Research

@article{Kampik:2024aa,

title = {Large Process Models: A Vision for Business Process Management in the Age of Generative AI},

author = {Timotheus Kampik and Christian Warmuth and Adrian Rebmann and Ron Agam and Lukas N. P. Egger and Andreas Gerber and Johannes Hoffart and Jonas Kolk and Philipp Herzig and Gero Decker and Han Aa and Artem Polyvyanyy and Stefanie Rinderle-Ma and Ingo Weber and Matthias Weidlich},

url = {https://doi.org/10.1007/s13218-024-00863-8},

doi = {10.1007/s13218-024-00863-8},

isbn = {1610-1987},

journal = {KI - Künstliche Intelligenz},

abstract = {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 enable organizations to receive context-specific (tailored) process and other business models, analytical deep-dives, and improvement recommendations. As such, it 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.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Kampik:2024aab,

title = {Large Process Models: A Vision for Business Process Management in the Age of Generative AI},

author = {Timotheus Kampik and Christian Warmuth and Adrian Rebmann and Ron Agam and Lukas N. P. Egger and Andreas Gerber and Johannes Hoffart and Jonas Kolk and Philipp Herzig and Gero Decker and Han Aa and Artem Polyvyanyy and Stefanie Rinderle-Ma and Ingo Weber and Matthias Weidlich},

url = {https://doi.org/10.1007/s13218-024-00863-8},

doi = {10.1007/s13218-024-00863-8},

isbn = {1610-1987},

journal = {KI - Künstliche Intelligenz},

abstract = {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 enable organizations to receive context-specific (tailored) process and other business models, analytical deep-dives, and improvement recommendations. As such, it 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.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}