Generative AI–Driven Hyperautomation for Financial and Enterprise Workflows: A Process Mining–Centric Theoretical and Empirical Synthesis

Abstract

The accelerating convergence of generative artificial intelligence, robotic process automation, advanced analytics, and process mining has catalyzed the emergence of hyperautomation as a dominant paradigm for transforming enterprise and financial workflows. Unlike earlier automation waves that emphasized task-level efficiency and labor substitution, contemporary hyperautomation frameworks aspire to end-to-end cognitive orchestration of complex organizational processes, integrating decision intelligence, learning systems, and adaptive governance. This article develops a comprehensive, publication-ready theoretical and empirical synthesis of hyperautomation with a particular emphasis on financial workflows, drawing strictly on the provided scholarly and practitioner-oriented literature. Anchored in recent conceptual advances, especially the integration of generative artificial intelligence with process mining for financial workflow empowerment, the study situates hyperautomation within broader debates on intelligent automation, the future of work, and digital enterprise transformation (Krishnan & Bhat, 2025).

The article advances three interrelated objectives. First, it constructs an extensive theoretical foundation tracing the historical evolution from robotic process automation to hyperautomation, highlighting shifts in epistemological assumptions about work, cognition, and organizational intelligence (Madakam et al., 2022; Lasso-Rodriguez & Winkler, 2020). Second, it elaborates a rigorous text-based methodological framework suitable for analyzing hyperautomation initiatives in financial and enterprise contexts, emphasizing interpretive synthesis, comparative conceptual analysis, and design-oriented reasoning grounded in the literature (Kedziora, 2022). Third, it presents a deeply elaborated results and discussion narrative that interprets emergent patterns, capabilities, and tensions associated with generative AI–enabled hyperautomation, including governance risks, workforce implications, scalability challenges, and ethical considerations (Man, 2022; Coombs et al., 2020).

Throughout the article, financial workflows are treated not merely as operational pipelines but as socio-technical systems characterized by regulatory constraints, data heterogeneity, and strategic significance. The integration of process mining and generative AI is analyzed as a transformative mechanism that enables continuous discovery, simulation, and optimization of financial processes while simultaneously raising new questions about explainability, accountability, and organizational trust (Krishnan & Bhat, 2025). By engaging critically with supporting domains such as ERP transformation, big data analytics, reinforcement learning–based automation, and cloud-scale data infrastructures, the article positions hyperautomation as a foundational capability for the next generation of digital enterprises (Rajan Rauniyar, 2024; Yerra, 2025).

The contribution of this study lies in its depth of theoretical elaboration and its integrative perspective, which bridges fragmented discourses across information systems, automation research, and organizational studies. Rather than offering prescriptive checklists or narrow case descriptions, the article provides a holistic analytical framework that scholars and practitioners can use to understand, evaluate, and responsibly advance hyperautomation initiatives in financial and enterprise settings. In doing so, it responds directly to the growing demand for rigorous, conceptually grounded research that can guide hyperautomation beyond hype toward sustainable and ethically informed organizational value creation (Krishnan & Bhat, 2025; Sujatha et al., 2023)