The concept of Hybrid Intelligence (HI) is frequently used interchangeably with Human-Centered AI (HCAI) and more broadly as human-in-the-loop. Dellerman et al. [1] outlined three differentiation criteria, emphasizing in particular the need for an evolving continuum of human-AI learning, a concept that has proven challenging to operationalize effectively. Recent efforts aim to expand the definition of HI beyond the domain of human-computer interaction to include application-oriented insights from management science [2]. This broader perspective integrates vital components such as facilitating end-user co-creation through narrative frameworks that foster psychological safety by addressing fears of job displacement [3,4], mitigating risks of deskilling during system deployment and scaling [5], and supporting business process innovation [2]. Additionally, in contrast to HCAI, the name hybrid intelligence conveys the possibly symmetric human-machine relationship and thereby preserves some of the disruptive potential of automated AI rather than relying on purely augmentation of human tasks and intentions [3]. Explicitly, the HI interaction should not only augment the existing, predefined task but also support aspects such as (business) process and business model re-engineering. Despite these considerations, a thorough discussion on which of the many established HCAI concepts and design guidelines form crucial components in achieving the aims of HI has so far been absent in literature. In particular, as it is becoming more and more likely that most knowledge workers will within a short timeframe become operators of complex virtual assistants tapping into LLMs and natural language interfaces, it becomes urgent to ensure that the human-ai interface and associated narrative is constructed to support HI principles and objectives. To initiate this discussion, we formulate explicitly updated HI design criteria in particular for generative AI virtual assistant design and discuss relevant HCAI concept.