Celonis launches context model in Ikigai Labs deal

Celonis has launched the Celonis Context Model and agreed to acquire Ikigai Labs, adding planning, simulation and forecasting technology to its process intelligence platform.

The new model is designed to give artificial intelligence systems a real-time representation of how business operations run across an organisation. Celonis describes it as a context layer between data sources and AI tools, using process data and business rules from enterprise systems, applications, devices and interactions.

The move reflects a broader push by technology suppliers to address a recurring problem in corporate AI deployments: models may have access to data but still lack a reliable understanding of how work actually flows through a company. Celonis, which has built its business around process intelligence software, argues that this operational picture is essential if AI agents are to make decisions or carry out tasks with fewer errors.

With the addition of Ikigai Labs, Celonis is also moving beyond analysis of current operations into prediction and modelling. Ikigai Labs brings software for planning, forecasting and what-if analysis, which will be integrated into the Context Model so customers can test future scenarios and identify possible process failures before they occur.

The acquisition also brings research ties to the Massachusetts Institute of Technology. As part of the transaction, Celonis will obtain exclusive rights to patents owned by MIT and previously licensed to Ikigai Labs, while MIT will become a shareholder in Celonis.

Carsten Thoma, president of Celonis, framed the launch as a response to weak returns from some enterprise AI projects.

"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," Thoma said. "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."

Customer views

Several Celonis customers said process context was becoming central to how they assess AI use in operational settings. Their comments reflect a common concern in heavily regulated and globally distributed businesses: an AI system may produce plausible answers in testing yet still be unsafe or unreliable in live workflows if it does not reflect business rules and exceptions.

"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 and chief technology officer of digital and technology services at 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."

At Cosentino, the focus is on automating business processes through AI agents.

"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 of 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."

Mondelez International also highlighted the importance of process-level understanding in large multinational environments.

"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 at 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 that AI investments generate real value rather than add another layer of complexity."

Technology stack

The Context Model is intended to work across different data platforms and AI systems rather than tie customers to a single vendor stack. Celonis cited integrations with cloud and data providers including AWS, Databricks, Microsoft Fabric and Oracle, as well as links to agent platforms such as Amazon Bedrock, IBM watsonx Orchestrate, Microsoft Copilot and Oracle OCI Enterprise AI.

That positioning matters in a market where many companies are assembling AI systems from multiple suppliers. Rather than asking customers to replace underlying infrastructure, Celonis is aiming to position itself as a control and interpretation layer for operational data.

Databricks supported that argument in comments released alongside the announcement.

"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 of GTM integration at 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."

Research roots

Ikigai Labs was founded on research developed at MIT and focuses on AI for structured enterprise data, including tabular and time-series information. Celonis said the team has worked with large companies to reduce planning and forecasting cycles, particularly in supply chains.

Devavrat Shah, Ikigai Labs co-founder, MIT chaired professor of AI, and chief scientist for enterprise AI at Celonis, outlined the rationale for combining the two businesses.

"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," Shah said. "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 transaction is expected to close imminently, subject to standard closing procedures.