Continuous Business Process Improvement Driven by Large Language Models

Authors’ abstract

Some of the main challenges faced by organizations when applying Continuous Business Process Improvement are data fragmentation, limited explainability, weak governance, and the isolated use of Artificial Intelligence in Business Process Management. This study initially conducts a Systematic Literature Review on the topic of business process improvement enabled by Large Language Models or Artificial Intelligence in organizations, presenting a comprehensive analysis of prevailing research trends, conceptual frameworks, and persistent limitations, identifying seventeen recurring gaps that affect the effectiveness of integrating the capabilities of Large Language Models and other Artificial Intelligence technologies throughout the entire lifecycle of Continuous Business Process Improvement. As a result, we propose a Framework and its gap-oriented reference architecture that, through modular components, facilitates data integration, reasoning, validation, execution, and monitoring within a closed loop of continuous business process improvement. The framework is operationalized through six phases: Process Understanding, Process Diagnosis, Process Redesign, Process Validation, Process Execution Support, and Continuous Monitoring. The results suggest that designing the framework and architecture directly from the identified gaps creates a coherent foundation for AI-driven process improvement, enabling more reliable, explainable, and easily governed and managed solutions. The study improves the current state of the art by creating a cohesive framework for intelligent, scalable, lifecycle-integrated, and operationally deployable process optimization systems. INDEX TERMS Artificial intelligence, business process improvement, explainable AI, large language models, process intelligence. I. INTRODUCTION Business processes are collections of related structured activ-ities or tasks that are used to generate a product or service [1]. They determine how the work should be done within an orga-nization, becoming the building blocks of the organization. Consequently, business processes are vital to the operation of an organization. Due to their use, resources can be used more efficiently, products and services can be delivered on time, and quality standards are reached. There-fore, it is important for organizations to properly manage their business processes [1]. The associate editor coordinating the review of this manuscript and approving it for publication was Sedat Akleylek. In this context, Business Process Management (BPM) is a systematic methodology to assess, plan, implement, mon-itor, and optimize business processes. It helps organizations streamline operations, minimize expenses, improve customer satisfaction, and ensure compliance while focusing on gover-nance and strategy [2]. Within the BPM lifecycle, Business Process Improvement (BPI) is a systematic approach to help an organization optimize its underlying processes for more efficient results [1]. Currently, organizations are increasingly dependent on BPI to maintain efficiency, service quality, regulatory compliance, and adaptability in rapidly changing environments. In this context, with the advancement of Artifi-cial Intelligence (AI) technologies, organizations have started to use these technologies, such as Large Language Models (LLMs), as a boost to promote the continuous improvement of business processes [3]. A Continuous Business Process Improvement (CBPI) approach seeks to maintain and improve organizational per-formance [2], [4]. In real settings, CBPI depends on the com-bination empirical execution evidence (e.g., event logs, KPIs, historical baselines) with contextual and normative knowl-edge (e.g., policies, procedures, regulations, risk controls) to produce diagnostic insights and actionable redesign decisions [5], [6]. Recent advances in AI, especially LLMs when com-bined with Retrieval-Augmented Generation (RAG), bring new opportunities for BPM and BPI, since these models have the ability to handle unstructured data, create summaries of operational evidence and textual explanations that aid in decision-making processes, particularly when related to business process redesign. [7], [8]. However, LLM-CBPI ini-tiatives frequently struggle with three interrelated challenges:

Paper

Authors

A.S. Araujo, H.S. Mamede, V. Santos, V. Filipe

Publication date

2026

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Creative Commons Attribution 4.0 International

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