Celonis acquires Ikigai Labs to boost enterprise AI

Celonis has agreed to acquire Ikigai Labs, adding decision intelligence technology to its Context Model.

The acquisition will bring Ikigai Labs's planning, simulation and forecasting tools into the Celonis platform as companies look to make artificial intelligence systems more useful in day-to-day operations. Celonis argues that AI systems need a clearer view of how businesses actually function if they are to support decisions reliably.

Founded in 2019, Ikigai Labs draws on research from the Massachusetts Institute of Technology. The agreement also gives Celonis exclusive rights to patents owned by MIT and licensed to Ikigai Labs, while MIT will take a shareholding in Celonis.

Context model

The transaction centres on Celonis's Context Model, described as a real-time operational model built from process data and business information gathered across corporate systems, applications, devices and interactions. The aim is to give AI systems a representation of how work happens inside an organisation, rather than relying only on static rules or isolated datasets.

That matters because many large companies are trying to deploy AI agents in areas such as customer service, planning, procurement and supply chain, but often struggle to connect those systems to the practical detail of internal processes. Celonis is positioning the addition of Ikigai Labs as a way to extend that model from describing current operations to supporting prediction and simulation.

"AI is only as good as the context it has. 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 the Celonis Context Model," said Carsten Thoma, president of Celonis.

"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 run tomorrow. This is what every enterprise needs to make AI work and deliver meaningful returns," added Thoma.

Ikigai Labs co-founder Devavrat Shah will become Chief Scientist for Enterprise AI at Celonis. Shah is also a chaired professor of AI at MIT.

"Ikigai Labs was built on a simple but firm conviction: better enterprise decisions require AI that works with enterprise data," said Shah.

"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.

"With the Celonis Context Model, AI agents have the hindsight, insight and foresight to adapt intelligently 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," added Shah.

Platform links

Celonis is also trying to position its software between data infrastructure providers and AI agent platforms. Its system connects to data sources including AWS, Databricks and Microsoft Fabric through zero-copy integrations, with Snowflake to follow, and links to enterprise systems such as Oracle and other ERP and CRM platforms.

It has also built integrations with a range of AI and orchestration tools, including Amazon Bedrock, Anthropic Claude, Databricks Agent Bricks, IBM watsonx Orchestrate, Microsoft Copilot and Oracle OCI Enterprise AI. The strategy suggests Celonis wants its Context Model to act as a common layer that AI applications can draw on, regardless of which model or software stack a customer uses.

Heather Akuiyibo, Global Vice-President of GTM Integration at Databricks, described the challenge facing corporate AI roll-outs in operational settings.

"Enterprise AI faces a reliability gap because scale isn't enough; agents need a deep understanding of how a business actually runs," she said.

"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 operationalise AI with Agent Bricks. They can do this with the Celonis business context required to make better decisions, faster," added Akuiyibo.

Customer focus

Several users and investors pointed to the same issue: AI systems can produce plausible outputs, but business adoption depends on whether those outputs reflect the reality of internal operations.

"Precision is paramount in the healthcare industry, and you can't accept AI that's only right most of the time," said Jerome Revish, Senior Vice-President and Chief Technology Officer for 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," added Revish.

Rafael Domene, CIO of Cosentino, linked that point to the use of AI agents in business operations.

"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 Domene.

"When you provide AI with a real understanding of your processes, such as the data, business rules and 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," added Domene.

For Celonis, the purchase broadens a pitch that began in process mining and has now moved into AI oversight, orchestration and operational modelling. Ikigai Labs has already worked with large enterprises to cut planning and forecasting cycles, including in supply chains, from months to minutes.

"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 a strong initial focus on improving our end-to-end 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 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 is the assurance that AI investments generate real value rather than add another layer of complexity," added Catalano.