The shift to circular supply chains (CSC) in the context of Industry 4.0 has further driven the adoption of artificial intelligence (AI) for production planning, logistics and sustainability considerations. But an increasing body of evidence suggests that the environmental implications are less a matter of AI adoption and more about how effectively AI complements human judgment and human skills. The paper contributes by introducing a meta-analysis on the function of human–AI co-decision systems and labour intelligence as enablers for circular economy (CE) in production systems. Based on PRISMA-2020 checklist, 269 Scopus-indexed empirical studies published through 2018 to 2025 were systematically screened and meta-analyzed by applying random-effect meta-analytic method. The findings reveal a robust and positive association with statistical significance between human–AI co-decision systems and the adoption of circular behavior, which ultimately enhances environmental performance. Workforce intelligence, including digital skills, collaborative capability and organization learning readiness enhances these relationships. Models that are based on a closely working relationship of the human and AI system continue to outperform dominant AI models, as well as highly assistant systems. Social sustainability outcomes are enhanced the most, whereas economic gains are rather modest, proving real-world trade-offs in implementation. Together, the results underscore the significance of human-centric design of AI and workforce readiness in driving sustainable and resilient CSC.
Artificial Intelligence (AI) is reshaping how organizations handle knowledge from the moment they acquire it, to how they generate, validate, integrate, learn from, and put it to work. Research has touched on many of these pieces: AI capability, knowledge management, organizational learning, and innovation, but these areas often feel disconnected in theory. This paper pulls those threads together by building a structured framework for the literature and theory, showing how AI capability fuels innovation via interconnected knowledge processes across the organization.
The approach draws from the Knowledge-Based View (KBV), Nonaka and Takeuchi’s SECI model, Organizational Learning Theory, Dynamic Capability Theory, and recent work on AI capability. Here, AI capability is conceptualized as a higher-order, formative organizational capability. It’s an ensemble of technological infrastructure, data resources, skilled AI professionals, strong coordination and change management, and robust AI governance.
One notable theoretical advance in this work is the introduction of Knowledge Validation and Epistemic Governance. These act as a bridge between AI-powered knowledge acquisition or creation and its integration within the organization. The idea is simple: just because AI creates content doesn’t mean it’s automatically part of the organization’s knowledge. That content must first be checked for accuracy, origin, relevance to context, clarity, bias, and accountability before it’s fully integrated.
The framework presented sees knowledge acquisition and creation as parallel tracks that come together through validation and integration. From there, organizational learning, smarter decision making, and finally, innovation performance follow. It’s not a one-way street, either innovations feedback into new knowledge creation, and what the organization learns helps shape AI capability itself.
The paper offers eight propositions based in theory, and touches on what these mean for future research: how to test these ideas, ways to measure them, the importance of context, and tracking change over time. In sum, this contribution links AI capability to core theories in knowledge and organization, putting human judgment, epistemic governance, and knowledge validation at the center of how organizations learn and innovate with AI.
Vaivaw Kumar Singh· International Journal of Lat...· 0 citations
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