mlsanityData to 7 Oct 2026

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

arXiv 2405.18580 ↗First posted 28 May 2024cs.AI cs.LGcited by 5

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Abstract (arXiv)

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.

Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Builds on 15 of its 82 references that are in the map. First posted on arXiv 28 May 2024.

Builds on

15 of 82 references.

References · 15

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3 more
  1. 2019Requirements Engineering Challenges in Building AI-Based Complex Systemscited by 21
  2. 2022SMPL: Simulated Industrial Manufacturing and Process Control Learning Environmentscited by 4
  3. 2023Robustness and Generalization Performance of Deep Learning Models on Cyber-Physical Systems: A Comparative Studycited by 2

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 1; 2025: 3; 2026: 1.

5 papers.

  1. 2025Evaluating Large Language Models for Real-World Engineering Taskscited by 7
  2. 2024Safeguarding AI Agents: Developing and Analyzing Safety Architecturescited by 3
  3. 2025Quantifying Robustness: A Benchmarking Framework for Deep Learning Forecasting in Cyber-Physical Systemscited by 1
  4. 2025Industrial AI Robustness Card for Time Series Modelscited by 0
  5. 2026Avionic Main Fuel Pump Simulation and Fault-Diagnosis Benchmarkcited by 0