John Furrier, host of theCUBE, interviews Alex, co-founder of Celonis, at the New York Stock Exchange. Celonis focuses on process improvement and visibility, working with an ecosystem to drive value for customers. They aim to bring AI into the enterprise with agency, enabling agentic automation. The company's Process Intelligence Graph allows customers to gain insights into their processes and optimize them. By providing a common language, Celonis helps automate processes and enable AI agents, leading to productivity gains. Partnership and ecosystem play a crucial role, as Celonis integrates with companies like IBM, Microsoft, and AWS to build AI agents seamlessly. The goal is to create a digital twin of an organization for simulation and scenario analysis, ultimately setting the foundation for future advancements in enterprise AI.
Many companies have established process owners for the first time, enabling them to drive impact and unleash human potential. Process owners can access data, knowledge, and a common language across systems, shifting from repetitive tasks to strategic work. In the realm of gen AI, companies are focusing on data and using AI to unlock, analyze, and optimize their data to drive success.
Several companies are using Celonis to gain insights from process intelligence and improve their operations. They are leveraging data integration, task mining, and network analysis to enhance their processes. Partnering with companies like IBM and Microsoft, Celonis offers a platform for bringing in process data and implementing packaged solutions quickly.
In terms of governance and resilience, companies are focusing on orchestrating data interactions, ensuring proper governance, and enabling recovery mechanisms. They are integrating human oversight with AI recommendations to enhance decision-making processes. Celonis is also helping companies redefine master data management by prioritizing data based on transactional insights and offering intelligent recommendations.
With a strong focus on partnership, integration, and value creation, Celonis is enabling companies of all sizes to optimize their processes for efficiency, cost savings, and enhanced customer experiences. From large corporations like BMW to public sector entities like the State of Oklahoma, Celonis is driving transformative changes in various industries. The company, now boasting nearly 3000 employees, remains committed to delivering ROI and value to its customers through data-driven process improvements.
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Alex Rinke, Celonis
John Furrier, host of theCUBE, interviews Alex, co-founder of Celonis, at the New York Stock Exchange. Celonis focuses on process improvement and visibility, working with an ecosystem to drive value for customers. They aim to bring AI into the enterprise with agency, enabling agentic automation. The company's Process Intelligence Graph allows customers to gain insights into their processes and optimize them. By providing a common language, Celonis helps automate processes and enable AI agents, leading to productivity gains. Partnership and ecosystem play a crucial role, as Celonis integrates with companies like IBM, Microsoft, and AWS to build AI agents seamlessly. The goal is to create a digital twin of an organization for simulation and scenario analysis, ultimately setting the foundation for future advancements in enterprise AI.
Many companies have established process owners for the first time, enabling them to drive impact and unleash human potential. Process owners can access data, knowledge, and a common language across systems, shifting from repetitive tasks to strategic work. In the realm of gen AI, companies are focusing on data and using AI to unlock, analyze, and optimize their data to drive success.
Several companies are using Celonis to gain insights from process intelligence and improve their operations. They are leveraging data integration, task mining, and network analysis to enhance their processes. Partnering with companies like IBM and Microsoft, Celonis offers a platform for bringing in process data and implementing packaged solutions quickly.
In terms of governance and resilience, companies are focusing on orchestrating data interactions, ensuring proper governance, and enabling recovery mechanisms. They are integrating human oversight with AI recommendations to enhance decision-making processes. Celonis is also helping companies redefine master data management by prioritizing data based on transactional insights and offering intelligent recommendations.
With a strong focus on partnership, integration, and value creation, Celonis is enabling companies of all sizes to optimize their processes for efficiency, cost savings, and enhanced customer experiences. From large corporations like BMW to public sector entities like the State of Oklahoma, Celonis is driving transformative changes in various industries. The company, now boasting nearly 3000 employees, remains committed to delivering ROI and value to its customers through data-driven process improvements.
John Furrier, host of theCUBE, interviews Alex, co-founder of Celonis, at the New York Stock Exchange. Celonis focuses on process improvement and visibility, working with an ecosystem to drive value for customers. They aim to bring AI into the enterprise with agency, enabling agentic automation. The company's Process Intelligence Graph allows customers to gain insights into their processes and optimize them. By providing a common language, Celonis helps automate processes and enable AI agents, leading to productivity gains. Partnership and ecosystem play a cr...Read more
exploreKeep Exploring
What is the secret to success for Celonis right now?add
What is agency and how does it enable enterprise AI for customers?add
What is the key to achieving 5X productivity in order management processes, as demonstrated by customer Cosentino, with the help of Celonis plus agent and the Process Intelligence Graph?add
What are the strategies for deriving more value from data in process mining and improving processes through data enrichment and collaboration with other companies?add
What are some key considerations for properly governing interactions between LLMs and data within a system?add
What is the importance of monitoring and observability when bringing agents into a process, and how are some companies incorporating human approval into automated processes for increased productivity?add
What is the new approach to bringing together process execution and master data in a single view?add
What are some examples of the cross-platform integrations and partnerships that we have in place for our data management solutions?add
What is the cultural theme and focus of the company as mentioned by the co-founder during the interview?add
>> Hello, everyone. Welcome to theCUBE here at the New York City location at the NYSE. I'm John Furrier, your host of theCUBE. Dave Vellante was just with me last week. We were at the IBM Analyst kickoff forum. That's where they kicked off all of their strategy innovations. Today, we're here at the New York Stock Exchange. Again, this is theCUBE coverage from NYSE. We're here with Alex who is the co-CEO or co-founder of Celonis, a really hot company doing amazing things. Alex, great to have you on theCUBE. Appreciate you.
Alex Rinke
>> John, thanks for having me.>> We're here at the New York Stock Exchange. You guys had a great event. theCUBE was there live-streaming for two days. First of all, you guys are doing extremely well. Congratulations.
Alex Rinke
>> Thank you.>> Had a great event. Before we get into some of the things you guys talked about at the event, because you had great customers there, what's the secret to success for Celonis right now? You guys are doing extremely well. It's like you skated through where the puck is, as they say. You caught the wave, you're on it, you're paddling, great. You've got great customers. You're really in the wheelhouse of what everyone's talking about, which is the business value, unlocking the data and also bringing in end-to-end automations, a big part of this phase one of the gen AI. What's the secret formula? What's the secret to the success?
Alex Rinke
>> John, I think obviously there's lots that it goes into it, but ultimately, we are the value company, and we enable our customers to get full visibility into their processes and then drive tons of improvement. And we're not doing it alone. We're doing it with an ecosystem obviously, of many players out there, but we enable that very fast for customers. And that's exciting about it. We always say that processes are your greatest level for value and your fastest level for change, and that's why this space is so important and that's why it's growing.>> And what's interesting too is you start to see AI kind of get visibility into the enterprise, which is where there's a lot of value. It's still a small percentage relative to all the AI action. Most of it's on the consumer side. When people talk about the foundation models, computer vision, or language, it's all kind of consumer, but those are the big, large language models. And it's now well-known that you don't need to have the large language model only and that there's a power law developing in the models where you've got specialty models as well as proprietary data in there, so you've got the private AI conversation. So you've got all this going on, and then now you've got the hype of agentic systems, which is basically taking multi-step processes and making that work, not just as an automation layer, but as true, trusted delegation.
Alex Rinke
>> 100%.>> This has to get done. And by the way, it's throwing off more data too.
Alex Rinke
>> Yeah. 100%.>> Talk about your reaction to that.
Alex Rinke
>> So a lot of our customers ask, how do we get from AI to enterprise AI, right? How do we get from it can do my children's homework, it can pass the bar exam, it can do all this amazing stuff, but how do we use it to automate our order management? How do we use it to provide better customer service? So we introduced agency. Agency, right? And agency is a suite of partnerships, of integrations, and of enabling tools to really enable agentic automation. It's the difference between doing your homework or passing the bar exam, and automating processes and being able to be that delegate is really understanding how each business flows, understanding the unique processes of a company, getting access to the unique process data across systems, and that's what Celonis provides. So we're very excited about using AI, and we're embedding it all across our stack, but we are even more excited about agency, because that enables AI agents, and that ultimately enables what we call enterprise AI for our customers. And it's driving a lot of value already.>> First of all, I love the name, because you know the old expression, "Have high agency-"
Alex Rinke
>> Exactly.... >> which means you can handle yourself, but also agents.
Alex Rinke
>> Exactly.>> All right. So a little bit of a play on words there. And this is where it's all going. And agents are coming down the road, we see it clearly, but it's not yet there. We're starting to see the setup there. Let's zoom them out for a bit. Let's talk about the company. Just set the table, what are you guys doing right now for business? Give us the inside the numbers and the basic overview of Celonis, and then we can talk about what the keynote was about, what was the overall theme of the event?
Alex Rinke
>> Yes. Yes. So we started Celonis in 2011. We started right out of college because we fell in love with process mining, okay? And process mining is really this x-ray for your business processes. Any business process, no matter which system, no matter across how many systems, you can get x-ray vision and to get visibility and improve it. And we fell in love with that idea. It was a research idea at that point, but it had no adoption within businesses. So we decided Celonis was that we want to bring this from the academia to the boardroom and build a company, and it's been an amazing ride. We are almost 3000 people around the world. We have amazing customers. You met some of them at the event here, and we work with about half of the Fortune 200, and many, many smaller customers around the world, and we help them drive more value out of their business processes. So it's an exciting journey, we've come a long way, but there's a lot ahead.>> Yeah. If you're watching, theCUBE.net has all the videos, check them out. A lot of great highlights. I mentioned between 2011 and now, if we look at that time period, a lot of things have happened. So big data. Hadoop was the big deal.
Alex Rinke
>> Yes. That's right.>> And then Spark came on. Now, you got the data lakes. But also, RPA was hot, okay? And then we got into the generative category. Jensen Huang from NVIDIA, their stock at an all-time high yesterday. I know no one's trading at these days. But you're seeing that it's generative, so it's a new category. It's generating, it's not a pre-programmed thing. Talk about the difference as you guys came into that changeover, because there's differences between RPA and now what generative AI offers. Could you share your thoughts on that?
Alex Rinke
>> So first of all, we love all the emerging cloud data infrastructure. We use a lot of that. We partner with those companies, because the faster we can get to the data, the more scalable it is, the more of our magic we can do, so that's great. And on the automation side, I think what you're talking about is this automation layer, and RPA, I still remember this, had a huge promise, a bot for every person. We're going to automate 30% of our processes, things like that, and it didn't really happen. And their word fell short, and it was too brittle, and too much scripted. It's almost like macro. "You have to tell it exactly what to do. If something changes, you have to do it over."
With agents now, they can actually reason and they can actually adapt, but what they need is they need the critical inputs, and that is a mix of data and context, right? John, over the last 50 years in our enterprises, we've created quite a mess. We have ERP systems, we have CRM, we have HR, we have cloud systems, we have this web of systems. It's very, very messy. So we need a semantic layer, which is really a fancy way of saying all these systems and departments speak a different language. They all speak their own unique language. We need a common language to enable language models, to enable AI. And that's what Celonis provides. Last year, we introduced our Process Intelligence Graph, which is a graph-based process intelligence foundation that can go across all of your processes, look at the correlations, look at how order management touches inventory and accounting. And then we've added context with process models, business rules, and that really enables agents in a new way, when you add in our agency. So we're extremely excited to enable enterprise AI across domains, applications, and processes.>> The process mining background, when you think about AI, allows you to go do the heavy lifting on the setup, so you-
Alex Rinke
>> Absolutely.... >> go mine all the process. They speak this language, that language. And again, harmonization has been a big topic, semantic layer, control plane, whatever you want to call it, shared data layer. There's a lot of different names for it. But what's interesting is that it's like the musicians. You play the guitar, you play the drums-
Alex Rinke
>> Exactly.... >> and at the end of the day, you just want to play good music, right?
Alex Rinke
>> Exactly.>> And so this is the problem with the enterprise. You've got these fragmented individual systems-
Alex Rinke
>> 100%.... >> that aren't working together.
Alex Rinke
>> .>> So now, okay, we love abstraction layers. That's where innovation happens. So take me through the mindset of, okay, you got all this stuff, you understand process, almost like the old days, you do some discovery. How did you guys get to that point, and what are customers doing right now when they say, "I know my estate's fragmented. I know it's a disaster for a reason. It was built that way. Whether you call it sprawl or intentional, I just want to make it work." So now, the opportunity, because I have data, if you have data right now, that's who's winning right now in gen AI.
Alex Rinke
>> 100%.>> Whoever has data-
Alex Rinke
>> 100%.... >> can instantly be agile and reset. Take us through that.
Alex Rinke
>> Yeah. That's a great point. And we really started with traditional process money, okay? So you take an order, you track it through the systems, you take an invoice, and that's always a case-by-case thing, right? Because you have orders , you have customers, and you have to create all these different models. What we then noticed is, well, all these processes are interdependent, right? How you process your orders is going to inform how your invoices and your billing works. That even goes all the way back to your master data, to your . So we've almost five years ago embarked on a journey to build the Process Intelligence Graph to bring all of this in one data model so we can look at each process, but we can also look at the combination. That in itself is a huge innovation. It drives innovation in supply chain, in financial back office processes, in KYC, in getting a single view of your customers' processes, so that's been a big step for us. It was introduced last year, we've never seen a new product being adopted so fast. Okay? So that's one thing. Then we've added the context, right? You need to know what is a good process, what is a bad process? How do we define on-time delivery? If you go on ChatGPT, you can get 50 definitions for on-time delivery, but how does your company define it? How do you want to pay? Is working capital or P&L more important when you pay invoices? All this stuff, we can add with our modeling layer, or process models and business rules and responsibilities. You put that together, and then you expose that to AI, and magic happens, because suddenly, agents can come, like in our customer Cosentino, which has been able to get 5X productivity in their order management process. It's enabled by Celonis plus agent, and you can build that agent in Microsoft, you can build it wherever you like, and we can talk about the partnerships that we have there, but really, this Process Intelligence Graph is really the key.>> It's interesting. You're hitting on something that I've been saying since 2015, since we started covering AWS deeply. Well, 2013 is when we started covering Amazon. But when people started to wake up to what they were really doing, it wasn't just for startups, it was just an easy way to get provision hardware. When they started winning the enterprise, I think the big goodness a-ha moment was this is horizontally scalable. And so data doesn't scale horizontally naturally.
Alex Rinke
>> No.>> It's usually vertically specialized around domain-specific information, so having a knowledge intelligence graph that could actually work with a new way to govern the data-
Alex Rinke
>> Absolutely.... >> would be a nice way to make it available. I remember back in the storage days, we used words like highly available and high availability. They mean different things.
Alex Rinke
>> 100%.>> So low latency is the key. Low latency and power are the key to the generative AI movement. Latency, in this case, is data latency.
Alex Rinke
>> 100%.>> Talk about why that combination of horizontal scale and specialism in the data has to be in concert and distributed? And the importance of it, and how hard is it?
Alex Rinke
>> Yeah. It's hard to do, because you have to get data from different systems. But that's really where this core technology comes into play, which is, it used to be process mining. Now it's object-centered process mining, because it allows you to bring together objects and events from different systems and look at them as one graph and one flow, and really understand where are my bottlenecks, where are things getting stuck, where do we have too much effort? And then again, you add the context to it, your business context, and you get a new data asset that you didn't have before, right? It was stuck in all these systems. The context wasn't even captured.>> This is where the x-ray comes in? You can see things.
Alex Rinke
>> That's where the x-ray comes in, and it's an x-ray that's contextualized, right? This is this bone, this is good, this is not so good, right?>> It hurts. You've got a broken leg.
Alex Rinke
>> Exactly. You've got a broken leg here. And it's not just a one-off. It's always on. You talk about latency. We just introduced huge improvements in all of our integrations, our latency, our scalability. There's one customer that we talk about in the new keynote, Daxa, they're bringing data in just one process from 80 systems, and it's one logistics process. It's 80 different systems involved, right? They have a franchise . So you bring this together, you build a real Process Intelligence Graph, and you can enable AI and automation. All forms of automation, really, in a new way.>> I want to talk about partnership. I'll come back to some of the domain-specific value positions and how general AI, call it the basic foundation models, because you can't just throw that at the enterprise. We're going to come back to that. But you have a lot of partners. You mentioned customers. Customers are now in the ecosystem.
Alex Rinke
>> Oh. 100%.>> So the world went from Cloud 1.0 ecosystems were super important, but they were just standalone SaaS vendors. Now, we have a notion of connected, I call it, connected ecosystem, because now, customers and partners and cloud are all connected together, and then you got the on-prem, which is basically cloud operations, so it's distributed cloud. I just call it all cloud, because it's all cloud. So the importance of data exchanging between APIs, now gen AI layer is going to be big, and the word intentional has been kicked around in a lot of the CUBE interviews we've done. People say, "We have an intentional partnership with this company."
I noticed that, and I talked about this on my podcast recently, where if you use the baseball metaphor, is that they're playing small ball. They're getting smaller ecosystems, but they're better partners. IBM used to have tons of logos. Now, it's a handful. You're on there. And so I'm seeing a trend where the partner now has to integrate platform integration into their app. If watsonx is going to do something, why wouldn't you partner? And they're clearly partnering. So Amazon's doing the same. Everyone's doing it. So talk about the role of partnerships because data trust is now an equation in AI. Where'd it come from? What's that API? Is it end-to-end? What's the security posture? There's a lot of stuff going on. Is that changing the partnership relationships that you guys are working on now? And how do you see that? Everyone wants to have an ecosystem. Some do, some don't have it, but this is very nuanced, but I see this happening.
Alex Rinke
>> Yeah. Oh, yeah. And I think you pick up on this. You're very plugged in. 100%. That's a great question. So we think it takes a village or an ecosystem to make this work, right? And our goal is to, when you have an ecosystem, the first question you've got to ask yourself, what value do you provide to the ecosystem, right? So we provide this common language for the business processes in a company. And just with our agency, we introduced a number of integrations, right? IBM watsonx Orchestrate is one, Microsoft Copilot, AWS, to build AI agents, and a lot of small ISVs. So the ecosystem is a very, very big and important factor for us. We also have partners building apps now where they bundle their domain-specific and industry-specific expertise into IP that customers can use to get started right away. So the ecosystem is multiple layers. It's really, really important on the infrastructure and data side, like we talked about that on the automation side, but all types of automation that we can enrich with our data and our context, and we're really excited about the->> It's just not a marketing partnership?
Alex Rinke
>> Oh, no.>> It's really has to be technically connected?
Alex Rinke
>> For us, partnerships always start with engineering, right? We want to bring real value to customers. So they use those things, and they say, "Hey, I want to build an agent in Copilot Studio, Agent Studio for Microsoft. How's this agent going to learn how my business flows?"
Well, we can provide a common language so you can just plug into it. It's seamless, it's fully integrated, and you can start automating your business.>> You know what I love about what you guys do? You touch a lot of things, and you make sense of it, and you got the Intelligent Knowledge Graph, you've got agency. theCUBEResearch posted a bunch of stories on the Economist on digital twins, and I've always loved digital twins, but it's also been a manufacturing thing because that's the first use case. And their whole goal was to do simulation to make efficiency work, okay? But the premise of our new digital twin view is, and I want to get your commentary on this, is that it's about process. And so the simulation and manufacturing is fit for purpose for manufacturing, but you can do simulations on other processes, so it's not just digital twin is the thing for manufacturing. We do digital twins for our media. I'm sure if I'm a process owner, I'm like, "Hey, I'd love to simulate things before I roll them out." And you're smiling. I can tell this is going to be a sweet spot for you. Yeah.
Alex Rinke
>> So you asked about the founding story, right?>> Yeah, yeah.
Alex Rinke
>> The part I left out is that the initial idea was actually to build a simulation company. We wanted to simulate processes, and we found out, okay, we don't have the data and the input and the knowledge to do it, so we focused on the process mining piece. But it's starting to happen, because what you're really talking about is a digital twin of an organization, right?>> Yeah.
Alex Rinke
>> And when you have a digital of an organization, the next step is you simulate it. You do scenario analysis. You say, "Hey, if we add resources here or we can automate here with AI agents, we're going to get some productivity, but think about how much we can speed this up for our customers. What do we need to take out?"
So we have emerging capabilities in that domain. And I think that, again, it's enabled, it's all enabled by that foundation that we talked about, the Process Intelligence Graph, the common language, and it's really exciting. There's a lot that's going to happen in that space. Yeah.>> I think it's headroom. I think we're not there yet.
Alex Rinke
>> Yeah, exactly. 100%.>> That's where we go.
Alex Rinke
>> 100%.>> It goes down to things like security, threat detection. What if I was hacked? Let's simulate. We can do blue team, red team. We have all that data. Let's just create a digital twin and to run an agent, so there's things like that.
Alex Rinke
>> It can even inform how you set up your front office and commerce operations, for example, right? How you configure your products because that's often restricted by the processes. So we want to help people scale their processes to the level of their potential and ambition.>> It's like we're in a progression. I compare the gen AI revolution to the web where everything was pegged by both technology innovation, which is how fast you can dial up, and broadband, and how the web page is loaded? But ultimately, the key was online population of users, was the key-
Alex Rinke
>> Correct.... >> benchmark. Now, we're seeing that same thing. Gen AI is early, but there's still not the adoption and the technology. Innovation's getting better. Now, the only problem is costs are getting higher, but inference is going to get dropped down. So we're in the early days, so yeah. Digital twins, all those awesome agents, they're coming. But right now, a lot of blocking and tackling's on process, I call them meat-and-potato process where the value is.
Alex Rinke
>> Well, I think that there's two things, right? One is if you optimize your processes, firstly, you're going to get a lot of value. It pays for itself, which is the cool thing.
The other thing that's nice is if you build that common language across 50 years of mass that was created, that the processes have to work its way through with different systems and different applications, if you build that foundational common language and layer, intelligence layer, it's going to save you money, but you are prepared for everything that's coming, right?>> Yeah. It's foundational.
Alex Rinke
>> It's foundational, because the agents are going to need it, the digital twins are going to need it, and really, your next-generation applications, because you want to get to a place where it's composable, where you can just compose processes and applications very, very quickly, and that's what this ultimately enables.>> So Alex, you agree that we're foundational right now, and setting the table, setting the foundation for the future?
Alex Rinke
>> 100%.>> Okay. So let's get into that, because one of the things we've been saying on theCUBE, Dave and I and George and the whole team talk about all the time on theCUBE is you can't just throw AI at the enterprise-
Alex Rinke
>> No.... >> because like you said, it can write a song for you, give you a poem, write your homework for you, write some blog posts, but it can't figure out who's got identity access to the systems. So we have a premise that the new IT value, I quote IT, because IT used to be provisioned to serve the business, IT serves the business, and they would provision networking, put a PC on your desk, then a laptop, virtual desktop. Now, the new value to the business, because technology is the business, so that's one thing that's happened. So the question is what serves the business? Domain expertise seems to be the critical linchpin between how AI works or not. So the first wave of work is process mining identification of those processes and what are the knobs and buttons that are pushed daily? Document the hell out of those, and then get it up and running. For instance, Red Hat's doing extremely well with Ansible, because they have so much experience with configuration management that they've got their agents already baked. Anything new comes in, they just learn, so they're already kind of ahead of the game there. I see this coming right into the business professional. So I want to ask you, because there is productivity gains and costs as well, but about a decade ago, there was a concept called the 10X engineer, and that the cloud enabled that technical labor to get a 10X efficiency. I think we're going to see 100X business professional-
Alex Rinke
>> 100%.... >> uptake. Take us through how that plays out, and what I got to do as a business to say, "Okay. My people are my asset. They know all the buttons, they've been with us forever, he built the process that doesn't work with the other girl's process over there, and she does that, he does that." So there's owners. That's an important part. Share your thoughts on this.
Alex Rinke
>> So what our customers are doing, and one of the customers you saw on the keynote was Exxon, ExxonMobil. And there's lots of those examples where they've established process owners for the first time, so someone that's accountable and then forwarded cash, for example, across systems, across silos, across applications. And those process owners, if we give them access to the right data, the right knowledge, and integrated common language that they can access across those systems, and in the end, they can drive incredible impact with their teams and organizations. And the work shifts from really a lot of repetitive tasks, to governing, scaling, overseeing, strategizing, right? So our human potential is unleashed. And you think about today, bad processes really hold people back. They can do more, they can achieve more. So sure, it's going to change some jobs, and sure, it's going to also create some disruption, and we need to manage that. But I think the upside case is that to your point, people will be able to do much, much more.>> They're enabling that 100X or 10X individual. Let's get the data side, because the process side, again, you guys are doing extremely well. When I talk to practitioners out there and these large companies that are going through the process, there's two camps. The process struggle bus that are on, "Oh my God, I got this process in this." They don't talk to each other, which you talked about. And then you got the data teams that instead of the large sets of data, so you got the data scientists, and maybe platform engineering, some data engineering. And then you have a company that has pretty much not a lot of process problems, but they have a ton of data. And so we're seeing people who have data, whether they're in an legacy company or a startup, tend to do really well with gen AI because they understand they have data. So they're using AI for a couple of reasons. One, using AI to say, "What is my data?" And then two, "How do I unlock that data?" And then after they get that done, they say, "What new data can I get?" So three progressions. Throw AI at the data and say I've got a ton of data. What is valuable? How do I do some instant successes? How do I get that little win, big win to knock it down, get validation it's working, and then how do I unlock this potential? And then wow, if I add more data to it, that's good too. So take me through your thoughts on that, because the first one's easy.
Alex Rinke
>> First one is easy. Yup.>> Okay. Second one is, okay, process helps there while it's scaling, we're getting some multiple productivity gains, demonstrable. And then here's my cost ratio done. That scales on a spreadsheet, and then it's like, "Wow, I want to enrich my data because I can affect my reasoning."
Alex Rinke
>> Right. So we see both of those things happening, right? So the first thing, the foundation layer, understanding the data and transforming it faster, I completely agree. The second thing, how do we get value out of the data? That's something where process mining really allows you to use the data to tell you about your processes and then allows you to monitor them on an ongoing basis to take action to feed agents. So that's really all we do every day is we take the data that companies already have and now we are deriving more value from it.
But the third piece is immersion, so there's a few things happening. One, companies instrument their systems better, so we work with a large bank not far from here, and they saw, well, we got some great insights from process intelligence, but there are systems that don't capture all the events. Let's put some middleware in. Let's actually capture more and more events as people execute their processes. People are putting agents on people's desktops. We have a task mining product where you can instrument the desktop and understand what processes are running on the desktop, and add that into your system-based process mining. So there's data enrichment. There's also a product that's in beta at the moment that we talked about in the keynote as well called Networks, where multiple companies are bringing together their process streams and process data to understand how are processes working when they leave the four walls of my company going to your company, and there's huge efficiencies there? So I think that once you get really good at driving value from your data, you want more of it, right? So you're going to->> And then you measure the health. How are we doing? I'm feeling good today. I've been working out, you know?
Alex Rinke
>> And that's another part, you want to monitor it, right? You're going to have agents, new applications. All that executing the processes, you want to have monitoring.>> Wow. So You guys got a great company. We could probably go for another hour. I do want to ask a couple more questions because a topic that's been around for a long time, but now with ransomware, has been kicked around a lot. Cyber resilience essentially is data backup and recovery that was renamed cyber resilience. Rubrik went public, brilliant branding, but the word resilience means recovery and being ready to handle things. In gen AI, there's a whole nother resilience challenge. Okay? So with gen AI, it's also very good, but also things can happen really fast. I can train maybe bad data, password sets, but we saw someone's passwords getting trained, and how do you untrain it? So you're starting to see the mindset of how do we be more resilient? How do you see that playing into companies as they think about the governance piece? I want to have explainability, I want have the ability to recover. You know what? We did something. We want to roll that back. What's your thoughts there? What's kind of the state-of-the-art thinking there?
Alex Rinke
>> Yes. So I think there's multiple things. One is the way you orchestrate how LLMs interact with your data needs a proper governance layer, right? So you need to understand exactly which information was exposed, make sure only the information that's really necessary is exposed, and then also understand what went into. When, for example, an agent uses Celonis sort of as a calculator, calculate the throughput time, we want to be sure that we can explain exactly how we got to that answer that fed the LLM. So that's basic stuff. The other thing that's really important is once you bring agents into a process, you need to have that monitoring and observability because you want to understand what the agents did. What we see a lot of our customers doing is they still have a human in the loop where the human approves or disapproves. So we talked about Cosentino. They've been able to get a 5X productivity gain in the order management clerks. The agent gets all the data, gets all the information from different process streams, makes a recommendation, but the human still approves it for now. That might change, but only as companies get more and more confidence. And then when we, for example, think about really how that's going to evolve, I think you're going to have more and more of that. So that's not a topic that's going to go away. That's going to be more and more, and you need very, very good governance and monitoring. A challenge still is you don't know how the LLMs reason, so that's not something we get involved in. There's other companies that do that.>> If my brain was a vector database, I'd be matching this conversation to some of the early DevOps conversations, guardrails, observability, management. How do I turn off-
Alex Rinke
>> It's very similar.... >> services? Very similar DevOps-
Alex Rinke
>> Yeah. Very similar.... >> kind of growth there. I want to get to the customers, but one final question. You brought up governance. I missed this before. Open table formats, great, we're seeing that standard. The catalog governance market, what's your view on the big change that gen AI is forcing, disrupting, and disruptive enabling the catalog side of it? Because I think to do horizontal scalability, you've got to have a intelligent governance. What's the old way and new way in governance? How does a customer who's made an investment in master data management maybe a decade ago, what are they staring down the tracks now on? What are they thinking? What's the ideal?
Alex Rinke
>> Well, the old way's becoming very rigid, in that way, getting good data. It's like, "You want a new data product? See you in two years." Or it's see you in six months, right? You need to go through all this process. The old way is, "Hey, you want to change your master data, create a new supplier? See you in six weeks, eight weeks," right"
And then the old way is let's spend millions and millions of dollars on master data cleanup projects where we go through all the data, we call people, we try to confirm the information. That's the old way. It's very expensive and never really worked. You can go to 10 companies, "Are you happy with your master data?" You're going to get 10 nos, okay? You're not going to get a single yes. Trust me. So the new way and what we are doing, and we actually launched some apps around this in Celosphere, is how do we bring process execution and master data together in a single view so that we understand? Not just where you have duplicate supplier records or something like that, but what impact does it have, right? "Hey. We got 5000 invoices from this supplier, and we had to rework 3000 of them." We need to look at the master data records associated with their product catalog, their supply catalog, et cetera. So what we can do now is we can actually prioritize you. So because master data governance will never go away, you're always going to have to do it, but we can actually do it->> But it has to change?
Alex Rinke
>> It has to change. It has to be informed by data, it has to be prioritized. It has to be fed with recommendations. "Hey, we had 5000 rework items." If you had changed the price list, because you always changed the same prices to this, you can just accept that with one click. All that rework goes away. So we can have intelligent master data management based on what actually happened in the transactions, what actually happened in the process, and we can enable that using obviously LLMs as well in terms of making those recommendations and identifying those patterns. So we are very excited about that, because we talk about supply chain planning. Supply chain planning is maybe about the optimization engine, but it's a lot about the inputs, the planning parameters. So we can actually look at what actually happened in the process and then recommend the best master data.>> It's a flywheel where you're changing the input criteria based on what's happened, so not just input-
Alex Rinke
>> And what went wrong. Exactly. It's a loop, right?>> It's a super-exciting environment. Okay. Before we get to the customers, what's the basic think you sell? For the folks watching, what do you guys sell? An app? Is it a software? Is it a service? Take us through the basic, how are your customers engaging with you? What are you selling?
Alex Rinke
>> So we sell two things, really. We sell apps by us and our partners, so if you are in accounts payable, we have all these apps. If you're in supply chain, we have all these apps. If you are a bank doing cross-border payments, well, we have a partner that has built an app to optimize that, so that's package solutions you can buy, you can implement very quickly, and you can optimize. And then we are selling the platform, and the platform allows you to bring in any process data, go horizontal across the enterprise, and go really, really big. Like our customer, BMW, for example, that is virtually analyzing almost every process across the business, 80% of their cars, and the processes, manufacturing, finance, procurement. So a lot of people start with a use case that's packaged and then use the platform to go across->> And your partner's strategy is to partner on the platform level-
Alex Rinke
>> Yes.... >> and give the customer choice on the apps. So if they want to use watson, they can use that, if they want to use you, they can use you? Or is that how it works?
Alex Rinke
>> Exactly. And on the apps, actually. So the apps are basically IP that partners package on top of the platform. But if you use the platform, you get all the integrations, you can use it to enable what's next, enable Microsoft Copilot Studio, enable AWS. So those partnerships are really, really important, because that's how people operationalize the change and the solutions.>> Partner-
Alex Rinke
>> And by the way, our partners can build apps using those tools as well.>> It's interesting. When you talk about the business market, the B2B market in this now connected environment, partners and integrations are critical. So partnerships, you've got great. You have to have a good partnership to get value out of that. But the integrations is what matters. At the end of the day, how do you feel about the integrations? What's the key there? What makes you guys different, and what's unique?
Alex Rinke
>> Well, first of all, we are committed to do cross-platform process intentions, right? We are not associated with any of the big systems of record in itself. If you're an Oracle ERP customer, well, we have great integrations. If you're an SAP customer, we have great integrations. Whatever system of record you use, we have a lot of those integrations built, and we are building more every single day, so that's on the data side. If you have your data in a data lake, like a Snowflake, or if you use Databricks, well, we have great integrations there as well, and we are continuing to advance that and build more and more of those integrations in partnership as your data . It doesn't really matter where you have those solutions. And then on the consumption side, again, if your strategies around Microsoft, and Copilot's an agent for Microsoft, well, we want to provide all the integrations there, and that's what we announced with agency, similar to IBM and others. So we have a super-open platform strategy, we know what we're good at, we focus on what we are good at because that's a huge opportunity for us, and we integrate and embrace partners up and down the steps.>> What's interesting is that the modern era of gen AI right now that we're seeing is that you can have a platform and apps combination without making it completely siloed. Customers can have choice, plug and play, use the building blocks. Because in data, you have to capture the value, and the platform gives you the data value of the data-
Alex Rinke
>> 100%.... >> and then the apps is where it's rendered.
Alex Rinke
>> Right. It's where it's rendered, and it's all these pre-packaged use cases that you can implement very fast.>> All right. Just one with customers. Tell us about your customers. BMW, you got a bunch of customers on stage, we interviewed a bunch of them. Talk about the size and scope of the customers. Is there a certain mix you're seeing? Is it across the board? I know you've got some big names and they've got a lot of data. Is it data companies that got a lot of data, they're full of data, they're busting out of the seams? Or you got startups? Take us through the customer roadmaps.
Alex Rinke
>> Well, the great thing and something that we're really excited about in Celosphere is to see all these customers from small to big. Many of the largest corporations in the world.
And one of my favorite stories is Ingka Group. It's an IKEA retailer. One of the main IKEA retailers. And when you order a kitchen from IKEA or from anybody, IKEA is very good at it, but that's a complicated process, right? And it's also very high-consequence, because whoever says your kitchen screws up, it doesn't fit, it arrives late, not all the components arrive at the same time, you're going to remember that for a while, because it's great to have a new kitchen and to move in, but it's not a usually very fun process to get there.
So they have this thing where they're using Celonis to create the perfect order, and they're thinking about a kitchen order end-to-end through the systems, through the different sub-processes, and define a perfect order, and they're driving that up with Celonis. And I love that because it touches inventory, it touches customer experience. It touches real people out there that are building a new kitchen, moving into new homes, so that's just one example. But such as the big corporates that have huge scale, like an ExxonMobil, like an Ingka, it's also really more and more public sector, right? State of Oklahoma, they had an issue where they had three billion of purchasing volume going outside of their central agency for procurement and services. And they've been able, within month, they had an audit, it's all public, and that was flagged. And within a month, they got full visibility across systems in their procurement, end-to-end purchase-to-pay, to understand what exactly can they do to make sure that the taxpayer money is spent in the ideal way possible? Or Northwestern Hospital, right? That's a great case. So like many customers we just talked about, they started in finance and procurement optimizing those processes, but now, they're taking Celonis into the imaging space. For all the patients that need a mammogram, how is that process? How can we reduce wait times? And the next step is they want to take it across all the patient flows so that when you go into one of their hospitals, you have an ideal journey. You have minimum wait times. And that excites us, because yes, it's economic value, but it's also really experience value and helps people, so there's a big range and lots of customers across industries->> You know what's interesting? As you talk, just my mind starts thinking about some of the things I've seen over the past 15 years just doing CUBE interviews. The consumer market has always had the big money to do big data projects. The enterprises, there's some big whales out there that have done some big data projects, mostly monolithic, big systems, Kubernetes clusters that didn't work, and they moved to Spark and did data lakes. But no one really had a good holistic view of the business data that they had, data about our business in one holistic place. It might've been stored in a data lake, but it wasn't understandable.
Alex Rinke
>> Exactly.>> And I think that consumer businesses have done that. For example, in the banking world, consumer banking is state-of-the-art. They don't care about cost. They just want to get the customer relationship nailed down.
Alex Rinke
>> Just so you get it done. Yeah.>> You go talk about commercial banks, they're lagging, and usually smaller. And so now for the first time, you can do things like saying, "Hey, I'm going to send a kitchen." Well, if you're sending it to Asheville, North Carolina, then you know the roads are closed, right? You need that data. Where's that data going to come from?
Alex Rinke
>> Exactly.>> This is where you start to get into, okay, I can do more if I peek my head out of my dataset, and so you're seeing the entire business B2B market just explode in growth.
Alex Rinke
>> Oh, 100%.>> This is an untapped market.
Alex Rinke
>> Oh, 100%. There's so many challenges there, right? You think about supply chains, for example, and just the internal processes. So one of our great customers is Smurfit WestRock, and they've been able to drive huge optimizations in how they manage inventory because there's multiple processes and multiple datasets that you have to bring together in one holistic view, plus you need the business context. What does on-time mean for us? What's still okay? What's not okay? It's different for every business. It's different depending on the product you sell. And they've seen huge amounts of improvement there, and now they're plugging AI into it so that material planners and even people in the factories can just chat with a chatbot interface to get information about their supply chain. Like, "Hey, can we get this material from another factory?"
Well, instead of purchasing it, we see the purchase order, and we can stop that purchase order because you can get it from another factory that's nearby, right? So the amount of opportunity in the supply chain just within the four walls of the company is huge, but then with our network as you extend it across multiple players. And then there's also, again, it's about partnership, so we have a partnership with p44, which provides visibility beyond the four walls of your company and many other supply chain businesses.>> So you become smarter with more partnerships? Because you're basically connecting, again-
Alex Rinke
>> We're connective tissue, you know?... >> in that connective ecosystem formula that's emerging. I've never seen this before like this.
Alex Rinke
>> Yeah. And we talk about connecting your enterprise with process intelligence.>> Yeah. To . Alex, you got to be excited. I got to ask the final question first. So I love that you're in New York now that we have our set here in New York, you've got a great ecosystem developed, and of course, theCUBE has a global network. But as we start doing more physical events, we definitely want to have you here and do more content, because this is going to be an ongoing set of conversations, because it's just the tip of the iceberg. How are you guys feeling? I mean, as the co-founder, you've been through the journey. I won't say war, but it's been up and down since Hadoop. It never went away, but the hockey stick doesn't kick up until, what? 2018?
Alex Rinke
>> Yes. COVID.>> COVID was around that timeframe?
Alex Rinke
>> A little bit before. Yeah. But->> So you're puttering along, and then, boom, you go up. How's the team? How many employees do you have? What's the update? Give us some stats, numbers, revenue. We can share it, or what's it like at the event? What are the hallway conversations like? Give us a vibe.
Alex Rinke
>> Right. Well, we have almost 3000 employees now, and I tell you, it took us almost six years to get to 80, okay? So the first six years was 80, and then in another six years, you're much, much bigger. But what we've always been driven by is the ROI that we can provide to our customers. That's how we incentivize our people, that's how we measure ourselves, and that's how our customers measure us. And that's really exciting. And it doesn't really matter what the market is doing. We are focused heads-down on what we can do for our customers, because they always want to make their processes work better, to save money, to improve the experience for their employees, for their customers.>> So your cultural theme is productivity gains, ROI for the customer?
Alex Rinke
>> Value. It's all about value.>> Value. Value extraction. Well, congratulations again. Great event. Thanks for coming here in our new CUBE East studios. Of course, when you're in Palo Alto, we got a set there too.
Alex Rinke
>> Pleasure to be here. I love you, what you guys are doing->> Yeah. I love it. Yeah....
Alex Rinke
>> and I look forward to being back.>> Great stuff. Okay. I'm John Furrier, your host of theCUBE. We are here in New York City at the NYSE CUBE Studios partnership with Brian Baumann and Wired, NYSE Wired. It's a new community. It's an open ecosystem that we're rolling out, and it's kind of an experiment, but it's kind of a digital twin of our events. Bringing the community together and forming high-value networks of great people, experts, inventors, entrepreneurs, fit investors. Just cool people who want to contribute to the community. If you're interested, hit me up on DM. I'd love to talk more. Thanks for watching.
