NEW YORK & MUNICH -- Celonis, the global leader in Process Intelligence, 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.”
