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This conceptual chapter aims to understand the role of artificial intelligence (AI) in value co-creation phenomena in a healthcare service ecosystem, through a literature review and the definition of a conceptual framework. AI, as an operant resource, can stimulate a completely patient-centered, adaptive and resilient healthcare system, and governance models in healthcare based on data-driven decision-making (DDDM), ensuring faster choices, more timely diagnosis and more personalized treatment paths. However, the full implementation of AI in healthcare is inhibited by some frictions, mainly related to the risk that the AI black box may generate an inadequate automatic decision, also due to the quality of data used, often partial and unstructured given the reluctance to share them by patients concerned by privacy threats. The co-design (multi-part and multi-level) of a predictive decision model based on the functional transparency of the AI algorithm would allow for augmented decision as result of an effective human–machine interaction. Healthcare actors could thus make decisions using the information detected by the software (based on clear cause-and-effect correlations and modifiable variables in case of mistakes), integrated with their professional knowledge. This would also help to strengthen the patient’s perception of the decision’s reliability and accuracy and the safety of the tool (factors that can affect his/her trust). AI may be considered as a driver for value co-creation in healthcare, thanks to transparency. It would allow the promotion of collaborative behaviors involving actors by generating new institutions and new resource integration practices among them.

Disruptive technologies are accelerating global growth. Artificial intelligence (AI) has the potential to transform the idea of delivering value to end users. On the other hand, the growth of Industry 5.0 has given rise to the concept of humanizing technology, and AI is a promising technology with the potential to contribute to business success. Nevertheless, the idea of value creation in the field of AI is novel, so it is necessary to define the meaning of value by understanding the context of AI applicability in different environments and industries. In this chapter, the author uses the Scientific Procedures and Rationales for Systematic Literature Reviews (SPAR-4-SLR) procedure to conduct an SLR that provides interesting insights into the focus, industries, and methodologies and approaches used in existing research. Following the initial literature review on the state of the art of AI and value creation, the author also offers a reflection on the strategic implications of AI in the field of marketing, postulating a macrovalue creation framework that addresses the existence of implications on three different levels: emerging markets, Sustainable Development Goals, and adoption issues. Therefore, this chapter examines the value creation perspectives of AI to understand the current research focus and future directions.

The COVID-19 pandemic stressed the health care sector's longstanding pain points, including the poor quality of frontline work and the staffing challenges that result from it. This has renewed interest in technology-centered approaches to achieving not only the “Triple Aim” of reducing costs while raising access and quality but also the “Quadruple Aim” of doing so without further squeezing wages and abrading job quality for frontline workers.

How can we leverage technology toward the achievement of the Quadruple Aim? I view this as a “grand challenge” for health care managers and policymakers. Those looking for guidance will find that most analyses of the workforce impact of technological change consider broad classes of technology such as computers or robots outside of any particular industry context. Further, they typically predict changes in work or labor market outcomes will come about at some ill-defined point in the medium to long run. This decontextualization and detemporization proves markedly problematic in the health care sector: the nonmarket, institutional factors driving technology adoption and implementation loom especially large in frontline care delivery, and managers and policymakers understandably must consider a well-defined, near-term, i.e., 5–10-year, time horizon.

This study is predicated on interviews with hospital and home health agency administrators, union representatives, health care information technology (IT) experts and consultants, and technology developers. I detail the near-term drivers and anticipated workforce impact of technological changes in frontline care delivery. With my emergent prescriptions for managers and policymakers, I hope to guide sectoral actors in using technology to address the “grand challenge” inherent to achieving the Quadruple Aim.

This chapter uses the case of Tay as a basis for exploring Artificial Intelligence (AI) and its implications for corporate social responsibility (CSR). We explore issues with AI in relation to two of Roome's (2005) CSR agendas – responsible business practices and consumer responsibility. Then, we build a framework for approaching AI that connects user and designer perspectives, pointing out key concepts and opportunities for public relations (PR) professionals to engage with both uses for and development of AI in the workplace. We point out the ability of AI, as a technology, to turn the action of connecting to publics from a front office to a back office endeavor. Also, we advocate for PR's need, as a field, to rethink potential changes resulting from the integration of AI into organizations which can affect PR practice at a fundamental level. Finally, we propose ways for PR practitioners, educators, and researchers to consider integrating this understanding of AI into their work.

Smart scenarios related to industries or cities, characterized by intensive technology transfer and use of innovative and disruptive technologies, have been in the spotlight either on academic or organizational discussions, especially those with a technocentric focus. Among these technologies, artificial intelligence (AI) emerges as the most challenging one due to its complexity. Therefore, this chapter aims to address AI, in particular the future of the labor market, exploring the challenges regarding the skills required in the context of AI technology, addressing its uses, challenges, and benefits. In order to achieve this goal, a systematic review was conducted on the extant literature using the methodology Methodi Ordinatio. The results show that the current literature is gradually changing from a more critical and negative view of AI to a more optimistic one, with more positive approaches and expectations regarding its benefits. As practical implications, the findings can be used as a guide for governments to develop strategies aiming to deal with upcoming challenges, especially regarding future jobs and employability.

It is important to implement artificial intelligence (AI) because it can simplify and solve complex problems faster than humans. Because AI learns about people and their behavior from the first purchase, AI marketing can boost marketing efforts by leveraging data to target extremely precise consumer groups. There is a debate about the efficacy of AI marketing due to the constraints and limits imposed by the system's nature. This chapter presents insights from published studies regarding the relationship of AI with marketing and how AI can affect marketing. A real-world example of Netflix's usage of AI in marketing has been demonstrated. Then, consumer attitudes regarding AI were revealed. Then, several ethical considerations concerning AI were highlighted. Finally, the anticipated future of AI marketing was addressed. This chapter demonstrated the significance of firms implementing AI marketing to get a competitive advantage. Although some of the difficulties mentioned in this study need to be resolved, AI marketing has a bright future. There are ethical concerns about bias and privacy that should be addressed further. This chapter will encourage firms to use AI systems in marketing, and it will open the door to concerns that will need to be investigated academically in the future.

Organizations across industries are increasingly using Artificial Intelligence (AI) systems to support their innovation processes, supply chains, marketing and sales and other business functions. Implementing AI, firms report efficiency gains from automation and enhanced decision-making thanks to more relevant, accurate and timely predictions. By exposing the benefits of digitizing everything, COVID-19 has only accelerated these processes. Recognizing the growing importance of AI and its pervasive impact, this chapter defines the “social value of AI” as the combined value derived from AI adoption by multiple stakeholders of an organization. To this end, we discuss the benefits and costs of AI for a business-to-business (B2B) firm and its internal, external and societal stakeholders. Being mindful of legal and ethical concerns, we expect the social value of AI to increase over time as the barriers for adoption go down, technology costs decrease, and more stakeholders capture the value from AI. We identify the contributions to the social value of AI, by highlighting the benefits of AI for different actors in the organization, business consumers, supply chain partners and society at large. This chapter also offers future research opportunities, as well as practical implications of the AI adoption by a variety of stakeholders.