The accelerating convergence of generative artificial intelligence, intelligent automation, and process mining has reshaped contemporary understandings of organizational efficiency, governance, and value creation within financial workflows. Hyperautomation, once framed narrowly as a technological upgrade, has increasingly emerged as a socio-technical paradigm that reconfigures institutional logics, labor relations, and strategic decision-making architectures across financial services organizations. This research article develops an extensive theoretical and interpretive analysis of hyperautomation as an institutional catalyst, grounded strictly in extant scholarly literature and framed through a rigorous academic lens. Drawing centrally on the hyperautomation framework articulated by Krishnan and Bhat, the study situates generative artificial intelligence and process mining as mutually reinforcing mechanisms that transcend traditional rule-based automation by embedding adaptive intelligence and real-time process visibility into financial operations (Krishnan & Bhat, 2025).

The article elaborates on the historical evolution of artificial intelligence from early symbolic reasoning paradigms to contemporary transformer-based architectures, contextualizing the rise of hyperautomation within broader trajectories of digital transformation and organizational learning (Bruderer, 2016; Bornet et al., 2020). It further interrogates how intelligent workflows mediate the relationship between technological innovation and human agency, particularly in financial institutions characterized by high regulatory intensity, legacy system entrenchment, and complex interdependencies between human judgment and algorithmic decision-making (Cameron, 2022; Kalluri, 2024). By synthesizing insights from research on meaningful work, ethical artificial intelligence, and digital governance, the article advances a nuanced conceptualization of hyperautomation not as a deterministic force but as an institutional assemblage shaped by organizational culture, leadership cognition, and normative constraints (Blustein et al., 2023; Kamatala et al., 2025b).

Methodologically, the study adopts an interpretive, theory-building approach that relies on critical textual analysis, cross-domain synthesis, and comparative conceptual reasoning. The results are presented as analytically derived patterns that reveal how generative artificial intelligence enhances process mining by enabling semantic abstraction, predictive reasoning, and dynamic orchestration of financial workflows, while simultaneously introducing new tensions related to bias, transparency, and accountability (Bura, 2025; Krishnan & Bhat, 2025). The discussion extends these findings by engaging competing scholarly perspectives on automation, digital transformation, and organizational ethics, ultimately proposing a research agenda that foregrounds hyperautomation as a central construct in future studies of financial innovation and institutional change.