Celonis Unveils Process Intelligence Graph to Transform Enterprise AI Operations

Process Intelligence Graph: Transforming business operations fast - SiliconANGLE

Process Intelligence Graph emerges as the digital twin powering AI-ready operations The Process Intelligence Graph is quickly becoming the connective tissue of modern enterprise operations -- a living system that turns hidden process data into real-time intelligence. By mapping how work truly flows across teams, systems and decisions, this digital twin gives organizations the power to see inefficiencies before they spread, forecast outcomes with precision and act on insights grounded in context rather than guesswork. It's the foundation behind Celonis SE's mission to "free the process" -- an effort to unlock data trapped within complex workflows and transform it into a clear, evolving picture of how business really happens, according to Daniel Brown (pictured), chief product officer of Celonis. "I think freeing the process is like, 'Hey, if you can't get that information out and you don't have visibility into how you're actually working, that's like a jail,'" Brown said. "We want to free the process, but let's start with the data core because it's the big enabler. In order to be able to understand and organize all of that data and use it, you need tons of scale, you need to be able to transform it into semantics, into a Process Intelligence Graph ... that's kind of the heart, and it's only going to get bigger, this digital footprint, it's a hockey stick." Brown spoke with theCUBE's Rob Strechay and Savannah Peterson at Celosphere 25, during an exclusive broadcast on theCUBE, SiliconANGLE Media's livestreaming studio. They discussed how the Celonis Process Intelligence Graph is transforming decision-making through innovations, such as its living process map. (* Disclosure below.) The Celonis Process Intelligence Graph offers a new way for organizations to understand, monitor and improve their operations by mapping processes through a connected, data-rich framework. This tool enables data-driven improvements that can reduce challenges by up to 99%, according to Brown. "The customer is Novo Nordisk, which you know is a pharmaceutical company out of Denmark," he said. "What they're trying to do is reduce the burden of certain processes, and one is protocol deviation, a standard life science pharmaceutical process. What they do is they've used agents to identify these deviations and document them ... that documentation has gone down from 100 hours to one hour." AI agents rely on understanding processes because context turns data into actionable, meaningful and safe insights. Agents don't just act on isolated steps -- they need to see how actions connect. Without this, decisions that seem correct in isolation can wreak havoc in real workflows, Brown pointed out. "When Celonis moved from case-centric to object-centric process mining, it was a very smart strategic bet," he said. "It was a recognition that you needed connected processes. A digital twin needs data to be organized, it needs data to be related, and then, the last part is to enrich it. That plugs into your agentic strategy because if they don't have access to that information, then your agents, they won't know what to do." Here's the complete video interview, part of SiliconANGLE's and theCUBE's coverage of Celosphere 25:

Celonis makes compelling case for 'freeing the process' to operationalize AI returns

While most enterprise software vendors are still promoting AI as a silver bullet, Celonis arrived at its annual Celosphere conference this week with a more grounded message: enterprise AI is failing to deliver, and the reason has nothing to do with model capabilities. Speaking to a packed audience in Munich, Celonis Co-CEO Alex Rinke cited a statistic from IDC that is an increasing reality for CIOs making decisions: only 11% of companies are getting any measurable benefit from AI projects today. Rather than gloss over this inconvenient truth, Rinke used it as the basis for Celonis' pitch - that process intelligence provides the missing operational context that AI needs to actually work in the enterprise. Rinke told the audience: I remember being on a Christmas vacation in 2022, and ChatGPT had just come out. I was on it 24/7. I wasn't just terrorizing my family - I was also terrorizing everybody at Celonis. I was texting people like, 'This is going to change everything.' And I still believe it will and is starting to - but we've also all learned that it's harder than we thought. That honest assessment set the tone for Celonis' announcements, which focused less on flashy 'AI will solve everything' features and more on providing the infrastructure and methodology for making AI actually deliver business outcomes. The company's core argument: enterprises need to "free the process" from legacy system constraints and create a living digital twin of operations before AI can be effectively deployed. Rinke outlined three fundamental problems holding back enterprise AI today. First, essential business context is trapped inside individual systems, with critical operational data spread across process designs, enterprise architecture diagrams, log files, and even employee actions that aren't captured anywhere. Second, AI solutions are often deployed based on "the loudest voice in the room" rather than strategic analysis of where they'll actually move the needle. Third, siloed AI solutions don't integrate with existing workflows and technology investments. The solution, according to Celonis, is a structured approach built on what it calls the Process Intelligence Graph - a semantic layer that creates a digital twin of business operations by extracting and enriching data from across the enterprise. On top of this foundation, the company has built capabilities to analyze, design, and operate AI-driven processes. Rinke explained: Our analysis capabilities allow you to discover the process and where to strategically deploy AI. Then you have our design capabilities so that you can redesign your target state based on these insights. And lastly, you can now operate these new improved processes, orchestrating AI solutions directly within your existing workflows. During a pre-conference press event, Divya Krishnan, Celonis' VP of Product Management, demonstrated how this works in practice using a composite example built from actual customer deployments. Krishnan's demo centered on a fictional company called Keystone Steel facing flat sales and rising costs. The demonstration walked through how Celonis' approach would tackle this challenge, moving from analysis through redesign to agent-enabled operation. The analysis phase began with building what Celonis calls a "living digital twin" - unifying data across Keystone's ERP, manufacturing execution system, and crucially, task mining data that captures clicks, spreadsheets, and desktop activity. This comprehensive view revealed that products were being scrapped after successful production runs because they fell slightly below the highest quality standard. The insight came from the Process Intelligence Graph's ability to provide context across systems and regions. The analysis showed that Denmark had a consistently lower scrap rate - not because of better manufacturing, but because sales representatives were manually reaching out to customers via email to sell outclassed units at a discount. This process wasn't documented in any system, but task mining captured it. Krishnan said during the demo: That's the power of analysis with Celonis. Denmark very impressively turned scrap into sales manually, but wouldn't it be even better if it could be done with agent-enabled workflows? The design phase used Celonis' Process Designer to redesign the workflow with AI assistance, recommending two agents: one to match materials with appropriate customers, and a voice AI agent to follow up if there's no response within 48 hours. Finally, the 'operate' phase showed how Celonis' Orchestration Engine (now generally available) coordinates AI agents, people, and systems in one flow - with intelligent triggers automatically starting the process when materials are at risk of being scrapped. The continuous feedback loop here is worth noting. As Krishnan explained: We're continuously monitoring the performance of these agents. These are the new process steps we just created. This isn't historical data. We're looking at every interaction between the voice agent and the customers to assess every bottleneck, every opportunity, and how we can refine this engagement. This is where I see Celonis differing from other vendors in the market. I've been to many conferences this year that have spoken about context being critical to AI deployments, but they don't take it to the next phase, which is making this context operational. Celonis is showing that it understands the dynamic environment of enterprises, where change is continuous. For AI to become effective, Celonis sees a contextual layer that takes knowledge and redeploys change regularly. Making this vision reality though requires significant technical infrastructure, and Celonis announced several platform enhancements at Celosphere. The company's Data Core - its high-performance data infrastructure - is now generally available, with Celonis claiming it provides the scale and low latency needed for operational use cases. Chief Product Officer Dan Brown emphasized that some customers are now refreshing data in less than a minute, enabling real-time operational decision-making rather than historical analysis. Perhaps more significantly, Celonis announced a partnership with Databricks using Delta Sharing for zero-copy, bi-directional integration. This allows customers to access process intelligence without moving data from their existing lakehouse environment. Brown noted that Snowflake integration is also on the roadmap. Brown explained during the press Q&A: If people have invested in a data store repository already that we can attach to, obviously, that makes it easier from an enterprise IT perspective. Reusing the asset allows us to move more quickly. The company also announced enhanced task mining capabilities with AI-driven Task Discovery, allowing Celonis to capture and organize desktop activity data that fills critical gaps in the digital twin. Combined with the ability to integrate enterprise architecture blueprints, unstructured data like PDFs, and data from beyond organizational boundaries through Celonis Networks, the platform is positioning itself to ingest operational context from wherever it exists in the enterprise. During the Q&A session, I asked Celonis executives to address what's becoming an increasingly crowded market position - the "context engine" for AI. Nearly every enterprise software vendor is now claiming to provide essential context for AI systems. President Carsten Thoma offered a surprisingly measured response, acknowledging that Celonis doesn't claim to provide all context for all types of data. He said: We will never claim we have all the context. We pull a lot of context also from large data pools. This is why zero-copy integration with Fabric, Databricks, and Snowflake in the future is incredibly important for us - so we don't have to own all of that data. However, he argued that the specific type of context Celonis provides - operational process intelligence - is uniquely critical for enterprise AI. He added: If you look at the enterprise AI level, you can live without many other things, but you cannot live without this operational digital twin that we provide. If you don't understand what your company is doing, I don't know how you could put AI to work and feel confident about it. Critically, Celonis is positioning itself as an open platform rather than trying to own the entire AI agent stack. The company announced support for Model Context Protocol (MCP) servers, allowing the Process Intelligence Graph to be incorporated into third-party agentic AI platforms like Amazon Bedrock and Microsoft Copilot Studio. Brown acknowledged that the agentic AI landscape will be distributed rather than winner-take-all: I don't really see a world where one or a handful of companies 'owns' this. I think Celonis will fit in by being a part of agents that are external to us. I think we will have our own agents. I think we will have agents that are completely inside of our own platform and domain. The company showcased several partner applications built on the Process Intelligence Graph, including Rollio (process collaboration agents for resolving exceptions in Microsoft Teams), Trullion (AI agents for lease accounting), and Bloomfilter's Agent Miner (which captures not just human activity in software development but also AI agent activity and reasoning). The contrast between Celonis' messaging at Celosphere and what most enterprise software vendors are saying about AI is very apparent. While competitors are either leading with features or promising that AI will magically fix everything, Celonis is offering something more valuable but less flashy: a blueprint for how AI will actually change enterprise operations. The analyze-design-operate framework acknowledges that AI deployment requires understanding where to apply it, redesigning processes to accommodate it, and continuously monitoring and improving those AI-enhanced workflows. This isn't the "sprinkle AI on everything" approach that's producing those dismal 11% success rates. The emphasis on operational context also addresses a real gap in the enterprise AI stack. As companies race to deploy agents, many are discovering that generic AI without business context produces impressive-sounding but ultimately useless results. The Process Intelligence Graph's ability to capture not just system data but also task mining information, process designs, and enterprise architecture creates a semantic layer that helps AI understand how the business actually operates. The zero-copy integrations with Databricks (and future Snowflake support) are smart moves, allowing Celonis to meet customers where their data already lives rather than forcing yet another data movement and governance headache. Combined with the generally available Orchestration Engine, Celonis is building the infrastructure for the continuous analyze-design-operate cycle it's promoting. However, building these digital twins is complex work, requiring data engineering and business process expertise. The partner ecosystem will be critical to making this accessible to a broader market. And while the open platform approach is strategically sound - no single vendor will own the agentic AI space - Celonis will need to demonstrate consistent customer ROI as the market matures beyond early adopters. Equally, buyers are often making decisions based on what's already in their enterprise technology stack - hoping that their historic, strategic partners will figure this out for them down the line. However, I've been impressed by Celonis' realistic talking points - being honest with buyers about the changes required is a key part of winning trust. In a market full of AI hype and disappointed enterprise buyers, Celonis' message that successful enterprise AI requires operational context, strategic deployment, and continuous improvement feels refreshingly grounded. Whether "freeing the process" becomes as essential to enterprise AI as Celonis claims remains to be seen, but the company is at least asking the right questions about why AI is failing and what's required to fix it.

AI-powered process intelligence key for Deutsche Telekom - SiliconANGLE

Deutsche Telekom sees process intelligence powering smarter automation Enterprises are rapidly advancing toward full-scale automation, integrating artificial intelligence, in the form of AI-powered process intelligence, to drive smarter, data-informed operations. That includes combining process mining with machine learning and predictive analytics to deliver more personalized engagement. Deutsche Telekom AG's broadband business involves complex products and processes that generate significant operational data within its sales and service departments. Its collaboration with Celonis SE involves object-centric process mining, according to Sebastian Dahs (pictured), vice president KVP of sales and service at Deutsche Telekom. "With Celonis, we tried whether we can find out more about the customers, more about the processes, and also finding solutions to improve these processes and handling these customers in order not to lose the customers, to make these customers happy, but also to keep the revenue at Deutsche Telekom," Dahs said. Dahs spoke with theCUBE's Savannah Peterson and Rob Strechay at Celosphere 25, during an exclusive broadcast on theCUBE, SiliconANGLE Media's livestreaming studio. They discussed the evolution of AI-powered process intelligence, and how data insights are transforming customer experience. (* Disclosure below.) As operations evolved, improvements in both technology and processes played a key role in achieving better results. Three main factors made this possible, according to Dahs. "The first one is having access to the data, having the data in order to get it structured," he said. "Sometimes it's really difficult to have the interface to all of these data," The second key factor is the human factor, Dahs added. While predictive models provide insights, success depends on engaging with customers directly, responding with sensitivity and guiding them carefully through the process. "The third aspect is the monitoring, the analysis, so verifying that there's an effect," he said. Customer interactions often come at emotional moments. Deutsche Telekom strives to uphold its reputation by delivering exceptional service and support during those times, Dahs emphasized. "We want to serve as a premium provider, especially once a customer is in a problem or in a process gap," he said. "We want to be there and helping our customers through these times." Here's the complete video interview, part of SiliconANGLE's and theCUBE's coverage of Celosphere 25:

AI agents is reshaping enterprise integration - SiliconANGLE

AI agents are redefining how enterprises connect their digital worlds - theCUBE analysis The next wave of enterprise tech isn't about bigger models or faster chips -- it's about systems that can think and act across the business. Fueled by artificial intelligence, a new generation of AI agents is breaking down silos between data and decisions, creating a connected, adaptive network of insight. No longer confined to dashboards or chatbots, AI agents are beginning to orchestrate the flow of work itself -- integrating data streams, interpreting intent and triggering actions in real time. It's a shift from automation to autonomy, where AI becomes the connective fabric of modern business, reshaping how enterprises learn, respond and evolve, according to George Gilbert (pictured, right), principal analyst at theCUBE Research. "Just the way the smartphone really accelerated the rise of the cloud, agents are going to accelerate the rise of the digital twin because they're only useful when they can see across the enterprise," Gilbert said. "Agents need the four-dimensional dynamic map, that's what Celonis is. That's the big picture." Gilbert spoke with theCUBE's Savannah Peterson (left) and Rob Strechay (center) at Celosphere 25, during an exclusive broadcast on theCUBE, SiliconANGLE Media's livestreaming studio. They discussed how AI agents are transforming enterprise integration by connecting data, processes and people through intelligent, end-to-end automation. (* Disclosure below.) As digital ecosystems grow more complex, the ability to see end-to-end processes becomes essential. AI agents are pushing that frontier, connecting applications that once operated in isolation and creating a new kind of enterprise intelligence. They thrive when given the complete process view that modern data architectures now make possible, Gilbert explained. "They made this big transition; it was a pretty profound transition," he noted. "The technical term is object-centric process mining, but that's where an object like a customer or a sales order can participate in many different processes. That's crucial for busting out of these silos." That holistic perspective is what allows enterprises to analyze, design and operate processes in real time rather than simply observe them. It turns passive dashboards into dynamic systems that guide continuous improvement. Process intelligence platforms now serve as the map AI agents depend on to act effectively and autonomously. "I think what you're both saying really ties tightly together with what George saw on the tech side and how the business metadata and the technical metadata coming together in a way that I don't see anybody else doing," Strechay said. The evolution of data infrastructure has made this integration more accessible and scalable. Instead of pulling from static databases, AI agents can now interact with hybrid data environments that draw from multiple systems and cloud sources. This change enables real-time operational visibility across the organization, according to Gilbert. "It's no longer got an on-prem database as its engine, so more scalable, and then it can take data from Databricks or Microsoft Fabric, eventually Snowflake, and it pulls that into the graph," he said. "It's now much easier to get your operational data into the graph, but then you can send the data back out." Beyond the technical architecture, the human dimension remains critical to success. True adoption happens when employees view AI not as a surveillance mechanism but as a partner in achieving outcomes. When teams recognize that agents exist to enhance their work, not replace it, the organizational culture shifts, according to Peterson. "What I've found very enlightening in talking to so many of Celonis' customers is the way that they've managed to get cultural buy-in," she said. "I think that's part of why this technology is having so much success." That balance of technology, process and trust is shaping a new model for enterprise work. It blends automation with human insight, empowering teams to innovate and execute more effectively. The convergence of AI agents and process intelligence is turning once-static workflows into living systems that evolve with business goals. "It was in the '70s and '80s, there was a big move toward making factories work efficiently end to end," Gilbert said. "My take on process intelligence is we're basically building built-to-order assembly lines for knowledge work, and that this is the intelligence." Here's the complete video interview, part of SiliconANGLE's and theCUBE's coverage of Celosphere 25:

Exploring Celonis' object-centric process mining approach - SiliconANGLE

The next wave of business intelligence: AI brings object-centric process mining to life Just a few years ago, object-centric process mining was a relatively new and experimental concept. Today, it has become the de facto approach to discover, monitor and improve business processes. Organizations are no longer just talking about AI -- they're applying it to real-world challenges. From car manufacturing to air travel and city services, process intelligence is driving measurable improvement across industries. Organizations now leverage OCPM with AI to gain visibility across complex systems, predicting outcomes with greater accuracy and reducing operational friction, according to Wil van der Aalst (pictured), chief scientist of Celonis SE. "What I find super interesting is that if I look at something like object-centric process mining, which used to be something novel and companies were not sure when and how to apply it, I think now the landscape has completely changed," Van der Aalst said. "Everybody has bought into it and sees that this is the way to go. What you also see is that now people do not just talk about AI, but also actually apply it." Van der Aalst spoke with theCUBE's Rob Strechay and Savannah Peterson at Celosphere 25, during an exclusive broadcast on theCUBE, SiliconANGLE Media's livestreaming studio. They discussed the promise of object-centric process mining across critical industries and use cases. (* Disclosure below.) The most impactful OCPM use cases target a company's core business -- not just administrative workflows, according to Van der Aalst. Lufthansa's use of process mining to minimize flight delays stands out as a model, illustrating how these insights can improve both efficiency and customer experience. "If you look at customers that were on the main stage, like Mercedes-Benz, in supply chains, it's very important that things are reliable and that you have an idea when something is going to happen," Van der Aalst said. "They're both object-centric process mining, and the ability to use AI to make more reliable predictions ... is a clear case of something that you could not do before." Beyond the enterprise, process intelligence can reshape public services. In Aachen, Germany, the local government came to appreciate the value of process mining after AI's limitations without a data context were demonstrated, Van der Aalst explained. "AI can help with these things, but you should first get the basics right," he said. "You should first get what the processes are, where the actual problems are ... you need to become evidence-based." Process intelligence functions as a layer across systems -- not a replacement for them. By integrating data from disparate sources, organizations can identify bottlenecks and act strategically, Van der Aalst noted. This composable approach aligns with Celonis' broader themes of openness, data-driven decision-making and AI orchestration. "I think with new technologies, lots of things are possible to do things very quickly, but we also have to realize these things are probably temporary," he said. "But getting this layer in your whole organization [so] that everything is well organized is not temporary. That will also remain valid when trends and techniques change." Here's the complete video interview, part of SiliconANGLE's and theCUBE's coverage of Celosphere 25:

AI-powered process mining: How Allianz enhances customer experience - SiliconANGLE

Allianz reimagines the insurance experience with AI-powered process mining As organizations embrace the era of artificial intelligence, AI-powered process mining is emerging as a catalyst for operational transparency, efficiency and predictive insight. By turning complex workflows into clear, data-driven narratives, it empowers enterprises to act with precision and deliver measurable outcomes. For global financial services leader Allianz SE, that transformation is personal. In partnership with Celonis SE, the company is redefining what customer satisfaction means -- shifting from contentment to genuine enthusiasm through the intelligent application of AI-powered process mining, according to Jan Malmendier (pictured), chief operating officer of Allianz. "We are an insurance company so, traditionally, we are probably not the one company that really thought about processes a lot," Malmendier said. "We are changing that and ... coming from customer satisfaction to customer enthusiasm, really saying, 'We want ... customers to love our products and processes.' That's where process mining helps us a lot." Malmendier spoke with theCUBE's Rob Strechay and Savannah Peterson at Celosphere 25, during an exclusive broadcast on theCUBE, SiliconANGLE Media's livestreaming studio. They discussed the transformative power of AI-powered process mining and how Celonis enables Allianz to harness it effectively. (* Disclosure below.) AI-powered process mining transforms raw data into actionable insights. Allianz leverages it to streamline accident claims -- cutting touchpoints, processing time and costs -- and to simplify home purchasing, accelerating transactions and enhancing customer experience, Malmendier pointed out. "One was really the accident claims process we completely revamped, where it took, in the past, years until we were really ready to pay," he said. "Now, we can pay in most cases after a few days and that really makes a difference. We completely changed the house purchasing process when a house moves to a new owner ... we got rid of all the documents required in the past and a very complex triangle process. We made it very two-dimensional." Celonis empowers Allianz to adopt a "systems thinking" approach, connecting customer interactions and internal processes to optimize the entire operational ecosystem. By leveraging data, analytics and tools, such as digital twins, Allianz can continuously monitor, understand and improve end-to-end processes, enhancing customer experiences and operational efficiency, Malmendier added. "We acquired a company in the U.K., and they have some really great system-thinking coaches," he said. "On the other hand, Celonis helps us to really do that on a large scale so we can build all our transactions into a digital twin and then check if the processes we envision to make our customers truly happy really happen in real life." Here's the complete video interview, part of SiliconANGLE's and theCUBE's coverage of Celosphere 25:

Celonis' AI-driven process intelligence vision, explained - SiliconANGLE

The hidden language of AI: Why clarity, not capability, defines success The multi-year reorganization journey at Celonis SE has culminated in a system-agnostic, artificial intelligence-driven process intelligence platform. Simultaneously, the company's market positioning has strengthened, with a fourfold increase in the number of platform customers in six months -- across more than 15 use cases and domains, according to Carsten Thoma (pictured), president at Celonis. With so much going on, how does Celonis plan to maintain its momentum as AI continues to entrench itself as an inalienable value differentiator? "Two years ago, we launched a concept of [object-centric process mining], this system agnostic, wall-to-wall, unbiased view," he said. "It was a technology, it was hard to operationalize, and it did cost us some nerves and a lot of investment, but we successfully did so. Last year, we embedded this concept into a platform that is open for business -- one that can serve our partners, customers and the whole ecosystem and community." Thoma spoke with theCUBE's Rob Strechay and Savannah Peterson at Celosphere 25, during an exclusive broadcast on theCUBE, SiliconANGLE Media's livestreaming studio. They discussed Celonis' central message: process intelligence isn't just about optimization anymore -- it's about reimagining how enterprises think, act and grow together. (* Disclosure below.) In an age where organizations are stuck between hype and value in AI, Celonis' focus is on clarity. While many players struggle to differentiate between generative and enterprise AI, the company developed a "process language", a shared business understanding that enables true AI, according to Thoma. "Celonis developed a process language that is a common business language and creates a shared understanding," he noted. "I think that the clarity for customers and also our partners that this layer brings is essential, especially this year with all this AI noise that we had in the industry last year." Companies struggle with agentic AI because their underlying processes are broken. Celonis' AI-driven process intelligence layer addresses this foundational gap, enabling agentic systems to function as intended, Thoma explained. "It's a question of keeping it elastic, keeping the velocity high and stable from an infrastructure perspective, and then customers will adopt," he said. "Every use case, every app, every partner app that will be launched over the next months will drive this adoption." The company also announced plans to double its partner applications by the end of January -- adding 30 new apps on top of the existing 23 -- signaling rapid ecosystem expansion and continued platform elasticity, Thoma added. Here's the complete video interview, part of SiliconANGLE's and theCUBE's coverage of Celosphere 25:

Process intelligence drives enterprise AI transformation - SiliconANGLE

Inside the rise of process intelligence: Turning enterprise data into real outcomes The race to make enterprise data truly useful is heating up -- and process intelligence is quickly becoming the secret weapon. By fusing analytics, automation and artificial intelligence, it's turning once-static workflows into self-improving systems that actually deliver measurable outcomes rather than just dashboards. In today's AI-driven economy, that evolution marks a shift from observing to acting. Businesses aren't just analyzing how things work -- they're teaching machines to anticipate, decide and optimize in real time. The result is a new playbook for operational efficiency, where data silos fall away and decision-making becomes a continuous, intelligent loop between humans and technology, according to Rudy Kuhn (pictured), lead evangelist at Celonis SE. "We have decisive AI to make decisions or support decisions by humans, from humans," Kuhn said. "We have agentic AI ... to do something and this is what really moves the needle in terms of value." Kuhn spoke with theCUBE's Savannah Peterson and Rob Strechay at Celosphere 25, during an exclusive broadcast on theCUBE, SiliconANGLE Media's livestreaming studio. They discussed how process intelligence is transforming enterprise operations by connecting data, AI and automation to drive real business value. (* Disclosure below.) For many organizations, process intelligence starts with visibility. By mapping workflows end to end, enterprises can identify where friction lives and where automation will truly make a difference. The focus is on enabling AI not just to observe but to decide and act. This approach links human context with machine execution, Kuhn noted. "They started by using process mining, process intelligence to understand the process, to get all the data, all the context data that is required to train AI," he said. "Then, they use AI not to generate text and nice pictures, but really, use AI to make decisions. They use these decisions with the orchestration engine to execute on it. It's really get the insights, make the decision and perform action, and that's how value is created." That pattern is being replicated across industries. Whether it's automotive, energy or logistics, process intelligence is turning operational data into continuous improvement, Kuhn explained. The emphasis is on outcomes -- efficiency, sustainability and speed -- not just technological adoption. "If you look into business processes ... it's impossible to have all the data in one silo; it just doesn't work," Kuhn said. "More important, you are never alone in your process. You have partners, not only internally, but also externally." Partnerships are critical to this evolution. Modern enterprises rely on a mix of ecosystems -- cloud providers, software platforms and AI models -- that must work in concert. The goal is to educate AI with real process context so it can deliver value that aligns with business priorities, Kuhn emphasized. "We see that our technology is very complementary to other technology, like the copilots," he said. "We don't have our own AI, but we are complementary. We help AI understand what it's all about." As automation becomes embedded across workflows, process intelligence is redefining what "smart operations" really mean. It's not about replacing humans but enhancing them through orchestrated systems that combine data, decision and action. That synergy is where transformation -- and true ROI -- begins. "You need something, you need treatment, something needs to happen, so action needs to happen," Kuhn said. "Insights is what we generate with process intelligence. Decisions is what we enable with AI. Action is what we perform with automation and orchestration." Here's the complete video interview, part of SiliconANGLE's and theCUBE's coverage of Celosphere 25:

Celonis feeds AI agents with process intelligence data to enhance their operational context - SiliconANGLE

Celonis feeds AI agents with process intelligence data to enhance their operational context Celonis SE is stepping up its efforts to help organizations reshape their business operations with agentic artificial intelligence, announcing today at its annual user conference Celosphere 2025 new capabilities within its namesake Process Intelligence Platform that position it as a foundation for AI-driven operations. Celonis, which has offices in Munich and New York City, is the creator of a process mining platform that helps companies to identify opportunities to increase their operational efficiency. Its platform can be used to detect if a retailer has more of a certain item than is necessary to meet customer demand, for example, while a shipping company could use it to predict and mitigate delayed deliveries. The Celonis Process Intelligence Platform works by collecting and analyzing massive amounts of operational data from business applications, devices and systems. It supports more than 1,000 connectors, which reduces the need for teams to create custom code to link those apps to its platform. Once the data is collected, Celonis enriches it with business context to create a digital twin of an enterprise's business operations. This is called the Process Intelligence Graph, which enables organizations to analyze, design and operate AI agents and other autonomous processes. With today's update, Celonis is expanding the Process Intelligence Graph to support additional data types from newer data sources. Celonis Data Core is a new offering that enables companies to integrate data lakes such as Databricks and the Azure Data Lake with a simple, zero-copy and bi-directional connection. The integration with Databricks is significant, because it means live data stored on that platform can be used to create more comprehensive digital twins of business operations. Customers will be able to feed intelligence from the Celonis platform back into Databricks' Agent Bricks platform and create production-grade AI agents that have been optimized with their own operational data. To support the creation of more capable AI agents, Celonis has also added new capabilities for connecting desktop actions such as keystrokes, mouse clicks and screen scrolls into business processes that support enhanced task mining and AI-driven task discovery. In addition, enterprises will be able to integrate their architectural blueprints to enable AI agents to understand which systems are used for which activities, so they can use the appropriate tool for each task they're asked to automate. All this is being done to support more sophisticated and composable AI-driven operations, Celonis said. To enable these operations, the company has introduced new object-centric process mining tools to help identify problems at key process intersection points, where problems often occur. This will ensure the smooth transition of data across business operations, such as the transport, storage, packing and shipping of final products. Meanwhile, the company is extending the capabilities of the Celonis Orchestration Engine so it can support AI agents as well as people and systems. It works by converting operational insights into automated task and workflow execution. Lastly, Celonis said it's launching the industry's first Model Context Protocol server designed specifically for process intelligence, which is essential for feeding AI agents with the dynamic operational context they need to make appropriate decisions and complete their work effectively. Holger Mueller of Constellation Research said process intelligence can be key to ensuring that AI works as intended, which is something that has challenged many enterprises until now. He explained that their reliance on their own data causes all sorts of problems for AI agents especially, because the quality of this information is often questionable, which means their agentic outputs reflect those inaccuracies. "Celonis provides a promising solution to this by carefully analyzing each organization's business processes through a loss-free approach that utilizes its latest innovations in object-centric process modelling," the analyst said. "Its process intelligence graph is a valid and workable alternative to traditional retrieval-augmented generation." In order to showcase what its platform is now capable of, Celonis has worked with several partners to develop a series of "composable" AI agents that can quickly be customized and adopted by customers. These include Rollio Inc.'s new Process Collaboration Agent, which is designed to automate information technology service management, procurement and other common enterprise processes. It's said to resolve process exceptions instantly by bringing the right people and context from the Celonis platform. Meanwhile, Trullion Inc.'s new agent helps to automate the complex, manual work needed for lease accounting tasks. Bloomfilter Inc.'s new Agent Miner app is designed to understand and govern the behavior of AI coding agents. Celonis said a number of early adopters have deployed AI agents that leverage its process intelligence and insights to improve their operations. Mercedes-Benz Group AG has implemented AI agents to accelerate decision making and improve its delivery times, while Vinmar International Ltd. claims to have transformed its cash-to-order process into a fully automated operation. Celonis Chief Product Officer Daniel Brown said enterprises need to adopt a more structured approach to get agentic AI to work at the level of accuracy and reliability that's needed. He said this means companies must first identify the right use cases, then redesign their business processes in such a way that facilitates AI automation. "[The last step is to] orchestrate the agents alongside your people and existing systems," Brown explained. "Our enhanced capabilities empower customers and their partners to build AI solutions that lift their operations to unprecedented levels of efficiency and agility."