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This study aims to show the inconsistency of the approach to the development of artificial intelligence as an independent tool (just one more tool that humans have developed); to describe the logic and concept of intelligence development regardless of its substrate: a human or a machine and to prove that the co-evolutionary hybridization of the machine and human intelligence will make it possible to reach a solution for the problems inaccessible to humanity so far (global climate monitoring and control, pandemics, etc.).

The global trend for artificial intelligence development (has been) was set during the Dartmouth seminar in 1956. The main goal was to define characteristics and research directions for artificial intelligence comparable to or even outperforming human intelligence. It should be able to acquire and create new knowledge in a highly uncertain dynamic environment (the real-world environment is an example) and apply that knowledge to solving practical problems. Nowadays artificial intelligence overperforms human abilities (playing games, speech recognition, search, art generation, extracting patterns from data etc.), but all these examples show that developers have come to a dead end. Narrow artificial intelligence has no connection to real human intelligence and even cannot be successfully used in many cases due to lack of transparency, explainability, computational ineffectiveness and many other limits. A strong artificial intelligence development model can be discussed unrelated to the substrate development of intelligence and its general properties that are inherent in this development. Only then it is to be clarified which part of cognitive functions can be transferred to an artificial medium. The process of development of intelligence (as mutual development (co-development) of human and artificial intelligence) should correspond to the property of increasing cognitive interoperability. The degree of cognitive interoperability is arranged in the same way as the method of measuring the strength of intelligence. It is stronger if knowledge can be transferred between different domains on a higher level of abstraction (Chollet, 2018).

The key factors behind the development of hybrid intelligence are interoperability – the ability to create a common ontology in the context of the problem being solved, plan and carry out joint activities; co-evolution – ensuring the growth of aggregate intellectual ability without the loss of subjectness by each of the substrates (human, machine). The rate of co-evolution depends on the rate of knowledge interchange and the manufacturability of this process.

Resistance to the idea of developing co-evolutionary hybrid intelligence can be expected from agents and developers who have bet on and invested in data-driven artificial intelligence and machine learning.

Revision of the approach to intellectualization through the development of hybrid intelligence methods will help bridge the gap between the developers of specific solutions and those who apply them. Co-evolution of machine intelligence and human intelligence will ensure seamless integration of smart new solutions into the global division of labor and social institutions.

The novelty of the research is connected with a new look at the principles of the development of machine and human intelligence in the co-evolution style. Also new is the statement that the development of intelligence should take place within the framework of integration of the following four domains: global challenges and tasks, concepts (general hybrid intelligence), technologies and products (specific applications that satisfy the needs of the market).

The purpose of this paper is to explain the technological phenomenon artificial intelligence (AI) and how it can contribute to knowledge-based marketing in B2B. Specifically, this paper describes the foundational building blocks of any artificial intelligence system and their interrelationships. This paper also discusses the implications of the different building blocks with respect to market knowledge in B2B marketing and outlines avenues for future research.

The paper is conceptual and proposes a framework to explicate the phenomenon AI and its building blocks. It further provides a structured discussion of how AI can contribute to different types of market knowledge critical for B2B marketing: customer knowledge, user knowledge and external market knowledge.

The paper explains AI from an input–processes–output lens and explicates the six foundational building blocks of any AI system. It also discussed how the combination of the building blocks transforms data into information and knowledge.

Aimed at general marketing executives, rather than AI specialists, this paper explains the phenomenon artificial intelligence, how it works and its relevance for the knowledge-based marketing in B2B firms. The paper highlights illustrative use cases to show how AI can impact B2B marketing functions.

The study conceptualizes the technological phenomenon artificial intelligence from a knowledge management perspective and contributes to the literature on knowledge management in the era of big data. It addresses calls for more scholarly research on AI and B2B marketing.

This paper aims to inform policymakers about key artificial intelligence (AI) technologies, risks and trends in national AI strategies. It suggests a framework of social governance to ensure emergence of safe and beneficial AI.

The paper is based on approximately 100 interviews with researchers, executives of traditional companies and startups and policymakers in seven countries. The interviews were carried out in January-August 2017.

Policymakers still need to develop an informed, scientifically grounded and forward-looking view on what societies and businesses might expect from AI. There is lack of transparency on what key AI risks are and what might be regulatory approaches to handle them. There is no collaborative framework in place involving all important actors to decide on AI technology design principles and governance. Today's technology decisions will have long-term consequences on lives of billions of people and competitiveness of millions of businesses.

The research did not include a lot of insights from the emerging markets.

Policymakers will understand the scope of most important AI concepts, risks and national strategies.

AI is progressing at a very fast rate, changing industries, businesses and approaches how companies learn, generate business insights, design products and communicate with their employees and customers. It has a big societal impact, as – if not designed with care – it can scale human bias, increase cybersecurity risk and lead to negative shifts in employment. Like no other invention, it can tighten control by the few over the many, spread false information and propaganda and therewith shape the perception of people, communities and enterprises.

This paper is a compendium on the most important concepts of AI, bringing clarity into discussions around AI risks and the ways to mitigate them. The breadth of topics is valuable to policymakers, students, practitioners, general executives and board directors alike.

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.

“Technological intelligence” is the capacity to appreciate and adapt technological advancements, and “artificial intelligence” is the key to achieve persuasive operational transformations in majority of contemporary organizational set-ups. Implicitly, artificial intelligence (the philosophies of machines to think, behave and perform either same or similar to humans) has knocked the doors of business organizations as an imperative activity. Artificial intelligence, as a discipline, initiated by scientist John McCarthy and formally publicized at Dartmouth Conference in 1956, now occupies a central stage for many organizations. Implementation of artificial intelligence provides competitive edge to an organization with a definite augmentation in its social and corporate status. Mere application of a concept will not furnish real output until and unless its performance is reviewed systematically. Technological changes are dynamic and advancing at a rapid rate. Subsequently, it becomes highly crucial to understand that where have the people reached with respect to artificial intelligence research. The present article aims to review significant work by eminent researchers towards artificial intelligence in the form of top contributing universities, authors, keywords, funding sources, journals and citation statistics.

As rightly remarked by past researchers that reviewing is learning from experience, research team has reviewed (by applying systematic literature review through bibliometric analysis) the concept of artificial intelligence in this article. A sum of 1,854 articles extracted from Scopus database for the year 2018–2019 (31st of May) with selected keywords (artificial intelligence, genetic algorithms, agent-based systems, expert systems, big data analytics and operations management) along with certain filters (subject–business, management and accounting; language-English; document–article, article in press, review articles and source-journals).

Results obtained from cluster analysis focus on predominant themes for present as well as future researchers in the area of artificial intelligence. Emerged clusters include Cluster 1: Artificial Intelligence and Optimization; Cluster 2: Industrial Engineering/Research and Automation; Cluster 3: Operational Performance and Machine Learning; Cluster 4: Sustainable Supply Chains and Sustainable Development; Cluster 5: Technology Adoption and Green Supply Chain Management and Cluster 6: Internet of Things and Reverse Logistics.

The result of review of selected studies is in itself a unique contribution and a food for thought for operations managers and policy makers.

This chapter attempts to provide answers to the following questions:

• What is artificial intelligence (AI)? Moreover, what is AI-based retail innovation?

• How does AI work?

• What are the applications of AI in retail services innovation?

• What are the ethical aspects, considerations and issues regarding the employment of AI in retail?

What is artificial intelligence (AI)? Moreover, what is AI-based retail innovation?

How does AI work?

What are the applications of AI in retail services innovation?

What are the ethical aspects, considerations and issues regarding the employment of AI in retail?

In this chapter, a general introduction on artificial intelligence (AI) is given as well as an overview of the advances of AI in different engineering disciplines, including its effectiveness in driving the United Nations Sustainable Development Goals (UN SDGs). This chapter begins with some fundamental definitions and concepts on AI and machine learning (ML) followed by a classification of the different categories of ML algorithms. After that, a general overview of the impact which different engineering disciplines such as Civil, Chemical, Mechanical, Electrical and Telecommunications Engineering have on the UN SDGs is given. The application of AI and ML to enhance the processes in these different engineering disciplines is also briefly explained. This chapter concludes with a brief description of the UN SDGs and how AI can positively impact the attainment of these goals by the target year of 2030.

It is often argued that anything observable may be simulated on a computer. Using this as a basis, workers in artificial intelligence (AI) often go forward to maintain that machines can be made intelligent by machine simulation of human intelligence processes. There are two difficulties with this concept. The first difficulty lies in the knowledge of human intelligence processes that we have presently obtained and may possibly obtain in the near future. A more basic question is of the sufficiency of the concept itself. Simulation in itself is not sufficient to produce intelligent action where perhaps modelling might be. There are fundamental difficulties in the problem of establishing an adequate mapping function. It is held that there is insufficient correspondence between human and machine intelligence processes to allow human intelligence modelling on existing digital computers.

Purpose: The purpose of this chapter is to elaborate on the major conceptual and practical considerations of the use of robots, artificial intelligence and service automation (RAISA) in travel, tourism, and hospitality companies (TTH).

Design/methodology/approach: The chapter develops a conceptual framework of the major issues related to the use of RAISA in the travel, tourism and hospitality context.

Findings: The findings indicate that while there is a creeping incursion of RAISA into TTH, there are major concerns that the TTH industry has to consider in regard to automating TTH services.

Practical implications: In a practical sense, the chapter identifies the decisions that TTH industry professionals need to take when dealing with RAISA technologies. Furthermore, the chapter elaborates on the impacts RAISA have on business operations, marketing management, human resources and financial management of TTH companies. The TTH industry has to adjust its practices and communicate with its workforce in ways as not to increase Luddite tendencies and resistance among employees.

Social implications: The analysis shows that there is an upcoming era in which automation of services will be so advanced that wealthy countries may not need to import labour to make up with its own aging workforce, suggesting that RAISA and its further development has the potential for disrupting society and international relations.

Originality/value: This chapter provides a comprehensive review of the issues related to the use of RAISA in the TTH industry, including the drivers of RAISA adoption in tourism, advantages and disadvantages of RAISA technologies compared to human employees, decisions that managers need to take, and the impacts of RAISA on business processes. It shows how macroenvironmental pressures shape the microeconomic decisions to use RAISA in a TTH context.