Celonis Acquires Ikigai Labs to Solve Enterprise AI's Operational Context Problem

Celonis Buys Igikai Labs To Provide The Context Engine For Enterprise-Wide AI Adoption

Celonis just announced its intention to acquire Ikigai Labs, a San Francisco-based start-up focusing on AI-powered decision intelligence. Igikai Labs specializes in software for complex forecasting scenarios based on Large Graphical Models, working closely also with the Massachusetts Institute of Technology (MIT). This acquisition will bring together Celonis' process intelligence graph technology with Ikigai labs' ability AI decision intelligence. For Celonis this has significant ramifications as through this acquisition it will gain exclusive rights to use MIT's patents, which Ikigai Labs had licensed from the MIT. Also, as part of this deal the MIT will become a Celonis shareholder. But what does this mean for Celonis clients? * Complex scenario planning becomes seamless and fast. In the currently volatile economic environment, scenario planning has become an essential capability to keep up operations and to take the right tactical and strategic decisions fast. However, hardly any company is equipped to leverage scenario planning today as they are missing relevant and comprehensive data and in-depth insights into their operating models. Celonis and Ikigai Labs technologies combined can help companies predict what is likely to happen, simulate what-if scenarios, and recommend what should be done. This will enable clients to overcome operational silos and act with relevance - based on process insights, powerful analytics, and enterprise context for accurate and relevant AI agentic outcomes. * Process intelligence becomes an AI adoption enabler. For most companies the key stumbling block for enterprise-wide AI adoption are internal silos and the need to embed AI into your operating model. This requires a deep understanding of how you operate. Process intelligence data can provide exactly this understanding. Paired with generative AI, process intelligence data provides the context AI needs to deliver reliable, relevant, and repeatable outcomes that are aligned with your new operating model. Celonis' newly introduced Context Model is aimed to do exactly that and Ikigai Labs will make it even more powerful with ad-hoc multi-dimensional forecasting and recommendations. The bottom line: With Celonis' focus on enterprise-specific context models, process intelligence is shifting from hindsight dashboards and ex-post analytics to infrastructure for the agentic era. But beyond the technological fit, there is also a good cultural fit since both companies have a very strong academic heritage and scientific aspiration. This is essential to keep talent inhouse for continued innovation leadership. Want to know more? Feel free to schedule an inquiry with me.

Celonis turns its platform on its head with Context Model and Ikigai Labs deal - pitching itself as enterprise AI's 'voice of reason'

Celonis, the process intelligence vendor, today announced the launch of the Celonis Context Model (CCM) and a definitive agreement to acquire Ikigai Labs, an MIT-founded AI decision intelligence company. The deal brings Ikigai's Large Graphical Model technology - patented MIT research focused on tabular and time-series data, planning, simulation, and forecasting - into the Celonis platform. As part of the agreement, MIT will become a shareholder in Celonis, and Ikigai co-founder Devavrat Shah will join as Chief Scientist, Enterprise AI. The headline news is of course the launch of the Context Model itself, which Celonis is positioning as a new layer in the enterprise technology stack. The vendor is arguing that AI agents and automation cannot deliver value unless they understand how a business actually runs - and that the operational ontology Celonis has built provides the only neutral, system-agnostic context for them to operate against. However, what's also interesting - and what Carsten Thoma, President of Celonis, spent most of our briefing yesterday talking through - is what this represents internally at the vendor. In his words, Celonis has decided to turn its own platform around. I've been covering Celonis for years and have written extensively about its process intelligence positioning, its 'free the process' argument, and most recently its defence and security offer. The argument that operational context is the missing ingredient for enterprise AI is not new from this company - however, Celonis has changed how it thinks about the platform internally and how it plans to deliver on this context for buyers. Thoma explained that the company has used the past two years to take everything it has built - process mining, object-centric mining, the Process Intelligence Graph, KPIs, the orchestration engine - and recompose it under a full operational ontology. Process mining, which used to be the product, is now one function consuming the ontology. As he noted, there are hundreds of others. He said: We need to turn our own platform around. We're not doing anything different in terms of what we talk about, necessarily, but we are doing something very different when you look at the output. We come from process mining, as you know. We managed to abstract this to object-centric mining, which allows you an end-to-end view across all systems. We added the platform and then partially the KPIs. But now we really have a full operational ontology...we were able to take what we've done so far and mould the objects into the flow - not only into the object view, but actually combining the business context and the objects together. Thoma argued that the trigger for this was a recognition - two years ago - that the foundations enterprises had been relying on to describe how their businesses work were no longer fit for purpose. He added: That was so important for us, because we always knew - when we saw everything that was coming two years ago - that legacy documentation is obsolete. You need to have a way, in the agentic frameworks and also with the other applications, to sit at the intersection of the applications, the data, the ontology, and the agentic frameworks. You need something that is able to orchestrate it. Hands down - where we come from and how we built it - we are, for sure, and in particular with this edition, the only solution that can provide that. The practical implication for buyers is that the ontology can now feed anything downstream. If a customer wants analytics, process mining draws on the ontology. If they want to instruct an agent, the ontology provides what Thoma described as "the how, the why, and how good" - giving the agent a single place to understand what good looks like. If they want a human workflow, the same context applies. It's a meaningful architectural shift and the thing that used to be Celonis is now a feature consuming what Celonis has rebuilt as its core asset. The Ikigai acquisition extends and Thoma argued that the operational ontology gives customers the hindsight and insight - what is happening, why it's happening - but the missing piece is the "what if". Ikigai's Large Graphical Model technology is built for simulation, scenario planning, and forecasting against structured and time-series data. Thoma said: With the Ikigai acquisition, we've now added the 'what if'. So it's not only the how and why and how good - it's also the 'what if'. Because now you actually have an orchestration question: what do you want to do afterwards? Is it analytics and you instruct someone? Is it a workflow? Is it an agent? Is it a different type of automation? Or is it recomposability? As I've noted previously, a lot of vendor messaging around context still operates at the level of slogans - 'context for AI', 'understanding how your business runs', 'grounding agents in reality'. The Celonis pitch, with Ikigai bolted on, is doing something more specific - it allows the platform to simulate process redesigns and outcomes before any change is committed. Thoma also referenced the broader concept of a "world model" for enterprise operational data - a phrase that travels well in current AI discourse, though customers will want to see this translate into deployment evidence before taking it at face value. And of course, Celonis isn't the only vendor in the market talking about enterprise context for AI. Plenty of vendors are throwing their hat in the ring - SAP, ServiceNow, Google, Databricks, Microsoft, Oracle. So, what makes this different? Thoma's answer is that most context offerings are about storing and exposing data that already exists, often in unstructured form. Celonis, he argues, is doing the harder thing - generating context from operational data that doesn't exist in a consumable form in the first place. He said: There are a lot of context models out there. But it's a different type of data. We are not claiming we want to deal with large volumes of historic, unstructured data - that's exactly what makes the difference. We are domain agnostic, system agnostic, very much focused on operational data - process and productivity - that you have to generate, because the data doesn't exist in a form you can consume... It's not like storing your data, because that's self-explanatory. The difference is: domain agnostic, system agnostic, full control, in and out - it's not a one-sided context model. Thoma pointed to a recent customer benchmark in the HR domain - notably an area Celonis doesn't consider core - as evidence. He said: The customer literally wrote us an email saying that in the benchmark, the data context and understanding on domain data for HR that Celonis provided was, by a magnitude, better than the other vendor's own understanding. And why? Because we know the dependencies. You can qualify the data better if you know the dependencies - when it leaves your system - and the others don't. The CIOs we at diginomica are hearing from in our network are increasingly cautious about vendor claims as it relates to AI and AI ROI specifically. And of course there is lots of chatter in the market about the so-called 'SaaSpocalypse' and the role of SaaS platforms in the future. Commenting on what buyers actually need to move forward in this moment, Thoma said: It's not all about the frontier models. I had a CIO summit two weeks ago in Tuscany with twelve leaders from very large global companies. Not one of them is planning a broad frontier model AI rollout in their company in the next three years. Not one. So what do they need? They need someone who is reasonable. Here's your context. Here's how your business runs. Here's why it's running badly. Here's what you can do. And here are your options. One option might be a model. One option might be an automation framework. One option might be agents from ServiceNow. One option might be recomposability. That's exactly what you need. I suspect this sort of framing will land well with buyers - not a single sweeping rollout, but a sequence of judgement calls about what to redesign, what to recompose, what to automate, and what to leave alone. Not a sale that's claiming 'we will fix everything for you' - but a platform that helps you to make a variety of different decisions, across a variety of different scenarios, where different tooling and people will be needed. Thoma extended the point to the broader market context: Where do you go? You need a trusted platform that can show you, with deep intelligence, your operational reality - the simulation. So you can see the hindsight, the current state, the improvements, and the foresight. Absolutely agnostic. That doesn't charge crazy amounts. In our platform replacements, we probably charge 20 per cent of what the customer was paying before... That is what the world needs right now: a neutral, transparent, trusted context model. And that's what we can provide. And this is where Thoma describes Celonis as "the voice of reason here" - a middle layer that is agnostic, intelligent, and trusted. What's interesting about this announcement is how grounded it feels relative to a lot of enterprise AI vendor messaging. There is no claim that AI fixes everything and the pitch is closer to the opposite: that most AI deployments fail because organizations don't have a clear, neutral view of their own operational reality, and that without one, the agents and automations on top will not deliver. The Ikigai acquisition adds the ability to simulate alternatives - to model what could happen before committing to change. Whether the architectural pivot delivers in practice will come down to execution. Celonis is making a substantial bet that the enterprise stack is consolidating around three layers - data, context, and agentic execution - and that it can own the middle. The integrations announced today with Databricks, Microsoft Fabric, AWS, Snowflake (coming), Amazon Bedrock, Anthropic's Claude Cowork, IBM watsonx Orchestrate, Microsoft Copilot and Agent365, and Oracle OCI Enterprise AI suggest the company is taking that seriously, by being consumable from both sides. There is much to test as customers adopt this. But after watching this vendor for some time, the argument is likely a compelling proposition for many buyers right now. In a market full of vague context claims and aggressive lock-in plays, "operational truth, neutrally provided, with simulation on top" is at least a tangible thing to evaluate.

Celonis buys decision-intelligence startup Ikigai Labs to provide operational context for enterprise AI - SiliconANGLE

Celonis buys decision-intelligence startup Ikigai Labs to provide operational context for enterprise AI Process mining software company Celonis SE said today it has snapped up the Massachusetts Institute of Technology-linked decision intelligence startup Ikigai Labs Inc. The startup's technology will power a new "context model" developed by Celonis that's designed to function as a real-time digital twin of its customer's business operations. Celonis says enterprises are facing a critical challenge in their efforts to successfully deploy artificial intelligence agents at scale. There's an urgent need to ensure AI doesn't have any "blind spots" in their understanding of how businesses operate, as any deficiencies would almost certainly undermine their potential. The new Context Model is meant to eliminate these blind spots by providing a "dynamic, real-time digital twin of operations," effectively translating all business processes into a language that AI can understand. It's built on process data and business knowledge derived from every application, system, device and interaction across an organization, and is designed to provide the "operational clarity" AI needs to reason correctly, the company added. Celonis Chief Product Officer Dan Brown described the problem in more detail in a blog post, explaining that AI models don't have any idea of how specific invoices are related to an organization's shipping records, because the data is usually proprietary, kept private and fragmented across multiple systems and apps. "But without that deterministic foundation - the ground truth of your operational reality - no AI agent can be trusted to make reliable real-time decisions and take actions that effectively drive your business outcomes," he said. Founded in 2019, Ikigai is led by its Chief Executive and Chief Technology Officer Devavrat Shah, who also holds a professorial chair of AI at MIT. It specializes in processing and analysing structured data, and sells a generative AI platform based on "large graphical models" that help AI systems to understand the nuances of proprietary enterprise data. Celonis President Carsten Thoma told Computer Weekly in an interview that his company has been working on developing the Context Model for over two years. The goal was to create a "holistic business graph" that serves as the brains of a company's AI operations. "We knew from our own insights that application landscapes are super-fragmented, that data lakes are competing, that AI is on the horizon, but that some of the AI is very hard to deploy in an efficient manner," he said. Celonis considered what enterprises might need to overcome these problems and realized that it already had a key piece of the puzzle with its flagship process intelligence platform, Thoma said. But some of the pieces were missing from its stack - notably the large graphical model that it's acquiring now with Ikigai. "AI is only as good as the context it has," Thoma said. "Every organisation needs to give its enterprise AI a holistic, living model of how a business truly operates. This has never been possible until now" With Ikigai, Celonis can offer its customers a "control tower and platform for operational context and intelligence," Thoma said. "It is important to understand we are domain agnostic and system agnostic from the operational context model [point of view] because other vendors talk about specific domains." Shah, who is now the Chief Scientist for Enterprise AI at Celonis, said AI needs to be able to understand the peculiarities of enterprise data. "Ikigai has proven foundation model technology for structured data at scale; Celonis has encoded enterprise processes," he explained. "Together, we provide the fullest operational representation of business reality." To emphasize its point, Celonis rolled out a number of early adopters of the Context Model, including the healthcare services firm Cardinal Health LLC. That company's CTO, Jerome Revish, explained that the industry simply cannot accept AI systems that are "only right most of the time." "Precision is paramount. We use AI as a tool to accelerate operational insight - process context enables agents to support our team in acting with precision," he added. "Defining guardrails then gives us the confidence to act. Ultimately, context is what makes the difference between AI that's impressive in a demo and AI that's trusted and safe to deploy." Celonis customers will benefit from the platform's zero-copy integrations with public clouds like Amazon Web Services, as well as data lakes such as Databricks and Microsoft Fabric. It also offers connectors to Oracle Corp.'s database and other enterprise platforms. In addition, it features integrations with agentic development platforms including Amazon Bedrock, Anthropic PBC's Claude Cowork, IBM Watsonx Orchestrate, Microsoft Copilot and Agent365 and Oracle Cloud Infrastructure Enterprise AI. Celonis isn't alone in the business process mining and process intelligence field, with its main rivals including SAP SE's Signavio, IBM Process Mining and UiPath Inc. But Ashu Garg of Foundation Capital, an early investor in Ikigai, said that Celonis now has a big advantage over those rivals. "This is our context graph thesis made real. Celonis has built the deepest operational understanding of how enterprises actually function - as a live, process-native model of how work happens, why it breaks and what should happen next," he explained. "With the acquisition of Ikigai Labs, they've added the decision intelligence and simulation capabilities that make it truly effective."

Celonis Launches Context Model and Acquires Ikigai Labs to Solve AI's "Blind Spot" Problem

Celonis launched the Celonis Context Model (CCM) and announced it has signed a definitive agreement to acquire Ikigai Labs, a leader in AI-powered Decision Intelligence. As organizations around the world attempt to deploy Enterprise AI, they face a critical challenge: ensuring AI does not have blind spots in understanding how the business operates. Without this understanding, AI agents cannot make a real impact, so companies struggle to see meaningful returns on their Enterprise AI investments. The CCM fixes this by providing a dynamic, real-time digital twin of operations, which translates the business into a language AI understands. Built on process data and business knowledge from every system, application, device, and interaction across the business, the CCM gives Enterprise AI the operational clarity it needs to reason correctly, act reliably, and deliver results at scale. The acquisition of Ikigai Labs will bring state-of-the-art enterprise Decision Intelligence and cutting-edge AI innovation -- which includes planning, simulation, and forecasting capabilities -- to the CCM, enabling organizations to model future-state scenarios, predict and prevent process breakdowns, and make sensible, reliable decisions. The Operational Context Imperative With the introduction of the CCM,Celonis is defining a new critical layer in the enterprise technology stack -- the context layer. This layer unifies process data, business knowledge, operational and decision intelligence to ground Enterprise AI in reality and power its effective execution -- continuously evolving as it learns from actions and outcomes across the business. "AI is only as good as the context it has. Every organization needs to give its Enterprise AI a holistic, living model of how a business truly operates. This has never been possible until now, with the Celonis Context Model," said Carsten Thoma, Celonis President. "And with Ikigai Labs, we're making our market-leading platform even stronger: extending its intelligence beyond how your business runs today to how it should -- and could -- run tomorrow. This is what every enterprise needs to make AI work and deliver meaningful returns." "Precision is paramount in the healthcare industry, and you can't accept AI that's only right most of the time," said Jerome Revish, SVP/Chief Technology Officer, Digital and Technology Services, Cardinal Health. "We use AI as a tool to accelerate operational insight -- process context enables agents to support our team in acting with precision. Defining guardrails then gives us the confidence to act. Ultimately, context is what makes the difference between AI that's impressive in a demo and AI that's trusted and safe to deploy." "Our goal at Cosentino is to build a digital workforce of AI agents that can run and improve our business operations at scale. What we've learned is that an agent is only as good as the context you give it," said Rafael Domene, CIO, Cosentino. "When you provide AI with a real understanding of your processes -- the data, the business rules, the decision logic -- it stops being a tool you experiment with and becomes one you trust to act. That's what makes the difference between an agent that makes a recommendation and one that runs a process." "At Mondelez International, we're in the middle of one of the most consequential technology transformations in our history while simultaneously building the foundation for agentic AI, with strong initial focus on improving our E2E flows and global shared services," said Filippo Catalano, Chief Information and Digital Officer, Mondelez International. "We've learned you cannot sustainably deploy and run trusted AI agents across a landscape as complex and varied as ours, unless those agents understand and act based on the reality of how your processes run across every market, system, and function - not just how they were designed in theory. Operational context isn't a nice-to-have; it's the assurance for AI investments generating real value versus adding another layer of complexity." AI Agents You Can Trust The acquisition will unite IkigaiLabs's world-class talent -- with deep expertise in AI, machine learning, tabular and time-series modeling, causal inference, and large-scale simulation -- with the global Celonis team. Ikigai Labs was founded on nearly two decades of groundbreaking MIT research, and their experts have worked with some of the world's most complex enterprises to reduce planning and forecasting cycles in areas like supply chain from months to minutes. As part of the agreement, Celonis will gain exclusive rights to MIT-owned patents, which Ikigai Labs had licensed from MIT, and MIT will become a shareholder in Celonis. "Ikigai Labs was built on a simple but firm conviction: better enterprise decisions require AI that works with enterprise data. Ikigai Labs has proven foundation model technology for structured data at scale; Celonis has encoded enterprise processes. Together, we provide the fullest operational representation of business reality," said Devavrat Shah, Ikigai Labs co-Founder, Chaired Professor of AI at MIT, and Chief Scientist, Enterprise AI at Celonis. "With the Celonis Context Model, AI agents have the hindsight, insight and foresight to intelligently adapt -- and can be trusted to deliver the expected business outcomes. I am excited to continue our mission with Alex, Basti, Carsten, Martin and the entire Celonis team." The Context Model Powers the Trusted Platform to Industrialize Enterprise AI The Celonis Platform and ecosystem provide end-to-end capabilities to analyze, design, and operate AI-driven processes and drive business transformation. The Platform enables customers to not just give AI the context it needs, but also to identify the best opportunities to deploy AI strategically, and to orchestrate agents, humans, and systems to work together. Celonis has partnered with the leaders in both the underlying data layer and the agentic execution layer to build this new context layer that bridges the two. The Celonis Platform brings data together from across the enterprise with zero-copy integrations to sources like AWS, Databricks and Microsoft Fabric (with Snowflake to be available soon), as well as pre-built connectors to systems of record like Oracle and other leading ERP and CRM platforms. Celonis has also built deep integrations with the leading agentic platforms -- including Amazon Bedrock, Anthropic's Claude Cowork, DatabricksAgent Bricks, IBM watsonx Orchestrate, Microsoft Copilot and Agent365, and Oracle OCI Enterprise AI -- ensuring that, however customers are building agents, the Context Model is accessible and consumable by them. "Enterprise AI faces a reliability gap because scale isn't enough; agents need a deep understanding of how a business actually runs," said Heather Akuiyibo, Global VP, GTM Integration, Databricks. "By combining Celonis with the Databricks platform, companies can enable their employees to chat with their data and get trusted answers instantly with Genie and build, govern, and operationalize AI with Agent Bricks. And they can do this all with the Celonis business context required to make better decisions, faster." The Future of the Enterprise is AI-Driven and Composable Celonis views the Context Model as an important step in the journey to the AI-driven,composable enterprise. In this future operating model, organizations' systems, data, processes, people, and AI agents work together with shared context, allowing them to improve continuously, adapt instantly, and innovate freely. "Celonis already sits at the operational core of thousands of the world's largest enterprises, capturing how work actually happens at unprecedented depth," said Sandesh Patnam, Managing Partner, Premji Invest. "Layering IkigaiLabs's simulation and decision intelligence on that foundation creates a flywheel where every operational signal becomes a sharper decision and every decision sharpens the operational model - a moat competitors will struggle to replicate." "This is our context graph thesis made real. Celonis has built the deepest operational understanding of how enterprises actually function -- as a live, process-native model of how work happens, why it breaks, and what should happen next," said Ashu Garg, General Partner, Foundation Capital. "With the acquisition of Ikigai Labs, they've added the decision intelligence and simulation capabilities that make it truly effective. The companies that control this layer will define the next era of enterprise software. Celonis is that company."