Celonis to acquire AI decision intelligence leader Ikigai Labs to boost its new Context Model
Process Intelligence powerhouse Celonis has announced it will acquire Artificial Intelligence (AI) decision intelligence leader Ikigai Labs to increase its ability to enable enterprisers to more actively predict, simulate, and optimize decisions.
Celonis will add Ikigai Labs’s advanced planning, simulation and forecasting capabilities to supercharge it “context model”, a new critical layer in the enterprise technology stack.
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 Celonis Context Model (CCM) addresses 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.
Celonis President Carsten Thoma said, “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.
“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.”
Ikigai Labs was founded in 2019 on nearly two decades of groundbreaking Massachusetts Institute of Technology research and have worked with some of the world’s most complex enterprises to reduce planning and forecasting cycles in areas such as 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,” said Devavrat Shah, Ikigai Labs Co-Founder, Chaired Professor of AI at MIT, and now the newly appointed Chief Scientist, Enterprise AI at Celonis.
“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 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 such as AWS, Databricks and Microsoft Fabric (with Snowflake to be available soon), as well as pre-built connectors to systems of record such as 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, Databricks Agenct Bricks, IBM watsonx Orchestrate, Microsoft Copilot and Agenct365 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 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.”
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.
Sandesh Patnam, Managing Partner, Premji Invest, said, “Celonis already sits at the operational core of thousands of the world’s largest enterprises, capturing how work actually happens at unprecedented depth. Layering Ikigai Labs’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.”
Ashu Garg, General Partner, Foundation Capital, said, “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. 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.”
Jerome Revish, SVP/Chief Technology Officer, Digital and Technology Services, Cardinal Health, said, “Precision is paramount in the healthcare industry, and you can’t accept AI that’s only right most of the time.
“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.”
Rafael Domene, CIO, Cosentino, said, “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.
“When you provide AI with a real understanding of your processes such as 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.”
Filippo Catalano, Chief Information and Digital Officer, Mondelez International, said, “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.
“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.”
