Keynote@EDOC'25 - AI-augmented Process Mining and Automation, by Stefanie Rinderle-Ma, Technical University of Munich, Germany

AI-augmented Process Mining and Automation

Starting the conversation:

Part I - Process Modeling

Can we use GenAI to generate process models?

- Process elicitation with domain experts yields text such as process descriptions, interviews and questionnaires. There is so much knowledge in the mind of the experts, it is better to understand this by integrating both human expert and AI.
- We must also consider regulations. "The body of law to which citizens and businesses have to adhere to is increasing in volume and complexity as our society continues to advance" (Boella et al. 2016a).
- Legal knowledge frequently changes. Financial regulations change every 12 minutes (she cited a website article about this).

Lifecycle:

Interesting readings from her on this topic:

She showed some tools built to support Conversation Process Model Design and Redesign, including one in a project in collaboration with SAP.

Quality assurance:
- They developed metrics to judge quality, such as Completeness and Correctness.
- Completeness: formal structure of process models: nodes and edges; precidsion,r ecall and jaccard index are used, in a way this metric can be automatically verified
- Correctness: semantic alignment between the process model and the text description, accounting for possible deviations.

Experiments showed that 70% of the users preferred the LLM model than the ground-truth model and the model they did themselves.
The experiments so far focused on the perception of the modeler, not the expert. So this remains as future work.

Part II - Process Automation

She finds the Manufacturing domain a good example of complex domain that may benefit from process automation, similar to Health, Logistics and Transprt.

She passed the "finger" around in the audience. Automation of Cocktail mix:

She showed a video of a robot mixing a cocktail, based on a process composed of several tasks. Very interesting!

Process Automation -> Process Autonomization (thiking about Agentic Systems)

Food for thought and discussion:
- How to measure the quality of process models created with GenAI?
- How can we involve the experts in the conversation?
- What is first: the model or the data?

She worked with nurses and realized that most of the time, nurses spend on documentation and not patients. That is sad!

- How to realize process orientation?
Leveled approach:
Soft integration: connect the machine and collect data
Process Modeling: convince the experts to model
Augmentation
Control

Stefanie's team: