Generative Artificial Intelligence–Enabled Hyperautomation: Reconfiguring Financial and Enterprise Workflows through Cognitive, Process-Aware, and Governance-Centric Architectures

Abstract

The rapid convergence of generative artificial intelligence, process mining, robotic process automation, and cognitive automation has catalyzed the emergence of hyperautomation as a dominant paradigm for enterprise transformation. While early automation initiatives focused primarily on task-level efficiency gains, contemporary hyperautomation frameworks seek to reconfigure entire organizational workflows, decision structures, and governance mechanisms. This article develops an extensive theoretical and interpretive analysis of hyperautomation with a particular emphasis on financial and enterprise workflows, grounding its arguments strictly in the extant scholarly literature. Drawing centrally on recent work that conceptualizes hyperautomation as an integrated framework combining generative artificial intelligence and process mining for financial workflows (Krishnan & Bhat, 2025), this study situates hyperautomation within broader debates on cognitive automation, agentic information systems, and intelligent governance. Through a qualitative, literature-driven methodological approach, the article synthesizes insights from information systems research, management studies, artificial intelligence governance, and industry-oriented hyperautomation analyses to articulate how hyperautomation reshapes organizational cognition, redistributes agency between humans and machines, and introduces novel risks related to opacity, accountability, and data security.

The findings presented in this study are interpretive rather than empirical, emphasizing conceptual coherence, theoretical depth, and critical comparison across scholarly perspectives. The results demonstrate that hyperautomation is not merely an extension of robotic process automation but represents a systemic reorientation of enterprise architecture in which generative AI models contribute adaptive reasoning, process mining enables continuous learning from execution data, and cognitive automation alters the nature of knowledge work itself. The discussion advances a multi-layered interpretive framework that reconciles efficiency-driven narratives with sociotechnical critiques, highlighting tensions between productivity, workforce transformation, and regulatory governance. By elaborating limitations inherent in current hyperautomation research and proposing theoretically grounded directions for future inquiry, this article contributes a comprehensive, publication-ready synthesis intended to support advanced academic discourse on intelligent automation and enterprise transformation.