Digital twin (DT) emerges as a key concept of the Industry 4.0 paradigm and beyond. However, the current literature lacks focus on humans and human activities as a part of complex system DTs. Acknowledging human aspects in DTs can enhance work performance, well-being, motivation, and personal development of professionals. This study examines emerging requirements for human digital twins (HDTs) in three use cases of industry–academia collaboration on complex systems. The results draw together the overall design problem and four design objectives for HDTs. We propose to combine the machine and human-related aspects of DTs and highlight the need for virtual-to-virtual interoperability between HDTs and machines alike. Furthermore, we outline differences between humans and machines regarding digital twinning by addressing human activities and knowledge-based behavior on systems. Design of HDTs requires understanding of individual professional characteristics, such as skills and information preferences, together with twinning between the physical and digital machine entities and interactions between the human and machine DTs. As the field moves toward including humans as a part of the DT concept, incorporating HDTs in complex systems emerges as an increasingly significant issue.