An ongoing conversation in the Information Systems literature addresses the concern, "How can we conduct research that makes a difference?" A shortage of high-impact research will, over time, challenge the identity and weaken the viability of IS as an academic discipline. This paper presents the systematic high-impact research model (SHIR), an approach to conducting high-impact research. SHIR embodies the insight gained from three streams of high-impact research programs spanning more than 50 years. The SHIR framework rests on the proposition that IS researchers can produce higher-impact contributions by developing long-term research programs around major real-world issues, as opposed to ad hoc projects addressing a small piece of a large problem. These persistent research programs focus on addressing the entirety of an issue, by leveraging multidisciplinary, multiuniversity research centers that employ a breadth of research methods and large-scale projects. To function effectively, SHIR programs must be sustained by academic and practitioner partnerships, research centers, and outreach activities. We argue that SHIR research programs increase the likelihood of high impact research.

AI is a phenomenon that does not only manifest itself as a disruptive technology, but also entails a paradigmatic shift with conceivably unprecedented and unforeseeable consequences for society and the human condition. This technology with its components Big Data and Machine Learning/algorithms has evoked critical reactions because of ethical concerns that follow on the backwash of its ubiquitous and ever more expanding use. Whose voices are presently raised and what contributions can actually be expected from the information systems (IS) discipline – an academic field that is prone to scrutinize phenomena that emerge when the social and technological system interact? By reviewing a systematically defined selection of the latest IS research literature and analyzing the critical perspectives, this study displays current themes in the IS discipline concerning critique of AI. The accumulation and clustering of the results can provide an answer to the question of what themes appear in the critique of AI in the IS discipline and whether a critical standpoint towards detrimental effects of AI use in society is formed. Social critique, which has no longstanding tradition in IS research, would be necessary to succeed in offering a well-informed and science-based guidance on how to face AI as a society and with the human condition as a focal point.