Hardy Kuhn, head of service management solutions at SAP, sits down with Savannah Peterson of theCUBE Research at Knowledge 2025 to talk about how AI is reshaping enterprise service management. From SAP’s innovations to industry-wide momentum, their conversation is packed with insight and energy.
Kuhn shares updates on SAP’s work with Agent Zero, a cutting-edge agentic AI solution built to simplify and enhance customer service workflows. As Kuhn explains, SAP is building intelligent systems that empower support teams by eliminating repetitive tasks and enabling smarter decision-making.
The pair dive into how agentic AI can augment human workers, not replace them, unlocking new levels of efficiency and precision in enterprise operations. SAP’s success comes from combining deep AI expertise with strong ecosystem partnerships, accelerating real-world adoption, according to Kuhn.
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Hardy Kuhn, SAP
Hardy Kuhn, head of service management solutions at SAP, sits down with Savannah Peterson of theCUBE Research at Knowledge 2025 to talk about how AI is reshaping enterprise service management. From SAP’s innovations to industry-wide momentum, their conversation is packed with insight and energy.
Kuhn shares updates on SAP’s work with Agent Zero, a cutting-edge agentic AI solution built to simplify and enhance customer service workflows. As Kuhn explains, SAP is building intelligent systems that empower support teams by eliminating repetitive tasks and enabling smarter decision-making.
The pair dive into how agentic AI can augment human workers, not replace them, unlocking new levels of efficiency and precision in enterprise operations. SAP’s success comes from combining deep AI expertise with strong ecosystem partnerships, accelerating real-world adoption, according to Kuhn.
Hardy Kuhn, head of service management solutions at SAP, sits down with Savannah Peterson of theCUBE Research at Knowledge 2025 to talk about how AI is reshaping enterprise service management. From SAP’s innovations to industry-wide momentum, their conversation is packed with insight and energy.
Kuhn shares updates on SAP’s work with Agent Zero, a cutting-edge agentic AI solution built to simplify and enhance customer service workflows. As Kuhn explains, SAP is building intelligent systems that empower support teams by eliminating repetitive tasks and ...Read more
>> Hello, CUBE community, and a welcome back to the Accenture Reinvention Lounge here in wonderful Las Vegas, Nevada. We're here at Knowledge 2025. My name's Savannah Peterson. Delighted to be joined today by now CUBE alum and VIP Hardy Kuhn from SAP. Thank you so much for coming to hang out, Hardy.
Hardy Kuhn
>> Thanks for having me again.
Savannah Peterson
>> I'm so glad we get to do this in person this time around, you were on Zoom with me in the studio before. We chatted a few months ago. A few things have changed, so give us the update on what you've been doing, the progress you're making, and all the excitement.
Hardy Kuhn
>> I would say in the good old times, I wouldn't have anything to update you with, but in times of AI, it has accelerated so much. Excited to say that we've climbed the next ladder, so to speak, and we've experimented and quite successfully experimented with agentic AI. Last time, I was talking about calling large language models, generating text, and doing single tasks, and now we're almost live, not yet, but almost live with what we call Agent Zero, which is an automatic answer to a customer incoming ticket.
Savannah Peterson
>> What does that mean exactly? What had to happen in this short period of time for Agent Zero to be able to be deployed?
Hardy Kuhn
>> I think there's a lot of glue, I would say to AI that has come on top of it. First underneath, obvious, it's still large language models, and they have evolved, so they're more advanced in their answer and so forth. But now you have a technology where you can encapsulate a single prompt and then pass it on to the next agent where it picks it up and then does the next job, et cetera. Let me tell you what Agent Zero is so that our listeners will understand. It's an incoming case from a customer that has a problem. Usually, an agent would pick it up from a queue and then do whatever they do. This time around, the first agent would summarize the case. That's the first one. The second, third, and fourth would look into different knowledge bases. Do a search, has it occurred before? Do I find a good result? And we do that in multiple databases in the back end. And then there's an evaluator that says, "Did it really help one of these answers?" And then the next agent that comes along is retrieving the information and writing an answer to provide to the customer that has the problem in the first place. So it's really incoming case knowledge base summarization, and then an answer written.
Savannah Peterson
>> It's all happening in tandem.
Hardy Kuhn
>> Absolutely.
Savannah Peterson
>> Very quickly.
Hardy Kuhn
>> These agents can run in parallel, but then they are summarized and I think that's next level. And the way this has evolved over the last three months is just insane.
Savannah Peterson
>> It is insane. I'm so excited. Congratulations on the progress. You've done a lot. You're moving, like you said, way faster than in historic technological-
Hardy Kuhn
>> Absolutely....
Savannah Peterson
>> revolutions. What is it that allows you internally to be able to move at this velocity with this technology?
Hardy Kuhn
>> I mean, first of all, the good team that we have in place, it's the people most of all, right? We've got our year and a half in with AI experiences, large language model experience, prompting experience. That wall is all foundation to now climb the next step so I think that's the first thing. The second thing is the technology has jumped again. Half a year ago, agentic AI was not a real thing and now it's there, so I think that part is absolutely amazing. Technology's way can really code an agent then in the end ask, reason why this is a good answer. I think that is what surprised me the most, that these models now not just carry out a job like an automation job or workflow or anything like that. They reason why they've done it, and why do you think it's a good answer?
Savannah Peterson
>> And why they think that's the most accurate or up-to-date answer.
Hardy Kuhn
>> Correct.
Savannah Peterson
>> For example, within a legacy system or knowledge grab. I mean, that makes a really big difference. You mentioned that that was the most surprising thing is that level of reasoning. What are some of the other surprises or delightful things you've learned in the last 90 days?
Hardy Kuhn
>> Funny enough, agents can run around like chickens turning in circles. We had some experiences where they never came back and never found a result and then so forth, but I think that's the part of the experimental phase and the development phase of it. Other than that, it's not like hard coding development because none of these agents are deterministic in what you prescribe them to do. It's really a prompt and then a large language model underneath finding an answer. You don't know exactly the outcome. You know what an agent is supposed to do, but the real outcome is undetermined. I think that's the part that you have to get accustomed to. And then I think what we learned as well is if you just let all the agent huddle or crew or whatever you want to call it, do their thing and just provide an end result, users don't trust it. So what we did is we tracked and traced and recorded every agent what they did, what they got from the previous agent, and why they think what they did is correct or good or not good. I think you can look that up in the law, and that's something that helps us then to do the change management with people that actually use it.
Savannah Peterson
>> Yeah, it is essentially asking the agents to explain their work just like we had to do or show our work in math class.
Hardy Kuhn
>> Absolutely.
Savannah Peterson
>> Whatever that might've been. So what is the outcome then that your customers and community get as a result of this advancement?
Hardy Kuhn
>> First of all, it's just something that we will equip our agents, our people with as an accelerator. Agents have done a job before human looks at it, and I think that takes away from tedious work like searches that no one likes to do in the first place.
Savannah Peterson
>> Right.
Hardy Kuhn
>> It takes away work like reading through a long knowledge article for the section on page 27 on the bottom left to provide the answer. It's all tedious work that we take away, and I think that accelerates work of a human agent, equips them with jobs that you don't like to do in the first place. Hopefully this is the first real value case. We had little bits and pieces, but I think this is now a really big thing and now we have a lot more of those.
Savannah Peterson
>> Well, we look forward to continuing to tell those stories. What does that mean for you as you're innovating at this velocity? How important are the partnerships like the one you have with the ServiceNow and working with companies like Accenture?
Hardy Kuhn
>> It's absolutely essential. If you don't have the technology or the people that are curious enough and know what to do, I think you won't get there. So I think that educational part, the curiosity part, the inspiration of having the goal and then trying to figure it out, it's just like real development but much faster.
Savannah Peterson
>> I think that's a really good point you just brought up, and tell me if you agree with this or not, but historically we all know IQ and AQ. I almost think there's a CQ, a curiosity quotient right now.
Hardy Kuhn
>> Absolutely. I think that's one thing that I learned over the last year and a half. If you're not curious, you're probably not one of the people in this business of AI at the moment, because there's so much that we don't know yet.
Savannah Peterson
>> Right.
Hardy Kuhn
>> That we figure out along the way, that we plan ahead and then it turns out differently. So I think that portion of curiosity is a huge portion of it.
Savannah Peterson
>> Yeah. Do you get excited to learn every day before you go to work?
Hardy Kuhn
>> Absolutely, I do. I even embarked on a study in parallel to really get more depth in some of these topics. So shows you the-
Savannah Peterson
>> Wait, tell us about that, what study are you doing?
Hardy Kuhn
>> Well, I concluded it. It's an MIT program that ran for four months that was about AI, so actually quite fun.
Savannah Peterson
>> Yeah, that is quite fun. I'd be curious, actually, I'm going to ask you this as a follow-up question to that. So who is on the front lines more, the enterprise or academia when it comes to the new concepts rolling out?
Hardy Kuhn
>> I think academia was in this field for a long time, right? AI goes back ages, so papers. I think the technology advancement, the compute power, all the ingredients that led to the first large language model and the evolution of it, I think that has been founded or sits on the shoulders of academia so I would say academia starts-
Savannah Peterson
>> Some heavy mathematicians, yes, and ....
Hardy Kuhn
>> and then it took a long time to catch up to really do it.
Savannah Peterson
>> Yeah, so we're finally here. We've arrived at a moment when the intersection of technology and academia is in a really sweet spot. Last question for you, since you're now becoming a regular guest here on theCUBE and we love that, what do you hope to be able to say the next time you and I sit down that you can't yet say today, let's call it six to 12 months from now?
Hardy Kuhn
>> I think last time we talked a lot about security, data privacy when building this out. I would say a year from now, if I could say we have really helped people eliminate jobs that you don't want to do and let them focus on what their strengths is, what their curiosity is about, what their talents is about, and we've done it in a responsible way, I would say I would be really happy.
Savannah Peterson
>> Oh, I love hearing that. How do we do it responsibly?
Hardy Kuhn
>> That's a good question. But I think there's a lot of talk about ethical AI so you have to set ground rules, what you should or shouldn't do. You have to give agents a certain behavioral pattern, be friendly, be professional, all that kind of stuff. Don't say this, don't say that. I think that's part of the learning curve. I think that's a huge part of it.
Savannah Peterson
>> Yeah, I think that is a huge part of it. All right, well, this is great. Stay curious, stay mindful, and stay working with fun people because you'll build faster, cooler things. Hardy, thank you so much for spending the time with us and you finally making it in person to the show, we appreciate it.
Hardy Kuhn
>> Total pleasure, thanks for that.
Savannah Peterson
>> It's a total pleasure indeed. We hope you're having as much fun as we're having here at the Accenture Reinvention Lounge here in Las Vegas, Nevada. My name is Savannah Peterson, you're watching theCUBE, the leading source for enterprise tech news.
Savannah Peterson