Notice: This article was created with AI.
What’s It About?
Celonis has fundamentally developed its platform further and is adding what it calls context models to classic process analysis. These digital representations of business processes link company data with business knowledge and enable an end-to-end view of the entire value chain. At the same time, the new architecture serves as a knowledge layer for AI agents, supplying them with the necessary business context.
The platform allows users not only to visualize actual processes but also to model target processes and continuously compare them against reality. Because it connects to various large language models, the solution remains vendor-independent and can incorporate data from existing ERP and CRM systems directly.
Background & Context
Over the past 15 years, Celonis has evolved from a pure process mining provider into a platform provider for process intelligence. While process mining originally made individual processes visible, the new approach brings all company processes together in one integrated data model. This enables a holistic view of workflows that were previously considered in isolation.
The context models act as digital twins that are enriched with business objects and domain knowledge. This creates a data basis on which companies can identify bottlenecks in real time and derive optimization measures. The platform’s open infrastructure allows different AI models to be integrated, which gives companies flexibility in choosing their technology partners.
With the planned acquisition of Ikigai Labs, Celonis intends to further expand its capabilities in AI-supported decision-making. The combination of process knowledge and AI technology aims to minimize undesirable outcomes from AI agents and to increase their efficiency through precise business context.
What Does This Mean?
- Companies gain an end-to-end view of their process landscape instead of isolated individual analyses, which makes holistic optimization possible.
- AI agents can draw on a structured knowledge layer with business context, making their decisions more precise and easier to follow.
- The vendor-independent architecture avoids dependence on individual AI providers and permits the flexible integration of various large language models.
- Using existing data from ERP and CRM systems means analyses can be implemented more quickly than with solutions that require new data structures.
- Comparing target and actual processes enables continuous process optimization and makes deviations visible immediately.
Sources
Wie Celonis sein Geschäftsmodell für das KI-Zeitalter umbaut (Computerwoche)
This article was created with AI and is based on the listed sources as well as the language model’s training data.
Further Reading: Paperclip: When AI Agents Get an Org Chart