Epistemic resilience in AI-driven innovation

Abstract

Artificial intelligence (AI) has become an integral component of innovation infrastructure, facilitating information retrieval, knowledge recombination, and decision support. While these applications accelerate innovation cycles, they simultaneously obscure evidential provenance, compromise output reliability, and diffuse accountability when algorithmic insights fail. To address these vulnerabilities, this paper introduces the concept of epistemic resilience, defined as an organizational and ecosystem capability to preserve, adapt, and restore trustworthy knowledge processes amid technological disruption, uncertainty, and epistemic risk. Synthesizing literature across innovation management, organizational learning, knowledge management, social epistemology, research integrity, and human–AI collaboration, this study conceptualizes AI-driven innovation as a sociotechnical knowledge process wherein computational acceleration remains contingent upon the integrity of the underlying epistemic environment. We propose a governance framework structured around four interdependent pillars: epistemic culture and virtues; data and model governance; critical AI literacy and human–AI judgment; and aligned incentives and leadership. Operating across individual, team, organizational, and ecosystem levels, these pillars facilitate the prevention, detection, adaptation, and remediation of epistemic breakdowns. By prioritizing adaptive learning, accountability, trust, and systemic recovery over static oversight, this framework delineates epistemic resilience from adjacent domains such as research integrity, data governance, AI ethics, regulatory compliance, and conventional risk management. Ultimately, this paper establishes a conceptual foundation for trustworthy, AI-augmented innovation and outlines an empirical agenda for operationalizing and cultivating epistemic resilience within organizations.