Building Business Context in Enterprise AI with Celonis CCM

Enterprises deploying AI and agents across their workflows face a common challenge. Does the technology understand operations completely or are there gaps that could prove expensive?

To help enterprises reap back the ROI from their AI investments, Celonis has introduced the Celonis Context Model – a new layer custom designed to provide enterprise AI models with operational knowledge of business processes in real time. 

The process intelligence company also announced an agreement to acquire Ikigai Labs.

Ikigai Labs is an AI-powered decision intelligence specialist bringing forecasting, planning and simulation capabilities into the platform.

Operational context for enterprise AI

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

"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 lacking operational context required to interpret business processes correctly could halt businesses from realising returns on their 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.

Building AI agents with trust

The Celonis Context Model aims to improve the trustworthiness of AI agents in enterprise environments.

Without deep domain and decision logic, AI systems risk producing inconsistent or unreliable outputs.

Celonis positions operational context as the element that could allow AI agents to transition from experimental tools to digital workers capable of executing processes.

The solution creates a dynamic digital twin of enterprise operations that unifies process data and business knowledge from across systems and interactions.

It translates information into a structured model that AI agents can use to reason and act with reliability.

By grounding AI within operational context, tools could move from isolated insights to coordinated action across the enterprise. This could translate into AI that can be deployed at scale.

The model could be useful for large organisations with complex global operations, where consistency, governance and precision are essential to ensure safe AI deployment.

Celonis views the Context Model 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.

To make deployment easier, the Celonis Platform easily integrates with systems including AWS, Databricks and Microsoft Fabric. It also connects with enterprise systems such as Oracle and other CRM platforms.

The platform links with AI agent frameworks including Amazon Bedrock, IBM watsonx Orchestrate and Microsoft Copilot. The Context Model is accessible across different environments.

Advanced decision intelligence capabilities

Celonis' AI capabilities gets a considerable boost post the acquisition of Ikigai Labs.

Ikigai's technology is built on nearly two decades of MIT research and specialises in structured data modelling, forecasting and large-scale simulation.

"Ikigai Labs was built on a simple but firm conviction: better enterprise decisions require AI that works with enterprise data," says Devavrat Shah, Co-Founder at Ikigai Labs, 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."

According to the companies, Ikigai's capabilities when integrated with the model could help enterprises shorten planning cycles and predict operational outcomes with higher accuracy.

The technology is designed to help organisations move from reactive analysis to proactive decision making.

By modelling future scenarios, businesses could anticipate disruptions and optimise processes before issues materialise.

Celonis can now support the development of AI agents grounded in operational history and predictive intelligence.

This dual capability is central to the vision of an AI system that understands what is happening in a business and what could 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.