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Refereed/Peer-reviewed Many industry sectors have been pursuing the adoption of Industry 4.0 (I4.0) ideas and technologies, which promise to realize lean and just-in-time production through digitization and the use of smart machines. This shift is driven by technological advances, including Artificial Intelligence (AI) and machine learning, sensor networks and Internet of Things technologies, cloud computing, additive manufacturing, and the availability of large amounts of data that can be exploited by these technologies. However, the adoption of AI technologies for I4.0 varies considerably among industry sectors. This article complements broader reviews of I4.0 by examining the specific applications of IAI in several industry sectors, highlighting the issues and concerns encountered in and across different industry sectors, and discussing potential solutions that have been introduced along with opportunities and challenges for adoption. In this article, we review the literature to identify common themes and concerns related to the adoption of AI technologies in the context of I4.0 in several industry sectors. AI solutions are discussed in the context of an AI adoption pipeline that spans data collection, processing, model construction, and interpretation of results. Our findings indicate that although different industries share common issues, the adopted solutions are often specific to a particular industry sector, which may be difficult to transfer to other sectors. Moreover, industry sectors may pursue different adoption strategies due to varying experience and maturity of AI practices. These findings may inform managers, practitioners, and decision-makers who are involved in the adaptation of Industry 4.0 transformation in their respective industry sectors.