Celonis has already helped half of the world's 200 largest companies gain the intelligence to better understand their businesses. Now it's helping them predict the future.

On Tuesday, Celonis announced the Celonis Context Model, or CCM, which can act as a digital twin of your business operations and dynamically update in real time. The idea is to create a context layer in your organization that is much smarter and more adaptable than just a knowledge graph or an MDM system.

CCM brings together structured and unstructured data from every interaction across screens, system logs, policies, and process descriptions. This creates a consistent language about your organization that AI can understand.

Alex Rinke, Co-CEO of Celonis, told The Deep View that the biggest issue with enterprise AI is the lack of context. The best enterprise AI is a “specialist in your business,” rather than a general model.

"You need to teach it how to be good at doing a specific job in that business,” Rinke said, “That requires a lot of context, and that context needs to be brought together and put in a language that the AI can understand and effectively reason about."

That context can be the missing piece between an AI project that's struggling to create value and one that can automate processes that have been resistant to AI so far.

Additionally, Celonis announced its acquisition of Ikigai Labs, a company led by Dev Shah and founded at MIT that has spent the past 20 years building AI systems for prediction, forecasting, time-series analysis, and what-if scenario simulation.

Rinke told The Deep View that Ikigai allows it to bring in graphical models and other tools that specialize in future reasoning, predictions and forecasting. That way, its context model “doesn’t just represent in real time what’s going on right now, but can also look into the future, [at] what could happen.”

So what can all of this do? In evaluations run by Celonis and customers such as AstraZeneca, Mercedes, Domino Foods, insurance companies, and others, they found that integrating the context layer with AI agents dramatically improved efficiency, accuracy, task completion, and speed.

Our Deeper View

When it comes to successfully adopting AI in business, smarter LLMs can only take you so far. The context layer can certainly be the difference between an AI project that fails to deliver ROI and one that meets expectations for creating value. Celonis was already doing that with its focus on process intelligence, where it was recently crowned as the industry leader in Gartner's Magic Quadrant. In many ways, CCM and the context layer are simply a rebranding of Process Intelligence. But integrating Ikigai is a game-changer. Where Celonis can make enterprises much smarter about the way they run in real-time, Ikigai can take that data and model out the future so that AI agents can make better decisions when doing things like offering sales, deals, price quotes, and other actions to save time and money and give customers a much faster and better experience, for example. Those are the kinds of outcomes that can compound significantly over time in many organizations, especially the largest ones.