Although data-driven artificial intelligence (AI) is increasingly applied in decision-making, challenges such as a lack of explainability and trust limit its integration in enterprise decisandrisks.Keytaion-making processes. Establishing a minimum level of common information sharing and trust among decision-making stakeholders, often termed as decisional interoperability, is needed for industry adoption. Purely data-driven approaches risk ignoring the enterprise environment and situational context of decisions and are insufficient for such interoperability to the extent that the decision-making rationale is opaque. While linked data and knowledge approaches have long been pursued in the context of data-driven machine learning, these have not been particularly well explored in the context of decisional enterprise interoperability. This paper aims to narrow this gap. It explores how the introduction of AI is changing decisional interoperability concerns. It then outlines patterns of human-AI teaming in decision-making, as well as methods, such as knowledge-infused AI and mechanisms, such as active learning, for enhancing decisional interoperability. Three diverse application domain-cases offer context for an analysis of AI decision-making considerations and decisional interoperability. The paper concludes with arguments about how integration of knowledge into data-driven AI contributes to decisional interoperability and further work needed in this area.