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Artificial intelligence (AI) is deemed to have a significant impact as a value driver for the firms and help them get an operational and competitive advantage. However, there exists a lack of understanding of how to appropriate value from this nascent technology. This paper aims to discuss the approaches toward knowledge and innovation strategies to fill this gap.

The discussion presents a review of the extant strategy and information systems literature to develop a strategy for organizational learning and value appropriation strategy for AI. A roadmap is drawn from ambidexterity and organizational learning theories.

This study builds the link between learning and ambidexterity to propose paths for exploration and exploitation of AI. The study presents an ambidextrous approach toward innovation concerning AI and highlights the importance of developing as well as reusing the resources.

This study integrates over three decades of strategy and information systems literature to answer questions about value creation from AI. The study extends the ambidexterity literature with contemporary.

This study could help practitioners in making sense of AI and making use of AI. The roadmap could be used as a guide for the strategy development process.

This study analyzes a time-tested theoretical framework and integrates it with futuristic technology in a way that could reduce the gap between intent and action. It aims to simplify the organizational learning and competency development for an uncertain, confusing and new technology.

Artificial Intelligence (AI) has revolutionised the tourism industry, offering personalised experiences and streamlining operations. AI provides customised recommendations for travellers through data analysis and machine learning, making their journeys more meaningful. It has also improved efficiency through automated processes, chatbots and enhanced security measures. AI's ability to analyse large volumes of data enables tourism organisations to make data-driven decisions and target their marketing strategies effectively. One of the most notable contributions of AI in tourism is its ability to offer personalised recommendations. By analysing vast travel history, preferences and online behaviour, AI systems can provide tailored suggestions for destinations, accommodations, activities and dining options. This level of customisation enhances the overall travel experience, making it more relevant and satisfying for individual travellers. AI has also greatly improved operational efficiency within the tourism sector. Chatbots, powered by natural language processing, are increasingly being deployed by hotels, airlines and travel agencies to provide instant customer support and assistance. These chatbots can answer queries, offer recommendations and handle booking processes, reducing waiting times and enhancing customer satisfaction. In addition, facial recognition technology allows for quick and accurate identity verification at airports, hotels and other travel-related facilities. This improves security and provides travellers with a seamless and efficient experience. As technology advances, we expect AI to play a more prominent role in augmented reality, voice recognition and virtual assistants, further enhancing the travel experience and facilitating seamless interactions. In conclusion, AI has transformed the tourism industry by providing personalised recommendations, improving operational efficiency, enhancing security measures and enabling data-driven destination management.

As technology becomes more sophisticated, there is an increasing demand for flexible operating systems that can automatically respond to emerging challenges. The concept of artificial intelligence (AI) is, therefore, increasingly integral to many of the most exciting technological developments occurring today.

This is a literature review.

Broadly defined, AI is the aspect of computing that enables the machine to take in information about its environment and make choices that help it succeed. This paper will explore some of the key concepts behind AI and demonstrate how they apply to emerging technology.

To get a sense of the importance of AI, we need look no further than Google. Although many companies are working on AI, Google is of particular interest to libraries because its mission to “organize the world’s information and make it universally accessible and useful” shares many similarities with libraries.

AI has been contemplated for as long as humans have considered the nature of their own intelligence and pondered how artificial beings might be able to accomplish similar tasks. The idea of AI can be seen as far back as the ancient Greek myth of Galatea. Philosophers and scientists from René Descarte to Alan Turing have posed questions that are still relevant to modern AI.

This paper aims to examine the feasibility of artificial intelligence (AI) performing as chief executive officer (CEO) in organizations.

The authors followed an explorative research design – classic grounded theory methodology. The authors conducted face-to-face interviews with 27 participants that were selected according to theoretical sampling. The sample consisted of academics from the fields of AI, philosophy and management; experts and artists performing in the field of AI and professionals from the business world.

As a result of the grounded theory process “The Vizier-Shah Theory” emerged. The theory consisted of five theoretical categories: narrow AI, hard problems, debates, solutions and AI-CEO. The category “AI as a CEO” introduces four futuristic AI-CEO models.

This study introduces an original theory that explains the evolution process of narrow AI to AI-CEO. The theory handles the issue from an interdisciplinary perspective by following an exploratory research design – classic grounded theory and provides insights for future research.

In spite of the merits of artificial intelligence (AI) in marketing and social media, harm to consumers has prompted calls for AI auditing/certification. Understanding consumers’ approval of AI certification entities is vital for its effectiveness and companies’ choice of certification. This study aims to generate important insights into the consumer perspective of AI certifications and stimulate future research.

A literature and status-quo-driven search of the AI certification landscape identifies entities and related concepts. This study empirically explores consumer approval of the most discussed entities in four AI decision domains using an online experiment and outline a research agenda for AI certification in marketing/social media.

Trust in AI certification is complex. The empirical findings show that consumers seem to approve more of non-profit entities than for-profit entities, with the government approving the most.

The introduction of AI certification to marketing/social media contributes to work on consumer trust and AI acceptance and structures AI certification research from outside marketing to facilitate future research on AI certification for marketing/social media scholars.

For businesses, the authors provide a first insight into consumer preferences for AI-certifying entities, guiding the choice of which entity to use. For policymakers, this work guides their ongoing discussion on “who should certify AI” from a consumer perspective.

To the best of the authors’ knowledge, this work is the first to introduce the topic of AI certification to the marketing/social media literature, provide a novel guideline to scholars and offer the first set of empirical studies examining consumer approval of AI certifications.

The purpose of this paper is to review and analyze how the development and utilization of artificial intelligence (AI) technologies for social responsibility are defined in the national AI strategies of the USA, Russia and China.

The notion of responsibility concerning AI is currently not legally defined by any country in the world. The authors of this research are going to use the methodology, based on Luciano Floridi’s Unified framework of five principles for AI in society, to determine how social responsibility is implemented in the AI strategies of the USA, Russia and China.

All three strategies for the development of AI in the USA, Russia and China, as evaluated in the paper, contain some or other components aimed at achieving public responsibility and responsible use of AI. The Unified framework of five principles for AI in society, developed by L. Floridi, can be used as a viable assessment tool to determine at least in general terms how social responsibility is implied and implemented in national strategic documents in the field of AI. However, authors of the paper call for further development in the field of mutually recognizable ethical models for socially beneficial AI.

This study allows us to better understand the linkages, overlaps and differences between modern philosophy of information, AI-ethics, social responsibility and government regulation. The analysis provided in this paper can serve as a basic blueprint for future attempts to define how social responsibility is understood and implied by government decision-makers.

The analysis provided in the paper, however general and empirical it may be, is a first-time example of how the Unified framework of five principles for AI in society can be applied as an assessment tool to determine social responsibility in AI-related official documents.

To help workers make the right decision, over the years, technological solutions and workplace learning analytics systems have been designed to aid this process (Ruiz-Calleja et al., 2019). Recent developments in artificial intelligence (AI) have the potential to further revolutionise the integration of human and artificial learning and will impact human and machine collaboration during team work (Seeber et al., 2020).

Complex problem-solving has been identified as one of the key skills for the future workforce (Hager and Beckett, 2019). Problems faced by today's workforce emerge in situ and everyday workplace learning is seen as an effective way to develop the skills and experience workers need to embrace these problems (Campbell, 2005; Jonassen et al., 2006).

In this commentary the authors argue that the increased digitization of work and social interaction, combined with recent research on workplace learning analytics and AI opens up the possibility for designing automated real-time feedback systems capable of just-in-time, just-in-place support during complex problem-solving at work. As such, these systems can support augmented learning and professional development in situ.

The commentary reflects on the benefits of automated real-time feedback systems and argues for the need of shared research agenda to cohere research in the direction of AI-enabled workplace analytics and real-time feedback to support learning and development in the workplace.

The study aims to analyze the synergy of artificial intelligence (AI), with scientometrics, webometrics and bibliometrics to unlock and to emphasize the potential of the applications and benefits of AI algorithms in these fields.

By conducting a systematic literature review, our aim is to explore the potential of AI in revolutionizing the methods used to measure and analyze scholarly communication, identify emerging research trends and evaluate the impact of scientific publications. To achieve this, we implemented a comprehensive search strategy across reputable databases such as ProQuest, IEEE Explore, EBSCO, Web of Science and Scopus. Our search encompassed articles published from January 1, 2000, to September 2022, resulting in a thorough review of 61 relevant articles.

(1) Regarding scientometrics, the application of AI yields various distinct advantages, such as conducting analyses of publications, citations, research impact prediction, collaboration, research trend analysis and knowledge mapping, in a more objective and reliable framework. (2) In terms of webometrics, AI algorithms are able to enhance web crawling and data collection, web link analysis, web content analysis, social media analysis, web impact analysis and recommender systems. (3) Moreover, automation of data collection, analysis of citations, disambiguation of authors, analysis of co-authorship networks, assessment of research impact, text mining and recommender systems are considered as the potential of AI integration in the field of bibliometrics.

This study covers the particularly new benefits and potential of AI-enhanced scientometrics, webometrics and bibliometrics to highlight the significant prospects of the synergy of this integration through AI.