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The AI Integrability Readiness Index (AIRI), introduced in this paper, constitutes a novel systematic methodology for quantifying Artificial Intelligence integration potential within legacy industrial equipment and production systems, aligned with the human-centric imperatives of Industry 5.0. Distinctively, while established indices-such as AI Singapore's AIRI (assessing national AI maturity) or the general AI Readiness Index (evaluating organizational strategy and talent)-operate at organizational levels, the proposed AIRI addresses a critical assessment gap by targeting individual industrial assets. This equipment-level framework evaluates AI integrability through seven specialized technical domains: Data Acquisition, Control and Interoperability, Predictive Maintenance Readiness, Hardware Adaptability, Cybersecurity, Energy Efficiency, and Human-AI Interaction. Through weighted aggregation, the methodology classifies equipment into four distinct readiness categories: Legacy, AI-Ready, AI-Integrable, or AI-Native. Comprehensive validation across heterogeneous industrial environments-encompassing automotive, chemical, metal products, and food processing sectors-confirms the framework's robustness and cross-sectoral applicability as a standardized diagnostic instrument. The AIRI provides actionable, domain-specific insights enabling engineers and managers to strategically plan modernization initiatives, optimize technological upgrades, and maximize AI investment returns within existing operational infrastructures. This contribution advances the theoretical understanding of equipment-level AI readiness while offering practical decision-support capabilities for industrial digital transformation.

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AIRI–A Methodology for Evaluating AI Integrability in Classical Industrial Equipment

  • 会议论文

  • 发表日期：

    2026年

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  • Alexandru Moldovan,
  • David Cherecheș,
  • Grigore Pop,
  • Mircea Murar,
  • Mircea Fulea,
  • Cristian Moldovan,
  • Bogdan Mocan

摘要

The AI Integrability Readiness Index (AIRI), introduced in this paper, constitutes a novel systematic methodology for quantifying Artificial Intelligence integration potential within legacy industrial equipment and production systems, aligned with the human-centric imperatives of Industry 5.0. Distinctively, while established indices-such as AI Singapore's AIRI (assessing national AI maturity) or the general AI Readiness Index (evaluating organizational strategy and talent)-operate at organizational levels, the proposed AIRI addresses a critical assessment gap by targeting individual industrial assets. This equipment-level framework evaluates AI integrability through seven specialized technical domains: Data Acquisition, Control and Interoperability, Predictive Maintenance Readiness, Hardware Adaptability, Cybersecurity, Energy Efficiency, and Human-AI Interaction. Through weighted aggregation, the methodology classifies equipment into four distinct readiness categories: Legacy, AI-Ready, AI-Integrable, or AI-Native. Comprehensive validation across heterogeneous industrial environments-encompassing automotive, chemical, metal products, and food processing sectors-confirms the framework's robustness and cross-sectoral applicability as a standardized diagnostic instrument. The AIRI provides actionable, domain-specific insights enabling engineers and managers to strategically plan modernization initiatives, optimize technological upgrades, and maximize AI investment returns within existing operational infrastructures. This contribution advances the theoretical understanding of equipment-level AI readiness while offering practical decision-support capabilities for industrial digital transformation.