The complexity and rise of data in healthcare means that artificial intelligence (AI) will increasingly be applied within the field. Several types of AI are already being employed by payers and providers of care, and life sciences companies. The key categories of applications involve diagnosis and treatment recommendations, patient engagement and adherence, and administrative activities. Although there are many instances in which AI can perform healthcare tasks as well or better than humans, implementation factors will prevent large-scale automation of healthcare professional jobs for a considerable period. Ethical issues in the application of AI to healthcare are also discussed.

In the future, artificial intelligence (AI) is likely to substantially change both marketing strategies and customer behaviors. Building from not only extant research but also extensive interactions with practice, the authors propose a multidimensional framework for understanding the impact of AI involving intelligence levels, task types, and whether AI is embedded in a robot. Prior research typically addresses a subset of these dimensions; this paper integrates all three into a single framework. Next, the authors propose a research agenda that addresses not only how marketing strategies and customer behaviors will change in the future, but also highlights important policy questions relating to privacy, bias and ethics. Finally, the authors suggest AI will be more effective if it augments (rather than replaces) human managers.

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Analytics have been employed by companies for several decades, but now many firms are interested in building their capabilities for artificial intelligence (AI). Many AI systems, however, are based on statistics and other forms of analytics. Companies can get a "running start" on AI by building upon their analytical competencies. The focus of this article is how to transition from analytics to AI. Three eras of analytical focus are detailed, with AI portrayed as a fourth era. The types of AI methods that are and are not based on analytics are described. AI applications that build on analytical strengths are discussed. Approaches to assessing analytical capabilities that relate to AI, and the development of an organizational plan and strategy for AI, are also described in brief.

Enterprise systems packages have long been associated with process change. However, it was assumed that most organizations would simultaneously design and implement process change while implementing the systems. A survey of 163 organizations and detailed interviews with 28 more suggests that enterprise systems were still being implemented even among early adopters of the technology, and that process change was being undertaken on an ongoing basis. After the prerequisites of time, critical mass of functionality, and significant expenditures were taken care of, the factors most associated with achieving value from enterprise systems were integration, process optimization, and use of enterprise‐systems data in decision making.