Högskolan i Skövde

Manufacturing and supply chain operations increasingly face persistent volatility that makes traditional optimisation and resilience logics insufficient. This paper examines how artificial intelligence (AI) can enable antifragility, defined as the capability that improve through exposure to volatility. Drawing on Interpretive Structural Modelling supported by Delphi and Nominal Group Technique with insights from senior practitioners from advanced manufacturing firms,this study identifies thirteen AI-enabled functions for antifragiltiy. It further organises these functions into a hierarchical capability architecture. Foundational functions provide predictive sensing, causal diagnostics, and continuous learning loops. Mid-tier functions use these learning capabilities to orchestrate flexibility, inventory, logistics, and sourcing reconfiguration, while upper tiers turn turbulence into innovation, demand reframing, and strategic capacity shifts that culminate in adaptive scheduling and autonomous control. The study moves antifragility from metaphor to mechanism and positions AI as a structured capability system, offering a strategic roadmap for sequencing AI investments towards higher-order autonomy in production contexts.