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Innovation adoption is challenging at both intra-organizational and interorganizational levels. Several decades of innovation adoption research have identified various barriers at both levels. Intra-organizational barriers are often related to the characteristics of the innovation, adopters, managers, environment, and ecosystem but can also include an incompatibility with an organization’s strategy, structural impediments, organizational resource constraints, a lack of fit of the innovation with an organizational culture and climate, decision making challenges, a lack of integration with an organization’s knowledge management, human resource management practices, dynamic capabilities, and active innovation resistance from customers. Interorganizational barriers include uncertainty with learning and implementation, the distributed nature of the innovation process, differences in production systems, disparities in regulatory systems, variation within local contexts, and the nature of embedded knowledge adopted in diverse organizational contexts. One of the key missing aspects in understanding innovation adoption is how extant practices within an organizational or interorganizational context enhance or hinder innovation adoption. Although the practices of innovation adoption emerge and evolve dynamically, existing research does not highlight fine-grained practices that lead to its success or failure. A practice lens focuses on people’s recurrent actions and helps to understand social life as an ongoing production that results from these actions. The durability of practices results from the reciprocal interactions between agents and structures that are embedded within daily routines. A practice lens allows us to study practices from three different perspectives. The first perspective, empirically explores how people act in organizational contexts. The second, a theoretical focus investigates the structure of organizational life. This perspective also delves into the relations between the actions that people take over time and in varying contexts. Finally, the third perspective which is a philosophical one focuses on how practices reproduce organizational reality. By focusing on the unfolding of constellations of everyday activities in relation to other practices within and across time and space, a practice lens hones in on everyday actions. Everyday actions are consequential in producing the structural contours of social life. A practice lens emphasizes what people do repeatedly and how those repetitive actions impact the social world. A practice theory lens also challenges the assumption that things are separable and independent. Instead, it focuses on relationality of mutual constitution to understand how one aspect of the issue creates another aspect. Relationality of mutual constitution is the notion that things such as identities, ideas, institutions, power, and material goods take on meaning only when they are enacted through practices instead of these being innate features of these things Focusing on duality forces us to address the assumptions that underlie the separation. A practice perspective on innovation adoption highlights the concepts of duality, dynamics, reciprocal interactions, relationality, and distributed agency to inform both the theory and practice of innovation adoption. Understanding these concepts enables a practice lens for successful adoption of innovations that impact organizational and societal outcomes, such as economic development, productivity enhancement, entrepreneurship, sustainability, equity, health, and other economic, social, and environmental changes.

Artificial intelligence (AI), commonly defined as “a system’s ability to correctly interpret external data, to learn from such data, and to use those learnings to achieve specific goals and tasks through flexible adaptation,” can be classified into analytical, human-inspired, and humanized AI depending upon its application of cognitive, emotional, and social intelligence. AI’s foundations took place in the 1950s. A sequence of vicissitudes of funding, interest in, and support for AI followed subsequently. In 2015 AlphaGo, Google’s AI-driven system, won against the human grandmaster in the highly complex board game Go. This is considered one of the most significant milestones in the development of AI and marks the starting of a new period, enabling several AI innovations in a variety of sectors and industries. Higher education, the fashion industry, and the arts serve as illustrations of areas wherein ample innovation based on AI occurs. Using these domains, various angles of innovation in AI can be presented and decrypted. AI innovation in higher education, for example, indicates that at some point, AI-powered robots might take over the role of human teachers. For the moment, however, AI in academia is solely used to support human beings, not to replace them. The apparel industry, specifically fast fashion—one of the planet’s biggest polluters—shows how innovation in AI can help the sector move toward sustainability and eco-responsibility through, among other ways, improved forecasting, increased customer satisfaction, and more efficient supply chain management. An analysis of AI-driven novelty in the arts, notably in museums, shows that developing highly innovative, AI-based solutions might be a necessity for the survival of a strongly declining cultural sector. These examples all show the role AI already plays in these sectors and its likely importance in their respective futures. While AI applications imply many improvements for academia, the apparel industry, and the arts, it should come as no surprise that it also has several drawbacks. Enforcing laws and regulations concerning AI is critical in order to avoid its adverse effects. Ethics and the ethical behavior of managers and leaders in various sectors and industries is likewise crucial. Education will play an additional significant role in helping AI positively influence economies and societies worldwide. Finally, international entente (i.e., the cooperation of the world’s biggest economies and nations) must take place to ensure AI’s benefit to humanity and civilization. Therefore, these challenges and areas (i.e., enforcement, ethics, education, and entente) can be summarized as the four summons of AI.