Heimberger, HeidiHeidiHeimbergerHorvat, DjerdjDjerdjHorvatSchultmann, FrankFrankSchultmann2023-02-232023-02-232023https://publica.fraunhofer.de/handle/publica/43698710.1007/978-3-031-18641-7_24Due to its high potential to perform many tasks faster, more accurately and in greater detail than humans artificial intelligence (AI) has been attracting growing attention across industries. In manufacturing, AI, in combination with digital sensors, networks and software-based automation, defines a new industrialization age. The integration of AI into production processes promises to boost the productivity, efficiency, as well as the automation of processes. However, AI adoption in manufacturing is currently still in its early stage and lacks practical experiences. This raises the question, to which extent manufacturing companies are ready to implement AI. While approaches to assessing the maturity in terms of the digitalization or Industry 4.0 (I4.0) of manufacturing companies are well established and discussed in the literature, approaches that specifically address AI in manufacturing are still lacking. To address this gap, we present an approach to analyze and monitor the readiness of manufacturing firms for working with AI technologies. In accordance with the existing assessment concepts of digitalization and I4.0, our approach examines different areas of digital technologies on the product and production level of manufacturing firms. Moreover, it incorporates the key foundation for AI-security and data - into a conceptual model. We generally assume that companies need to achieve a certain level of digital readiness in three key dimensions in order to be ready for implementing AI-based technologies. We operationalize these dimensions through a variety of product- and production-specific as well as data- and safety-related indicators. In order to illustrate the implementation of our concept in practical terms, we present the results of the readiness assessment of two German manufacturing companies.enArtificial intelligenceReadinessManufacturingconference paper