The emergence of data as a critical asset and the prevalence of technologies such as the Artificial Intelligence of Things (AIoT) on the one hand, and the importance of collaborations for value creation on the other hand have given rise to a new breed of ecosystems known as data ecosystems. While data ecosystems provide new business opportunities, proposing and capturing value in those ecosystems is challenging, and the extant literature provides little guidance in this regard. Our research encompasses two studies that address this limitation and establish a framework for business-model archetypes in the context of AIoT data ecosystems. In the first study, exploratory qualitative research on 28 leading AIoT data ecosystem actors leads to the identification of value propositions and value-capture mechanisms in these ecosystems. We identify eight possible value propositions and eight possible value-capture mechanisms. The second, qualitative study centers on 19 expert interviews. Our analysis leads to the identification of two dimensions – control and customization – that guide the conceptualization and formation of business-model archetypes. Using these dimensions, we develop a framework for business-model archetypes in AIoT data ecosystems. Our findings contribute to the discourse on data ecosystems and offer new perspectives valuable for both researchers and industry practitioners.