I was more dissatisfied. And that's not what we wanna do as a company. We really wanna align our customers' outcomes to our outcomes.

**[0:35:19] Michael Krigsman:** Here's an interesting one from Ryan Smith. This is really nuts and bolts. He says, As AI begins to resolve tickets faster and automate more interactions, how should CX leaders rethink success metrics? Do legacy KPIs like customer satisfaction and FCR first call resolution still matter, or do we need a new framework?

**[0:35:46] Tom Eggemeyer:** I'm gonna give you an example. There's a lot of, uh, companies right now that use average handling time. So, how long does an human agent stay on, whether it's a phone, an email, uh, a chat, you name it, a web form. How long does it take for that human agent to resolve the problem? And you want lower average handling time. One of the things that we tell our, uh, customers is once you, uh, implement automation, automation takes, let's call, all the easy and medium hard interactions away, and so the humans are left with the most difficult ones.

**[0:36:19] Tom Eggemeyer:** And so, we tell CX leaders, your average handling time is gonna go way up, okay? And you're gonna be like, Why did this happen? I just implemented AI agents, uh, in automation. And then I gave my human agents co-pilot, which should make them more efficient, but their average handling time is going up. And so, we're starting to develop more frameworks with our customers so they're not surprised. When you explain that, you know, a customer, a CX leader is like, Of course, because they're getting the 10% or 20% of interactions that are really, really difficult that take a long time, okay?

**[0:36:50] Tom Eggemeyer:** And so, we're encouraging more A-B testing, different frameworks. I do think still NPS or CSAT matters. I think there's gonna be some new frameworks and some new, um, metrics, like how much of how can you reduce interactions, not for reducing interactions sake, because you're solving the root cause. And so, I think over time, how quickly you you reduce the interactions that keep coming up is gonna be a metric, uh, in the future. Average handle time is gonna be less of a metric.

**[0:37:21] Tom Eggemeyer:** I still do think first contact resolution is important. If you look at the psychic, uh, the the the psyche of customers, they rather wait longer to interact with an AI agent or human agent, rather wait on hold quote on hold for five minutes and get it solved immediately rather than being moved around two or three times between different humans and AI agents. So, I think first contact resolution, but Ryan, I think your, um, prep uh uh, supposition that we're gonna have an evolution in metrics is absolutely gonna happen with CX leaders.

**[0:37:52] Michael Krigsman:** It's so interesting how AI is driving change in so many different directions. The the tentacles are far-reaching, and the implications are very profound. I mean, just just look at what you just described with your pricing policy. I mean, that's such a dramatic shift.

**[0:38:15] Tom Eggemeyer:** I don't think we know, Michael, all the, uh, implications of AI, and I think, uh, people that are certain on how it's gonna go over the next, you know, fifteen or twenty or twenty-five years, um, are probably wrong, but it is profound. Um, you know, one of the things I do is every two weeks, I do a deep dive on what's changed in AI in the last two weeks and how could it impact our customers, and how can we provide more value to our customers.

**[0:38:40] Tom Eggemeyer:** And I would tell you, you know, if you're in the customer service industry from 2009 to 2019, like I was at another company, um, like, the changes you'd have in years are about the same pace that we now have in weeks. And so, there's just so many profound implications on the future of work. Um, you know, I have a, uh, twenty-two-year-old daughter and a twenty-year-old son, and they ask me with AI, what should I do?

**[0:39:02] Tom Eggemeyer:** And that's like kind of an existential question about what skills and capabilities they should learn because I think the nature of work is gonna just massively shift over the next ten to fifteen, twenty years, for instance.

**[0:39:14] Michael Krigsman:** As CEO of Zendesk, what does this rapid change? It's extraordinary that you have to look every two weeks to see what's changed in this AI world that could have some type of potentially profound impact on the market and on your company, Zendesk.

**[0:39:33] Tom Eggemeyer:** It really changes that you need to make sure you're getting a lot of signals from customers in particular, and, like, when we're doing well and when we're failing, I think is really, really important. From the industry at large is really important, and I think it really what I encourage employees is you've gotta be in a learning employee. You've gotta learn AI. You gotta embed it into your daily workflows. You gotta stay on top of it because that is a skill, and I think it's gonna differentiate employees going and including CEOs in the future.

**[0:40:01] Tom Eggemeyer:** Who really understands AI? Who's embedding it in their daily life? Who's using it strategically and tactically? And so I think always learning. I I give advice to people. You have to always be learning, and you have to be always learning about AI. It is gonna impact your job in some shape, way, or form over the next five years, no matter what job you're doing right now.

**[0:40:22] Michael Krigsman:** This is from Arslan Khan, who is a regular listener. He asks great questions. He says, When will we be able to replace humans with AI? What happens to people? Will manager AIs be able to write performance reports for other AIs and humans?

**[0:40:42] Tom Eggemeyer:** I'm an optimist about this point. I think AI is gonna enhance people's lives, and I think, um, we've seen with every major revolution in the world, it create more jobs, but create different jobs. So, I'm still an optimist here, um, on what AI is gonna do. Um, I think, you know, it's an insightful question about performance reviews. Um, I think right now, where we are, I think AI can give summaries. If you get a lot of three sixty feedback, summaries for performance reviews, insights for performance reviews, it's probably gonna happen in fifteen, ten, fifteen, maybe even sooner, that you are gonna have performance reviews written by AI and edited by a human.

**[0:41:20] Tom Eggemeyer:** I think it happens right now. I've talked to some startups that are doing that right now. And so, I think, again, we just gotta really understand at the end the other end of that, there is a human getting a performance review. Um, and you gotta be really, really confident in the technology that it's getting all the data sources, all the signals, you know, honest, uh, uh, in a good way and transparent on, again, that white box theory, Michael, on what it got and how it came to those reasoned conclusions about your performance.

**[0:41:48] Tom Eggemeyer:** I we're already there with quality assurance, where we are evaluating 100% of interactions, whether it's a human agent and AI agent and scoring them. It's not a formal performance review, but it's like indicators of success. So, we're already there. Um, I think there's some really profound, um, implications for human society over the next, again, five, ten, fifteen, twenty years about how jobs are gonna evolve. You're a longtime listener, I think, alluded to it, where he said, um, you're gonna have different jobs, and we think there's gonna be managers human managers managing teams of AI agents going forward, uh, and that's gonna be probably one of the new jobs that's created from this AI revolution.

**[0:42:29] Michael Krigsman:** Arslan Khan has been waiting a long time, so I'm gonna pop another question from him. And he says on Twitter, Can you talk about the role of data in your product and AI?

**[0:42:43] Tom Eggemeyer:** One of the things that we have an advantage as a company is we do 4.6 billion resolutions a year. Uh, and so, there's a huge, huge data set, uh, that can help post-train our models, so they're not just large language models off the shelf. You post-train those models to get more accurate. Okay? It's based on that 4.6 billion interaction data set. It's based upon to the earlier questions, deeper vertical, uh, information. And we're able now with knowledge bases and pulling things from Confluence or SharePoint or others, we're able to get even more knowledge and more data.

**[0:43:20] Tom Eggemeyer:** But, again, um, your models and your answers are only as good as your data. So, I think, again, he's hitting on something really, really strong that you wanna get as much data ingested, but you want, even if it's unstructured, clean scrubbed data that has some effect uh, effectiveness, uh, in it. And so, I think we're getting to a world where the more structured and unstructured data that you have that's scrubbed, the more precise with post-training you can do to give answers, um, to c- uh, customers or employees or other businesses more accurately.

**[0:43:56] Tom Eggemeyer:** And so, I do think we're in a situation where data is really, really important. We've gone through a whole thing internally at Zendesk the last two and a half years. That wasn't that sexy, but we've spent a lot of time, uh, with our internal data scrubbing it, putting it into the data lake so that we could go use it in a bunch of different AI, uh, applications. And until we did that, we kinda had a bottleneck on using our data.

**[0:44:18] Tom Eggemeyer:** I'm not talking about our customer data. I'm talking about internal data to go run the business.

**[0:44:22] Michael Krigsman:** I think smart companies have taken this on as a project because it's not fun, it's not sexy, but it's going to have a dramatic impact on how a business operates in the coming years. It's the foundation.

**[0:44:40] Tom Eggemeyer:** It's the foundation, and there's some really cool use cases right now. I've been talking to a bunch of startups on reporting analytics, and we just, uh, acquired a company that does some really cool stuff in reporting and analytics that you're getting to the point where instead of getting a, you know, report, uh, you can go natural query. What what is the, you know, year over year, um, increase in automated resolutions in Malta for our customer base?

**[0:45:06] Tom Eggemeyer:** And, you know, and you can go get that data with natural language queries that can give you really, really insights. And you can go prompt, um, reporting analytics to give you more forward thinking things. What should I be thinking about to run my business that I'm concerned about Malta, and I'm concerned about how our customers are getting value from our product? And you can get prompts and ideas back. And I think that's, again, shifting from old static reports to real insights, and, again, you can do that through natural language.

**[0:45:33] Michael Krigsman:** Brandon Byrd on LinkedIn says, When you increase self-service levels, how do you address agent burnout? And I'm gonna ask you to start answering these, Tom, relatively quickly because I wanna get to everybody, and we have we have a lot of questions, and I have my own questions.

**[0:45:49] Tom Eggemeyer:** You can self-service you're answering the easy questions, and human agent burnout is real. It's one of the most stressful jobs possible. I think you can help them get better at their job through Copilot, um, and so they you're serving them the answers, which I think can go really help with burnout because they're getting more accurate, more empathy, and, uh, they're getting it right with customers.

**[0:46:11] Michael Krigsman:** Here is a question from Preeti Narayan, who says in her region, there is a lack of computer literacy. And what can a company like Zendesk do to make those computer interactions, uh, easier for folks who are not computer experts?

**[0:46:33] Tom Eggemeyer:** We have a foundation, and we have a program. We we've trained, um, over 10,000, um, people, uh, that might not have the same kind of access, um, there are there is in other parts of the world to become customer service agents, human agents, and giving them the computer skills and the customer skills. And so, that's one of the things we're doing at Zendesk, you know, uh, doing good, doing right is doing good, and, um, um, I think, you know, more and more companies are gonna have to figure out how to, uh, help people become more computer literate, more customer service literate, and become great, uh, customer service agents.

**[0:47:07] Michael Krigsman:** Tom, how has this AI trajectory affected how you think, how Zendesk thinks about talent, team composition, and, uh, jobs?

**[0:47:21] Tom Eggemeyer:** We haven't gone as radical as other people, I know. They've said, Hey, you need to justify any job requisition with why AI cannot do it. But when we talk to talent now, I know one of the first questions I ask is how are you using even for CX, C-suite employees, how are you using AI in your everyday life, you know, professionally and personally? Because in this AI era, if you're not using the tools, if you're not experimenting, you're not getting better.

**[0:47:46] Tom Eggemeyer:** I don't think you're gonna have the skills and capabilities to succeed. So, that's one of the s things that we've talked about, and then we're gonna start a big push. We've kinda had a bottoms-up, um, approach up until now about how our employees use AI, and we've opened up like an enterprise license for ChatGPT. I use it every day. I think you're gonna see a little more top-down pushes, um, to encouraging employees to use, um, you know, these tools more, because I think it's good for the company, it's good for their career.

**[0:48:13] Michael Krigsman:** Can you talk about the, uh, future and where is AI customer experience headed?

**[0:48:21] Tom Eggemeyer:** We believe at Zendesk that 80% of interactions will be through automated resolutions in five years, so do not involve a human in any way, shape, or form. We think 100% of interactions will involve AI in some way, shape, or form within five years. That means that last 20% that humans are solving, they're gonna do that with the assistance of AI. We do think there's another trend, though, that once you start getting s quick satisfaction, it's the opposite of dealing with that chatbot, Michael, you and I talked about earlier.

**[0:48:53] Tom Eggemeyer:** We think consumers, employees, and other businesses are gonna interact with those companies more and more and have more interactions. So, we think interactions are actually gonna explode, particularly when you start using your own AI agent and task them with doing something. Uh, and so those are some of the trends that we're gonna see in AI over the next five years.

**[0:49:12] Michael Krigsman:** What about the impact of AI on jobs in general across the economy? Is that something that you have thought about at all? Where where is this going from a from a jobs? What jobs will be affected, what what won't? And so forth.

**[0:49:31] Tom Eggemeyer:** Definitely thought about it, and I'll give more of a personal story, and I alluded to it before. I have a twenty-two-year-old daughter who just recently graduated from college, and I have a twenty-year-old son who's in college. And they've asked me with the advent of AI, what kinda what should what job should I be doing? I think an answer would've been fifteen years ago, go into coding, right? Software engineering is a great place to go. I'm really telling them that, again, number one, be really, really knowledgeable about AI, use it in your personal life, use it in your professional.

**[0:49:59] Tom Eggemeyer:** That is gonna distinguish you from other people. Number two, I tell them, it's gonna go back to the basics. If you can read well, you can write well, you can analyze well, you're gonna be fine, cause no one can predict what the job market's gonna be evolve over the next forty to fifty years. And that's kind of their career trajectory of, you know, young twenty-something peoples, what's gonna happen the next forty or fifty years, and we've seen what's happened in the last, you know, three years with AI.

**[0:50:25] Tom Eggemeyer:** So, really encourage people to get those basic skills. And third, I encourage them to learn how to prompt, and it's kinda tied to one and two, how to use AI. And I think I've experimented the last two or three years, and my prompting's getting better and better. I think that skill is gonna be great, and it's kinda interesting. I think some of the liberal arts education, when you write well, you analyze well, you read well, is gonna be more and more important in prompting.

**[0:50:50] Michael Krigsman:** On the prompting topic, folks listening, we did a show specifically on prompting for business leaders. So, check out cxo talk dot com and go back a few episodes, and you'll find that show. It was really, really great. Uh, very, very quickly, data privacy and security, cybersecurity of your data. Uh, can you talk about that? Because that's obviously it's an extremely important topic.

**[0:51:16] Tom Eggemeyer:** We take data privacy and it's kind of that white box approach really, really seriously. And so, um, we wanna get we are out in the forefront right now on trust. Uh, the amount of certifications that we have to make sure that we have a lot of confidence that we are gonna protect with our our customers, your data is really paramount to us. Um, second, we wanna make sure that, um, we can personalize things to you, but not in a creepy way with companies, and so we work with companies on that.

**[0:51:46] Tom Eggemeyer:** But I think we're gonna be in an era where that white box approach versus the black box approach, and exposing that not just to companies, but employees, consumers, and other businesses on how people are using your personally identifiable information, how they're securing that, is gonna be more and more of a topic over the next five to ten years. And but it's absolutely the forefront of our AI approach.

**[0:52:09] Michael Krigsman:** And with that, I'm afraid we're out of time. Tom, thank you so much for being a guest on CXO Talk. I'm so grateful to you and for your team at Zendesk.

**[0:52:20] Tom Eggemeyer:** Great questions, great flow. Really, really appreciate your time, Michael, and I really appreciate the opportunity to be on with you today.

**[0:52:26] Michael Krigsman:** Folks, before you go, now would be an excellent time to subscribe to the CXO Talk newsletter. Go to cxo talk dot com. We'll keep you updated on upcoming shows. Have a great day, everybody, and we'll see you soon. Take care.