Advanced analytics empowered by Artificial Intelligence (AI) contributes to the achievement of global sustainability and business goals. It will also contribute to global competitiveness of enterprises through enablement of fact-based decisionmaking and improved insight. The digitalisation process currently ongoing in industry, and the corresponding implementation of AI technologies, requires availability and accessibility of data and models. Data and models are considered as digital assets (ISO55K) that impact a system’s dependability during its whole lifecycle. Digitalisation and implementation of AI in complex technical systems such as found in railway, mining, and aerospace industries is challenging. From a digital asset management perspective, the main challenges can be related to source integration, content processing, and cybersecurity.

However, to effectively and efficiently retain the required performance of a complex technical system during its lifecycle, there is a need of appropriate concepts, methodologies, and technologies. With this background, Luleå University of Technology, in cooperation with a number of Swedish railway stakeholders – fleet managers, railway undertakings, infrastructure managers and Original Equipment Manufacturers (OEM), has created a universal platform called ‘the AI Factory’ (AIF). The concept of AIF has further been specialised for railway industry, so called AI Factory for Railway (AIF/R).

Hence, this paper aims to provide a description of findings from the development and implementation of ‘AI Factory (AIF)’ in the railway context. Furthermore, the paper provides a case-study description used to verify the developed technologies and methodologies within AIF/R.