Celonis and Ikigai Partner to Build Trusted AI Agents

Businesses deploying AI across their operations face a common challenge. AI systems lack complete visibility into complex workflows, creating gaps that could undermine enterprise investment returns.

Celonis has launched the Celonis Context Model to address this issue. The new technology layer provides AI systems with real-time operational understanding of business processes.

The company also announced plans to acquire Ikigai Labs. The AI-powered decision intelligence firm brings forecasting, planning and simulation capabilities to the Celonis platform.

Understanding operational context

"AI is only as good as the context it has," says Carsten Thoma, Celonis's President. "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."

According to Celonis, enterprise AI systems often lack the operational context needed to interpret business processes correctly. This gap prevents organisations from achieving expected returns on AI investments.

"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," Carsten adds. The acquisition expands Celonis' ability to support strategic planning and business optimisation.

The Context Model creates a dynamic digital twin of enterprise operations. This digital twin unifies process data and business knowledge from across systems, translating it into a structured model that AI agents can use to make decisions.

Enabling trusted AI deployment

The Celonis Context Model aims to improve AI agent reliability in enterprise environments. Without deep domain and decision logic, AI systems could produce inconsistent or unreliable outputs.

Celonis positions operational context as the component that could enable AI agents to transition from experimental tools to trusted digital workers. By grounding AI within operational context, the technology moves from isolated insights to coordinated action across the enterprise.

This approach could enable AI deployment at scale. Large organisations with complex global operations require consistency, governance and precision to ensure safe AI implementation.

The Context Model functions as a foundational layer in the enterprise technology stack. It sits between raw data systems and AI execution platforms, connecting operational data, business rules and decision logic into a unified structure.

Integration with enterprise systems

The Celonis Platform integrates with major ecosystems. These include:

• AWS
• Databricks
• Microsoft Fabric
• Oracle
• leading CRM platforms.

The platform also connects with AI agent frameworks including Amazon Bedrock, IBM watsonx Orchestrate and Microsoft Copilot. This ensures the Context Model works across different environments.

The acquisition of Ikigai Labs extends Celonis' AI capabilities. Ikigai's technology, built on nearly two decades of MIT research, specialises in structured data modelling, forecasting and large-scale simulation.

Advancing decision intelligence capabilities

"Ikigai Labs was built on a simple but firm conviction: better enterprise decisions require AI that works with enterprise data," says Devavrat Shah, Ikigai Labs Co-founder, Chaired Professor of AI at MIT and Chief Scientist of 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."

Ikigai's capabilities could help enterprises shorten planning cycles and predict operational outcomes with higher accuracy. When integrated with the Context Model, the technology could enable organisations to move from reactive analysis to proactive decision-making.

By modelling future scenarios, businesses could anticipate disruptions and optimise processes before issues materialise. This capability could support more effective growth strategies and resource allocation.

Celonis can now support development of AI agents grounded in both operational history and predictive intelligence. This dual capability could enable AI systems to understand not just what happens in a business, but what might happen next.

"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," Devavrat says. The technology could help organisations make more informed decisions about process optimisation, capacity planning and market expansion strategies.