Abstract Organizations collect growing volumes of data to extract value through analytics. However, this data growth creates challenges for effective data understanding, which forms the foundation for reliable decision-making and effective AI systems. Established analytics frameworks such as CRISP-DM and KDD acknowledge this importance but provide limited guidance to achieve this understanding, particularly for data-centric AI requiring collaboration across stakeholder groups. To address this gap, the authors conducted a systematic literature review, developing a five-dimensional framework for data understanding. They then performed a systematic mapping study analyzing how existing methods support these dimensions and accommodate different target audiences. The analysis reveals critical gaps in current methods, particularly in systematically supporting the understanding of data collection and contextualization. While most methods target data experts, the authors find a notable lack of methods supporting domain experts and decision-makers. This research advances both theoretical understanding by identifying the key dimensions that constitute data understanding and practical implementation by providing organizations with guidance on building data understanding.

Abstract As the real-world use of Artificial intelligence (AI) becomes increasingly pervasive, the interest of organizations in the nascent technology is currently at its peak. Although the scientific literature points out that a strategy is key to responding to technological breakthroughs, the three facets of autonomy, learning, and inscrutability that distinguish contemporary AI from previous generations of IT give rise to a novel and distinctive perspective on strategy. Particularly, the facets of contemporary AI lead to AI-induced market and resource shifts and, thus, to AI-related strategic challenges regarding the scope, scale, speed, and source from which organizations make strategic deliberations. This ultimately requires a strategic response from organizations in the form of an AI strategy. Against this backdrop, this study proposes a multi-layer taxonomy with 15 dimensions and 45 characteristics that unveils how organizations currently structure and organize an AI strategy. Conducting a cluster analysis on this foundation, this study further provides four clusters that delineate predominant design options for developing a new AI strategy or evaluating an existing one. In this way, the results contribute to a fundamental understanding of the design space of an AI strategy and enrich recent discussions among researchers and practitioners on how to advance the real-world use of AI.

Abstract The success of palliative care requires collaboration among multiple professions within a sensibly digitized work system. The diverse perspectives and expertise of team members inform their collective endeavor, often leading to differing interpretations and priorities in patient care. This diversity necessitates a continual exchange of knowledge and information. Current technologies, including the hospital information system, do not foster such collaboration, particularly in palliative care. This study explores digital enhancements that can promote multi-professional collaboration (MPC). The authors employed action design research and used a work system theory lense to examine the palliative care work systems in two hospital wards in Germany. Through extensive on-site observations and interventions with practitioners, the study identified challenges that arose during MPC. This paper presents the proposed organizational and technical solutions. The paper provides design principles and guidelines for a collaboration support system to maximize MPC. Theoretical contributions include insights into the challenges of MPC and design knowledge about collaboration support. This work can inform practitioners about common challenges and offers potential solutions and guidance for implementing a collaboration support system.