  In the rapidly evolving field of business process management, there is a
growing need for analytical tools that can transform complex data into
actionable insights. This research introduces a novel approach by integrating
Large Language Models (LLMs), such as ChatGPT, into process mining tools,
making process analytics more accessible to a wider audience. The study aims to
investigate how ChatGPT enhances analytical capabilities, improves user
experience, increases accessibility, and optimizes the architectural frameworks
of process mining tools. The key innovation of this research lies in developing
a tailored prompt engineering strategy for each process mining submodule,
ensuring that the AI-generated outputs are accurate and relevant to the
context. The integration architecture follows an Extract, Transform, Load (ETL)
process, which includes various process mining engine modules and utilizes
zero-shot and optimized prompt engineering techniques. ChatGPT is connected via
APIs and receives structured outputs from the process mining modules, enabling
conversational interactions. To validate the effectiveness of this approach,
the researchers used data from 17 companies that employ BehfaLab's Process
Mining Tool. The results showed significant improvements in user experience,
with an expert panel rating 72% of the results as "Good". This research
contributes to the advancement of business process analysis methodologies by
combining process mining with artificial intelligence. Future research
directions include further optimization of prompt engineering, exploration of
integration with other AI technologies, and assessment of scalability across
various business environments. This study paves the way for continuous
innovation at the intersection of process mining and artificial intelligence,
promising to revolutionize the way businesses analyze and optimize their
processes.
