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Artificial Intelligence in Industry 4.0: A Review of Integration Challenges for Industrial Systems

Alexander Windmann, Philipp Wittenberg, Marvin Schieseck, Oliver Niggemann

TL;DR

This paper surveys the integration of Artificial Intelligence in Industry 4.0 CPS, addressing the gap between AI potential and actual adoption in manufacturing. It systematically maps 16 challenges into four themes—system integration, data, workforce, and trustworthy AI—and conducts a quantitative analysis across 55 sources to identify where academic focus is concentrated. The study highlights data quality/availability and trustworthy AI as the most addressed areas, while noting gaps in system integration and workforce issues, and proposes mitigations such as AutoML, transfer learning, federated learning, and standardization to guide practice. The findings offer actionable guidance for practitioners and researchers, emphasizing data availability, robust quality assurance, and clearer standards to enable safer, compliant, and scalable AI deployment in industrial settings.

Abstract

In Industry 4.0, Cyber-Physical Systems (CPS) generate vast data sets that can be leveraged by Artificial Intelligence (AI) for applications including predictive maintenance and production planning. However, despite the demonstrated potential of AI, its widespread adoption in sectors like manufacturing remains limited. Our comprehensive review of recent literature, including standards and reports, pinpoints key challenges: system integration, data-related issues, managing workforce-related concerns and ensuring trustworthy AI. A quantitative analysis highlights particular challenges and topics that are important for practitioners but still need to be sufficiently investigated by academics. The paper briefly discusses existing solutions to these challenges and proposes avenues for future research. We hope that this survey serves as a resource for practitioners evaluating the cost-benefit implications of AI in CPS and for researchers aiming to address these urgent challenges.

Artificial Intelligence in Industry 4.0: A Review of Integration Challenges for Industrial Systems

TL;DR

This paper surveys the integration of Artificial Intelligence in Industry 4.0 CPS, addressing the gap between AI potential and actual adoption in manufacturing. It systematically maps 16 challenges into four themes—system integration, data, workforce, and trustworthy AI—and conducts a quantitative analysis across 55 sources to identify where academic focus is concentrated. The study highlights data quality/availability and trustworthy AI as the most addressed areas, while noting gaps in system integration and workforce issues, and proposes mitigations such as AutoML, transfer learning, federated learning, and standardization to guide practice. The findings offer actionable guidance for practitioners and researchers, emphasizing data availability, robust quality assurance, and clearer standards to enable safer, compliant, and scalable AI deployment in industrial settings.

Abstract

In Industry 4.0, Cyber-Physical Systems (CPS) generate vast data sets that can be leveraged by Artificial Intelligence (AI) for applications including predictive maintenance and production planning. However, despite the demonstrated potential of AI, its widespread adoption in sectors like manufacturing remains limited. Our comprehensive review of recent literature, including standards and reports, pinpoints key challenges: system integration, data-related issues, managing workforce-related concerns and ensuring trustworthy AI. A quantitative analysis highlights particular challenges and topics that are important for practitioners but still need to be sufficiently investigated by academics. The paper briefly discusses existing solutions to these challenges and proposes avenues for future research. We hope that this survey serves as a resource for practitioners evaluating the cost-benefit implications of AI in CPS and for researchers aiming to address these urgent challenges.
Paper Structure (5 sections, 4 figures)

This paper contains 5 sections, 4 figures.

Table of Contents

  1. Introduction
  2. Literature Search Methodology
  3. Challenges
  4. Discussion
  5. Conclusion

Figures (4)

  • Figure 1: Identified challenges of integrating AI within industrial systems.
  • Figure 2: Number of publications per year included in this study (sample size $n=55$). The full list of references can be found in Table \ref{['tab:challengesA']}.
  • Figure 3: Proportion of addressed, mentioned, and not covered challenges over time (sample size $n=55$). 'Addressed' means explicit focus on the challenge, while 'mentioned' indicates brief reference to it or related issues.
  • Figure 4: Mosaic plot of the identified challenges presented in Table \ref{['tab:challengesA']} (sample size $n=55$). 'Addressed' means explicit focus on the challenge, while 'mentioned' indicates brief reference to it or related issues.