Accountable Agility: Extending the AI Capability–Performance Relationship in Humanitarian Supply Chains

AI capability may support humanitarian supply chains through organisational learning and agility, but faster adaptation is not necessarily better humanitarian performance. This conceptual paper critically extends Bdair’s study of AI capability, organisational learning, supply chain agility and performance in Jordanian humanitarian NGOs. It proposes accountable agility: the capacity to revise operational decisions promptly while preserving traceable responsibility, context-sensitive judgement and routes for correction. Drawing on resource-based and dynamic-capability reasoning, absorptive capacity and AI risk management, the paper distinguishes technical capability from the decision arrangements through which it affects delivery. Six propositions address workflow bottlenecks, verification under urgency, local knowledge, minimum service requirements, feedback and interorganisational coordination. The analysis treats accountability as both a normative requirement and a possible operational complement whose effects require testing. It also explains why cross-sectional mediation estimates cannot establish the temporal sequence of capability development, learning and performance. A future research agenda combines decision-episode observation, longitudinal capability measures, operational records and perspectives from affected communities. Practical implications include uncertainty records, differentiated review requirements, explicit decision rights and tested fallback arrangements. No new empirical findings are claimed. The contribution is a bounded conceptual framework for identifying when AI-assisted agility may produce defensible humanitarian benefit and when it may instead accelerate unreliable decisions.