  The continued success of Large Language Models (LLMs) and other generative
artificial intelligence approaches highlights the advantages that large
information corpora can have over rigidly defined symbolic models, but also
serves as a proof-point of the challenges that purely statistics-based
approaches have in terms of safety and trustworthiness. As a framework for
contextualizing the potential, as well as the limitations of LLMs and other
foundation model-based technologies, we propose the concept of a Large Process
Model (LPM) that combines the correlation power of LLMs with the analytical
precision and reliability of knowledge-based systems and automated reasoning
approaches. LPMs are envisioned to directly utilize the wealth of process
management experience that experts have accumulated, as well as process
performance data of organizations with diverse characteristics, e.g., regarding
size, region, or industry. In this vision, the proposed LPM would allow
organizations to receive context-specific (tailored) process and other business
models, analytical deep-dives, and improvement recommendations. As such, they
would allow to substantially decrease the time and effort required for business
transformation, while also allowing for deeper, more impactful, and more
actionable insights than previously possible. We argue that implementing an LPM
is feasible, but also highlight limitations and research challenges that need
to be solved to implement particular aspects of the LPM vision.
