Celonis launches context model in Ikigai Labs deal

Celonis has launched the Celonis Context Model and signed an agreement to acquire Ikigai Labs, adding decision intelligence technology to its process intelligence platform.

The new model is intended to give artificial intelligence systems a live view of how a business operates by drawing on process data and business rules from systems, applications, devices and interactions across an organisation. Celonis said this should help AI systems avoid gaps in understanding that can limit their use in day-to-day operations.

The acquisition would bring Ikigai Labs' work in planning, simulation and forecasting into the platform. Celonis said the combination would allow customers to model future operating scenarios, anticipate process failures and support decisions with a broader operational picture.

Context layer

At the centre of the launch is what Celonis describes as a context layer for enterprise technology. It combines process data, business knowledge, operational intelligence and decision intelligence in a form AI systems can use while interacting with business processes.

Carsten Thoma, President of Celonis, said the model addresses a longstanding problem in business AI deployments. "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.

He said the Ikigai deal is part of a wider effort to extend the platform beyond monitoring current processes.

"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," he said.

Celonis is seeking to position itself between the corporate data layer and the software used to build and run AI agents. Its platform connects to data sources including AWS, Databricks and Microsoft Fabric through zero-copy integrations, with Snowflake to follow, and also links to systems of record such as Oracle and other ERP and CRM platforms.

It also highlighted integrations with agent-building and orchestration products from Amazon, Anthropic, Databricks, IBM, Microsoft and Oracle. The aim is to make the context model available regardless of the technology stack a customer uses to build AI agents.

Customer view

Several customers and partners pointed to the importance of process context in regulated and complex operating environments.

"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."

Cosentino drew a similar distinction between experimentation and operational use.

"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."

Mondelez International also described operational context as a requirement for wider deployment across large, varied businesses.

"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."

Ikigai deal

Ikigai Labs was founded on research from the Massachusetts Institute of Technology and focuses on AI models for structured enterprise data. The transaction would add expertise in machine learning, time-series modelling, causal inference and simulation, alongside exclusive rights to MIT-owned patents previously licensed to Ikigai Labs. MIT will become a shareholder in Celonis as part of the agreement.

"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," Shah said.

Databricks, one of Celonis's integration partners, said the combination is aimed at reducing reliability problems in enterprise AI projects.

"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."

Celonis said the acquisition of Ikigai Labs is expected to close imminently, subject to standard closing procedures.