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.

翻译：在工业4.0中，信息物理系统（CPS）产生海量数据集，可被人工智能（AI）用于预测性维护和生产规划等应用。然而，尽管人工智能已展现出巨大潜力，其在制造业等领域的广泛应用仍受到限制。我们对近期文献（包括标准与报告）的全面综述，明确了以下关键挑战：系统集成、数据相关问题、管理人力相关顾虑以及确保可信人工智能。定量分析特别突出了对从业者至关重要但学术界尚未充分研究的挑战与议题。本文简要讨论了针对这些挑战的现有解决方案，并提出了未来研究方向。我们希望本综述能为评估CPS中人工智能成本效益影响的从业者，以及致力于解决这些紧迫挑战的研究人员提供参考资源。