In the forthcoming years, the role of advanced automation-technologies, including Artificial Intelligence (AI), is going to increase at a fast pace in complex socio-technical systems. This fully applies to the Air Traffic Management (ATM), where advanced automation is a key to reduce the workload of controllers in future solutions. Anyway, in mid- and even long-term time horizons, cognitive aspects should be pursued, where ATM systems gain a high level of autonomy while remaining human-centric. Thus, research is needed to develop a more agile and holistic approach to support the certification of advanced-automation ATM solutions, based on a human-centred view. In this context, the HUCAN (Holistic Unified Certification Approach for Novel systems based on advanced automation) project is exploratory research to develop a methodology and a set of guidelines for the development of innovative ATM solutions based on advanced automation and AI, supporting their certification since the early stages of concept design. In detail, this work analyses the certification challenges concerning the level-of-automation assessment by applying a case-study-based approach. The selected case studies cover several ATM aspects and involve different technologies and kinds of algorithms, including an AI-powered digital assistant in terminal manoeuvring area. The proposed work describes the case studies, and assesses their level of automation using a functional automation-related approach. A proper taxonomy is applied for the classification of automation levels. Lastly, the work presents some preliminary arguments underlying gaps and challenges within the current classification of advanced-automation levels.