In an era of persistent disruptions, growing uncertainty, and rising sustainability pressures, ensuring supply chain viability has become a strategic imperative. Viability, as conceptualized in this study, refers to a system’s capacity to sustain functionality, adapt to change, and improve over time, and encompasses five interrelated dimensions: sustainability, resilience, adaptability, plasticity, and antifragility. While artificial intelligence (AI) is increasingly applied in supply chains, existing research largely focuses on localized optimization and short-term efficiency, leaving its role in enabling systemic viability underexplored. This study examines how AI contributes to supply chain viability through a qualitative analysis of 31 semi-structured interviews with senior supply chain executives across multiple industries. The findings show that AI does not act merely as an operational tool but rather as a strategic enabler operating through dynamic capabilities related to sensing, scenario-based orchestration, structural reconfiguration, and organizational learning. Through these mechanisms, AI helps align different viability dimensions over time rather than optimizing them in isolation. At the same time, the effectiveness of AI depends on organizational conditions such as data governance, cross-functional alignment, and a sustained orientation toward learning. Beyond these moderating effects, the analysis reveals a reverse influence from supply chain viability requirements to AI itself. Building on these insights, the study develops an integrative representation that positions AI as a managed socio-technical capability embedded within supply chain systems. The findings extend research on AI and digital transformation beyond efficiency-oriented perspectives and offer a forward-looking foundation for understanding how supply chains can remain viable under persistent uncertainty.
CORE (COnnecting REpositories)
CORE (COnnecting REpositories)