In this contribution, we briefly examine the role of end users in the evaluation and characterisation of sophisticated AI-based systems, such as autonomous vehicles or near-future robots. Indeed, when trying to ensure the safety of learning, perception and control in real world settings, one aspect that needs consideration is that human end users are often part of such settings. We argue that current approaches for considering end users in this respect are insufficient, not the least from a safety perspective, and that this insufficiency will become more acute when transitioning to neuromorphic and/or strongly cognitively inspired solutions. We demonstrate this by borrowing examples from the field of enactivism, which demonstrate that human end users might change the system dynamics of advanced neuromorphic systems when interacting with them, which needs to be taken into consideration. Enactivism might also provide clues as to how to design future evaluation metrics for human-machine teams.