Paper Type

Complete

Abstract

Organizations are investing in artificial intelligence (AI) for forecasting, scheduling, quality, maintenance, and service operations, yet many initiatives stall at pilot scale because leaders lack an operations-centered strategic integration logic that links use cases to workflows, data and platform foundations, governance, and change execution. This study develops an evidence-informed framework for the strategic integration of AI through a PRISMA-guided systematic literature review and design science artifact construction. The review applied explicit inclusion and exclusion criteria, structured coding, quality appraisal, and evidence-to-design translation to derive seven decision domains and a strategy-to-execution cycle. Initial ex ante formative evaluation combines analytical traceability mapping and literature-grounded scenario demonstration in representative operations management contexts to assess clarity, completeness, internal consistency, actionability, and boundary conditions. The resulting framework offers a rigorous and practical blueprint for scalable and trustworthy AI-enabled operations while clarifying where future expert and field validation are still required.

Paper Number

1148

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Aug 15th, 12:00 AM

Strategic Integration of AI for Data‑Driven Decisions and Automation in Operations Management

Organizations are investing in artificial intelligence (AI) for forecasting, scheduling, quality, maintenance, and service operations, yet many initiatives stall at pilot scale because leaders lack an operations-centered strategic integration logic that links use cases to workflows, data and platform foundations, governance, and change execution. This study develops an evidence-informed framework for the strategic integration of AI through a PRISMA-guided systematic literature review and design science artifact construction. The review applied explicit inclusion and exclusion criteria, structured coding, quality appraisal, and evidence-to-design translation to derive seven decision domains and a strategy-to-execution cycle. Initial ex ante formative evaluation combines analytical traceability mapping and literature-grounded scenario demonstration in representative operations management contexts to assess clarity, completeness, internal consistency, actionability, and boundary conditions. The resulting framework offers a rigorous and practical blueprint for scalable and trustworthy AI-enabled operations while clarifying where future expert and field validation are still required.

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