Process mining as a foundation for the application of AI in companies
The deployment of artificial intelligence (AI) in companies can only truly generate value if AI understands how the business operations are structured. This insight is crucial for improving processes and adapting corporate cultures.
Summary
- Celonis emphasizes the importance of context for effective AI applications within companies.
- Through process intelligence, AI can gain insight into processes and optimization opportunities.
- Companies such as Mercedes-Benz and Andritz demonstrate how they achieve improvements in their operations using process mining.
AI and business operations
To get started with AI, companies must meet three prerequisites. First, AI must understand how the business operations are structured and have the right context. Second, AI should be strategically deployed in the right areas within the organization. Finally, AI must be able to collaborate with existing systems. Software provider Celonis believes they can provide the right context. According to them, effective deployment of AI is not possible without process intelligence.
Process mining and the process intelligence graph
Celonis applies process mining to gain insight into the actual course of events within processes. This often deviates from what companies believe their processes to be. By combining data from systems with company-specific information, it is possible to create a digital twin of the operation through what they call the ‘process intelligence graph’ (PIG). This graph helps companies identify bottlenecks and optimization opportunities.
Case studies in the automotive industry
During the Celosphere event, companies in the automotive industry, including Mercedes-Benz, share their experiences with process mining. Mercedes-Benz discovered that during the chip crisis, they could gain better insight into bottlenecks by linking data from different systems. This resulted in fewer delays, fewer production errors, and faster parts logistics. Their approach has not only changed the processes but also the corporate culture.
Sustainability in the supply chain
Machine manufacturer Andritz has developed an application that calculates CO2 emissions based on the process intelligence graph. This application uses transaction data from the supply chain and links it to emission data. This enables the company to easily calculate the total CO2 footprint. The data is accessible to everyone within the company, which benefits decision-making.
Insight into deliveries
Thyssenkrupp uses Celonis software to gain control over incoming goods flows. By linking the production schedules of different locations, they can predict shortages early. With the help of generative AI, the information is presented in understandable language, allowing priorities to be adjusted in a timely manner.
Optimization of the order-to-cash process
The American chemical distributor Vinmar explains how the process intelligence graph helps them manage their complex supply chain. Previously, booking sea freight was a manual task, but by deploying AI, they can now easily compare the best options. This makes it possible to handle the entire order-to-cash process automatically, allowing employees to focus on other important tasks.
Source: Henrieke Wagenvoort, Tue, 02 Dec 2025 11:19:15 +0000, link
