Towards LLM-Driven Transformation of Goal Models for AI-Enabled Socio-Technical Systems

Abstract

Organizations are increasingly transforming traditional software systems into AI-enabled socio-technical systems. While goal models are widely used to capture stakeholder intentions, organizational objectives, and system requirements, existing approaches provide limited support for systematically evolving these models to reflect AI capabilities and the governance concerns they introduce, such as explainability, fairness, and accountability. This paper presents AI-Oriented Goal Model Transformation (AIGMT), an approach for transforming Goal-oriented Requirement Language (GRL) models of traditional systems into GRL models for AI-enabled socio-technical systems. AIGMT combines an AI Readiness Assessment, a catalog of AI transformation patterns, and Large Language Model (LLM)-assisted reasoning to identify candidate tasks for AI augmentation and generate corresponding model transformations. The methodology systematically introduces AI-enabled tasks, supporting resources, and governance requirements while preserving the original stakeholder intentions and goal-model structure. The approach is evaluated using three representative GRL models from the insurance, education, and healthcare domains, namely Insurance Claim Processing (the running example), University Course Registration, and Hospital Patient Intake and Triage. Together, these examples demonstrate the domain-independence of AIGMT and its ability to systematically evolve existing goal models into AI-enabled systems.