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Artificial Intelligence (AI)Artificial Intelligence (AI) in the Indian healthcare supply chain (HSC)Healthcare supply chain faces numerous problems, necessitating an organized approach to overcome obstacles and ensure smooth integration. Interpretive Structural Modeling (ISM)Interpretive Structural Modeling (ISM) offers a systematic framework for analyzing the interdependencies between these barriersBarriers and developing strategic solutions. The study identifies high driving power barriers as primary obstacles, including high implementation costs (C1), high initial investment (C9), a lack of skilled laborSkilled labor (C3), and infrastructure integrationInfrastructure integration challenges (C5), and recommends government incentives, targeted training programs, and middleware solutionsMiddleware solutions to overcome them. Regulatory and data-related hurdles (C8, C7) need the creation of AI compliance frameworksAI compliance framework, and standardized data protocols to improve system reliability. Awareness campaignsAwareness campaigns and explainable AIExplainable AI strategies to boost stakeholder confidence must address adoption resistance and trust difficultiesTrust difficulties (C10, C12). By focusing on fundamental roadblocks stepwise to additionally resolve other related challenges, ISMInterpretive Structural Modeling (ISM) provides a roadmap for how AIArtificial Intelligence (AI) can best be adopted to scale in the India Healthcare value chain while keeping the process efficient, transparent, and scalable.

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A Structured Approach to Overcome AI Adoption Barriers in the Indian Healthcare Supply Chain: An ISM-Based Analysis

  • 章节

  • 发表日期：

    2026年

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  • Ambuj Kumar,
  • Om Ji Shukla

摘要

Artificial Intelligence (AI)Artificial Intelligence (AI) in the Indian healthcare supply chain (HSC)Healthcare supply chain faces numerous problems, necessitating an organized approach to overcome obstacles and ensure smooth integration. Interpretive Structural Modeling (ISM)Interpretive Structural Modeling (ISM) offers a systematic framework for analyzing the interdependencies between these barriersBarriers and developing strategic solutions. The study identifies high driving power barriers as primary obstacles, including high implementation costs (C1), high initial investment (C9), a lack of skilled laborSkilled labor (C3), and infrastructure integrationInfrastructure integration challenges (C5), and recommends government incentives, targeted training programs, and middleware solutionsMiddleware solutions to overcome them. Regulatory and data-related hurdles (C8, C7) need the creation of AI compliance frameworksAI compliance framework, and standardized data protocols to improve system reliability. Awareness campaignsAwareness campaigns and explainable AIExplainable AI strategies to boost stakeholder confidence must address adoption resistance and trust difficultiesTrust difficulties (C10, C12). By focusing on fundamental roadblocks stepwise to additionally resolve other related challenges, ISMInterpretive Structural Modeling (ISM) provides a roadmap for how AIArtificial Intelligence (AI) can best be adopted to scale in the India Healthcare value chain while keeping the process efficient, transparent, and scalable.