Celonis has launched the Celonis Context Model and announced an agreement to acquire Ikigai Labs, a move the company says is designed to help enterprises make AI more reliable by giving it a deeper understanding of how business operations actually work.
The announcements reflect a growing challenge in enterprise AI adoption. Many organizations are investing heavily in AI agents and automation, but still struggle to translate those investments into measurable operational outcomes. Celonis argues that the issue is not only model capability, but context: AI systems often lack a real-time understanding of processes, business rules, operational dependencies and decision logic.
With the Celonis Context Model, the company is positioning process intelligence as a foundational layer for enterprise AI. The model is designed to act as a dynamic, real-time digital twin of operations, translating business activity into a form that AI systems can understand and use to reason, act and improve outcomes.
A New Context Layer for Enterprise AI
The Celonis Context Model is being introduced as what the company describes as a new “context layer” in the enterprise technology stack. Built on process data and business knowledge from systems, applications, devices and interactions across the business, the model is intended to provide AI with operational clarity.
This is significant because AI agents need more than access to data. To operate effectively in enterprise environments, they must understand how work moves across systems, where processes break down, which rules apply and what outcomes matter. Without that operational understanding, AI can generate recommendations that may appear useful in isolation but fail to reflect the reality of how the business runs.
Carsten Thoma, President of Celonis, said AI is only as strong as the context it receives, adding that organizations need a “holistic, living model” of how the business truly operates. He said the Celonis Context Model is intended to provide that foundation, while the addition of Ikigai Labs would extend the platform’s intelligence from how a business runs today to how it could run in the future.
Ikigai Labs Adds Decision Intelligence, Forecasting and Simulation
Alongside the launch of the Context Model, Celonis announced that it has signed a definitive agreement to acquire Ikigai Labs, an AI-powered decision intelligence company built on nearly two decades of MIT research. The acquisition is expected to add planning, simulation and forecasting capabilities to the Celonis platform.
These capabilities are important because they extend enterprise AI beyond understanding current operations. By combining Celonis’ process intelligence with Ikigai Labs’ decision intelligence, Celonis aims to help organizations model future scenarios, predict process disruptions and assess possible outcomes before decisions are made.
Ikigai Labs brings expertise in machine learning, tabular and time-series modeling, causal inference and large-scale simulation. As part of the agreement, Celonis will gain exclusive rights to MIT-owned patents that Ikigai Labs had licensed from MIT, and MIT will become a shareholder in Celonis.
Devavrat Shah, co-founder of Ikigai Labs, Chaired Professor of AI at MIT and Chief Scientist, Enterprise AI at Celonis, said the combination brings together Ikigai Labs’ foundation model technology for structured data and Celonis’ encoded understanding of enterprise processes. He described the result as a fuller operational representation of business reality.
From Process Mining to AI-Driven Execution
Celonis has long been associated with process mining and process intelligence. The latest announcements suggest a broader ambition: to become a core operational foundation for AI-driven enterprises.
The Celonis Platform is designed to help organizations analyze, design and operate AI-driven processes. With the Context Model, Celonis aims to connect process data, business knowledge, operational intelligence and decision intelligence so AI agents can work with a more accurate understanding of enterprise reality.
Celonis is also emphasizing ecosystem integration. The platform includes zero-copy integrations with data sources such as AWS, Databricks and Microsoft Fabric, with Snowflake expected to be available soon. It also offers pre-built connectors to enterprise systems including Oracle and other ERP and CRM platforms.
On the agentic AI side, Celonis has built integrations with platforms including Amazon Bedrock, Anthropic’s Claude Cowork, Databricks Agent Bricks, IBM watsonx Orchestrate, Microsoft Copilot and Agent365, and Oracle OCI Enterprise AI. This positions the Context Model as a layer that can be consumed by different AI agents and platforms rather than being limited to a single environment.
Why Operational Context Matters
The enterprise AI market is increasingly focused on agents that can take action, not just generate content or insights. But in complex business environments, action requires a high degree of reliability. AI systems need to understand process variation, exceptions, dependencies and consequences.
This is where Celonis is making its case. The company argues that operational context can give AI hindsight, insight and foresight: the ability to understand why something happened, what is happening now and what is likely to happen next.
Customer perspectives included in the announcement reinforce this point. Cardinal Health, Cosentino and Mondelez International each emphasized the importance of process context, guardrails and operational reality in deploying AI agents safely and effectively across complex environments.
For ERP and enterprise software leaders, this points to an important shift. AI adoption is moving from experimentation toward execution, and execution depends on context-rich systems that understand how work is performed across finance, supply chain, operations and shared services.
Toward the AI-Driven, Composable Enterprise
Celonis describes the Context Model as part of a broader move toward the AI-driven, composable enterprise. In this model, systems, data, processes, people and AI agents work together with shared context, allowing organizations to adapt more quickly and continuously improve operations.
The acquisition of Ikigai Labs, which Celonis expects to close imminently subject to standard closing procedures, strengthens that direction by adding simulation and decision intelligence to the company’s process intelligence foundation.
For enterprises evaluating AI investments, the announcement highlights a central issue: AI performance will increasingly depend on the quality of operational context surrounding it. As companies look to move from isolated AI pilots to scalable deployment, the ability to connect AI with real business processes may become one of the defining factors in enterprise AI success.
ERP News Editorial Team
The ERPNews Editorial Team covers global developments in ERP (Enterprise Resource Planning), enterprise software, cloud platforms, AI, automation, and digital transformation, providing independent news and editorial analysis for senior business and technology leaders. Our reporting focuses on market signals, strategic shifts, and enterprise impact across the ERP and enterprise technology ecosystem.
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