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<p>Artificial Intelligence (AI) can enhance Humanitarian Supply Chains (HSCs) by improving responsiveness, efficiency, and transparency. Humanitarian organizations (HOs), i.e., local NGOs, international NGOs, and UN agencies, are exploring AI, yet adoption is hindered by resource constraints, data issues, skills gaps, and unclear policies. This study develops a readiness-assessment framework tailored to HSCs that integrates the Technology-Organization-Environment (TOE) lens with fuzzy logic and Total Interpretive Structural Modelling (TISM). TOE is used to identify and structure enablers and their attributes; fuzzy logic develops a readiness index and highlights weaker attributes; TISM then maps causal linkages to reveal driver barriers and priority interventions. Using expert input and a validating case study, results indicate that HOs show moderate readiness but are held back mostly by inadequate ICT infrastructure, limited AI awareness and skills, insufficient leadership support, and gaps in data governance and regulation. Conceptually, the study contextualizes AI readiness for humanitarian settings; methodologically, it combines quantitative (fuzzy) and qualitative (TISM) analysis to produce a practical roadmap. The findings guide policymakers and practitioners on where to invest first, i.e., data quality and governance, capability building, leadership commitment, and infrastructure to accelerate responsible AI adoption in humanitarian operations.</p>

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AI adoption readiness assessment for humanitarian supply chain: a novel TOE-fuzzy logic-TISM approach

  • 期刊论文

  • 发表日期：

    2026年4月21日

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  • Siddharth Prajapati,
  • Ramesh Anbanandam

摘要

Artificial Intelligence (AI) can enhance Humanitarian Supply Chains (HSCs) by improving responsiveness, efficiency, and transparency. Humanitarian organizations (HOs), i.e., local NGOs, international NGOs, and UN agencies, are exploring AI, yet adoption is hindered by resource constraints, data issues, skills gaps, and unclear policies. This study develops a readiness-assessment framework tailored to HSCs that integrates the Technology-Organization-Environment (TOE) lens with fuzzy logic and Total Interpretive Structural Modelling (TISM). TOE is used to identify and structure enablers and their attributes; fuzzy logic develops a readiness index and highlights weaker attributes; TISM then maps causal linkages to reveal driver barriers and priority interventions. Using expert input and a validating case study, results indicate that HOs show moderate readiness but are held back mostly by inadequate ICT infrastructure, limited AI awareness and skills, insufficient leadership support, and gaps in data governance and regulation. Conceptually, the study contextualizes AI readiness for humanitarian settings; methodologically, it combines quantitative (fuzzy) and qualitative (TISM) analysis to produce a practical roadmap. The findings guide policymakers and practitioners on where to invest first, i.e., data quality and governance, capability building, leadership commitment, and infrastructure to accelerate responsible AI adoption in humanitarian operations.