Artificial Intelligence,Digital Trends and Globalization: Future Research Trends

Abstract

Artificial intelligence is rapidly changing businesses based on digital trends and globalization. The aim of this article is to focus on emerging research trends regarding artificial intelligence that will influence global business management. This is significant as there have been rapid changes recently regarding artificial intelligence, including the surge in interest in generative forms. This article summarizes the trends taking place in the adoption process of artificial intelligence and what business managers need to do in order to increase their competitiveness. A brief history of how artificial intelligence has developed is stated, along with the main international business uses for artificial intelligence. Implications at the managerial and policy levels are stated that highlight the relevance and interesting nature of artificial intelligence in international business.

Keywords

Artificial intelligence digital technology international business technology entrepreneurship big data data analytics machine-based learning

Introduction

Recently, there have been enormous advances in the use of artificial intelligence that will continue in the future as new ideas and technologies evolve (Schneider et al., 2023). Kopalle et al. (2022, p. 522) state that artificial intelligence ‘is one of the world’s most promising new technologies and entails programs, algorithms, systems and machines that mimic intelligence human behaviour’. To many, the word ‘artificial intelligence’ involves using real minds through technology (Wang et al., 2022). Numerous definitions of artificial intelligence exist in the literature, with their meanings corresponding to technological advancements at the time (Xu & Babaian, 2021). Kulkov (2023, p. 861) states that ‘the classic definition of AI was offered by McCarthy (1959), namely, AI is a type of machine that is inherent in intelligent behaviour’. This early definition emphasizes machine learning in terms of algorithms being used to identify patterns. However, as Loureiro et al. (2021, p. 911) state: ‘the roots of AI may lie in ancient cultures of Greek (e.g., the mythological and robot Talos), Chinese (e.g., Yueying Huang’ dogs) and other mythologies’. In modern times, artificial intelligence as a topic gained prominence in the 1950s with the realization that machines could be built with human-like intelligence. Alan Turing, who developed a code-breaking machine in the Second World War, is one of the pioneers of artificial intelligence. He became well known for his article titled ‘Computing Machinery and Intelligence’ published in 1950 that had radical ideas for its time and discussed what became known as the Turing test. This meant an artificial system was deemed intelligent if, when interacting with a human, it was not distinguishable (Cetindamar et al., 2020).

In 1956, at the Dartmouth Summer Research Project on Artificial Intelligence in New Hampshire, the US interest in artificial intelligence grew (Dilyard et al., 2021; Loureiro et al., 2021). From the 1960s to the 1990s, governments around the world continued to fund artificial intelligence. In 1965, Gordon Moore, the cofounder of Intel, developed Moore’s law, which suggests the speed and memory of computers double every year. In addition, science fiction books and shows such as The Jetsons (1962), 2001: A Space Odyssey (1968), ET (1982), The Terminator (1984), AI (2001) and Avatar (2009) further fuelled interest in artificial intelligence and human interaction. It was not until the World Wide Web was introduced in the early 2000s that there was increased interest. This was due to the cost of computers being high, and only recently have they become affordable.

In 1997, IBM’s Deep Blue chess programme beat a human, showcasing the capabilities of machines. In the early 2000s, when the Internet was just beginning to take shape, artificial intelligence was defined as a form of computer science with computers engaging in human-like behaviour (Dilyard et al., 2021). In 2016, Google’s AlphaGo, a computer programme that played Go, a complex board game, defeated a human player named Lee Sedol. In the 2020s, improvements in computing capabilities and technological progress have made artificial intelligence a key conversation topic for businesses (Canhoto & Clear, 2020).

Haenlein and Kaplan (2019, p. 5) define artificial intelligence as ‘a system’s ability to interpret external data correctly, to learn from such data, and to use those learnings to achieve specific goals and tasks through flexible adaptation’. This definition is widely used in the literature, but there are also other definitions that exist. For example, Abdelwahab et al. (2023, p. 22) state that artificial intelligence involves ‘intelligent systems that analyze their environments and executive actions—without full human intervention—to attain specific goals’. Furthermore, Gama and Magistretti (2023, p. 2) define artificial intelligence as ‘the ability of machines to think and perform tasks simulating human behavioural patterns’. Common to these definitions is a focus on machines, systems and technology.

Many global companies use artificial intelligence for business reasons, including to analyse data and regulate transactions (Gupta & Jauhar, 2023). The impeding transformations from artificial intelligence are likely to change existing business processes and practices (Sestino & De Mauro, 2022). Businesses are focusing on technology as a way to improve overall performance through their innovation capacities and are actively incorporating new and emerging technologies, which is a way for businesses to improve their efficiencies (Sandeep et al., 2022). This is due to artificial intelligence being a type of technology that has made significant advances in how businesses structure their operations and enables businesses to advance their ideas by responding to customer needs by using new technology (Wamba-Taguimdje et al., 2020).

Much has been written about how artificial intelligence will revolutionize the world, but what it is in relation to the practice of international business is still largely unknown. A key goal in writing this article is to provide a practical and up-to-date view of artificial intelligence that corresponds with its role in the internationalization process. There are many futuristic and frightening conversations that are taking place about artificial intelligence, but sometimes these discussions are not realistic. As a consequence, there is confusion amongst international business people regarding what actually will occur and timelines for these expected changes (Alon, 2020). This article seeks to capitalize on the substantial interest from international business managers in knowing how, when and if to implement artificial intelligence (Alon et al., 2020; Hanaysha et al., 2022; Kaurav & Gupta, 2022).

Types of Artificial Intelligence

Borges et al. (2021) suggest that the artificial intelligence field has developed based on a human-centred and rationalist approach. The human-centred approach focuses on mimicking the emotional features of humans in machines (Hemphill & Kelley, 2021). This means the artificial intelligence technology replicates the similarity of a person in how it looks and acts (Lanteri, 2021). The idea is that robots can be built through technology and can potentially surpass biological reasoning practices. The rationalist approach focuses on engineering and science to build computing technologies with higher levels of intelligence. This means mathematics and data analytics are emphasized in terms of defined outcomes. The rationalist approach is based on the idea that quantitative data can result in better outcomes than interpretative information.

There are different ways to analyse artificial intelligence based on how it is defined and conceptualized (Quan & Sanderson, 2018). One of the main ways to analyse artificial intelligence is based on intelligence mechanisms such as human-inspired and humanized (Bahoo et al., 2023). Analytical artificial intelligence relies on cognitive mechanisms to understand human–computer interaction. This means behaviour is formed based on opinions about certain factors (Rana et al., 2022). Human-inspired artificial intelligence involves cognitive functions but goes further by including emotional mechanisms. This means it understands how to behave in certain environments but can also read people’s behaviour (Rana, 2019). This means it understands different emotional states. Humanized artificial intelligence includes not only cognitive and emotional functions but also social functions, thereby going a step further by involving social interactions (Ng, 2016).

Another way to analyse artificial intelligence is by focusing on how it is used in society. Pitt et al. (2023) state that there are three main types of artificial intelligence: narrow, general and super. Artificial narrow intelligence refers to a computer processing information based on a specific skillset, which can occur when machines exhibit intelligence related to an area such as sport or entertainment in which they have a large amount of information (Han et al., 2021). Artificial narrow intelligence involves a specific subset of human cognition that can occur through technology. This means artificial intelligence can learn without being told what to do in a general way, thereby replacing humans doing routine tasks (Klumpp, 2018). It refers to most types of artificial intelligence in practice today. Sometimes narrow artificial intelligence is called weak artificial intelligence, as it focuses on a specific task rather than having a more holistic view of the business environment. However, many tasks are now performed by narrow artificial intelligence, including driving directions that provide time benefits. In addition, narrow artificial intelligence allows for productivity gains by freeing up time for other tasks.

General artificial intelligence is considered a strong form of artificial intelligence as it enables the general environment to be considered, but this type of artificial intelligence is still in its infancy and is predicted to be more relevant when technology has evolved (Haenlein et al., 2022). This means it is more a concept than a reality for international business managers. The idea behind general artificial intelligence is that when it is fully functional as a concept, it will be hard to differentiate from human intelligence. Thus, for this to happen, technology needs to simulate, replicate and then extrapolate information. The usefulness of general artificial intelligence is that it will be able to learn like humans and possess a sense of imagination, thereby thinking beyond solving problems to consider future needs (Chen et al., 2022).

Super-artificial intelligence is speculative and considered a possibility. It involves technology having superpowers beyond what a human can currently do. This means it exceeds current human intelligence levels in order to make complex decisions (Enholm et al., 2022). It learns at a fast rate, enabling knowledge to be quickly acquired. By doing so, information is accumulated, so that artificial intelligence surpasses current knowledge levels, thereby enabling it to train other computers while being aware of its limitations.

Main Subfields of Artificial Intelligence

Artificial intelligence is reshaping the competitive business environment by enabling more technologically embedded systems, for example, software such as voice-activated computing systems or hardware devices. The most commonly known forms of artificial intelligence involve robots, but other types that are less obvious in terms of computing technologies exist. The current research and practice on artificial intelligence have mostly been done in silos without considering the international business applications. This is due to the topic of artificial intelligence, which means different things to different people depending on their disciplinary background. For example, those in the engineering and science fields are interested in machine learning, whilst business management academics are more focused on human–computer interaction and ethics.

Artificial intelligence systems need to have the following capabilities: natural language processing, knowledge representation, automated reasoning and machine learning. Natural language processing means that the technology can communicate in a natural language format, meaning it can be understood by others (Loureiro et al., 2021). Knowledge representation refers to the ability to store and retain information in a transferable format. Automated reasoning refers to utilizing information to understand data and make inferences. Machine learning involves analysing and updating information as it becomes available.

To facilitate more research on artificial intelligence and international business, an interdisciplinary and practical perspective is required. This enables an audit of what has been done in different international business fields (e.g., entrepreneurship, organizational behaviour, strategy, technology) to be analysed. The main subfields of artificial intelligence are: (a) machine learning, (b) knowledge-based systems, (c) computer vision, (d) robotics, (e) natural language processing, (f) automated planning and scheduling and (g) optimization (Loureiro et al., 2021). Table 1 states each of these subfields and their usage in international business.

Machine Learning

Apell and Eriksson (2023, p. 180) state machine learning ‘refers to computer learning without being explicitly programmed’. Machine learning can be used to achieve artificial intelligence as it simulates the artificial neural network architecture that occurs in the human brain. The idea of machine learning is that it learns from new information, thereby generating new thought patterns (Shaikh et al., 2022). Similar to a human brain, machine learning allows for complex patterns to develop that lead to new outcomes. This can occur through deep learning, in which functioning artificial neural networks are utilized to solve problems.

The main types of machine learning are supervised, unsupervised, reinforcement and deep (Abioye et al., 2021). Machine learning involves the process of using computers to learn from past information in order to predict the future. This means a machine is programmed in such a way that it acquires knowledge from experience in order to interpret new information (Ahmed et al., 2022). Supervised machine learning involves enabling machines to learn based on desired information. Large data sets are inputted into a machine and then trained to understand their significance. By doing so, it integrates human feedback in order to model outcomes. This is important for enabling machine learning to create new artificial intelligence systems (Mohanta et al., 2020). For machine learning to function properly, it requires a large amount of data and validation to correctly classify the data.

Table 1.

Usage of Artificial Intelligence Subfields in International Business.

Subfield	Usage in International Business
Machine learning	Predict economic and social changes in international markets. Efficient utilization of resources in different markets. Cost savings through supply chain coordination.
Computer vision	Overall international market understanding of interlinkages. Recognition of the cultural and regulatory implications of different markets. Visualization of each geographical location.
Automated planning and scheduling	Implement it based on real-time information in international markets. Scenario modelling about exchange rate forecasts. Integrate times for production and selling.
Robotics	Global usage based on need. Internationalization of production methods. Commonality of practices to enhance safety.
Knowledge-based systems	Access to relevant international business data, such as intellectual property rights. Enables real-time information about political situations to be accessed. Consistent ability to update knowledge about demand and supply in international markets.
Natural language processing	Better communication amongst people speaking different languages. Time efficiency in translating speech and accurately recording it. Privacy amongst those in the communication channel.
Optimization	Data analytics and processing of global information. Scalability of production or service facilities. More efficient processes due to better computing power.

Unsupervised learning involves machines learning based on unstructured data sets. This is more difficult to do but can be achieved through clustering techniques. The non-labelled data is entered by humans but still requires validation by humans. It is a way for hidden patterns in data to be found, thereby enabling trends to be recognized. Reinforcement learning involves machine learning based on the analysis of data through rewards or reinforcements. It involves understanding the global business environment and how entities interact. Thus, it is based on trial and error and is memory-intensive. The advantage is that it is helpful in complex environments in which personalized recommendations are required. Deep learning goes beyond simple tasks and involves interpreting and acting on data.

Computer Vision

Computer vision involves analysing images through artificial intelligence in order to make decisions. This can be conducted through scenes that are reconstructed. To do this, real-time as well as interactive images are used. Motion analysis can be conducted to analyse how events take place and then mimic them. Images can be restored through the use of computer devices. This enables images to be recognized in order so that they can be processed. The Internet of things has made many technological devices interconnected. This is due to people having multiple electronic gadgets, such as phones and computers, that they use on a daily basis. As wireless communications are now available at most locations, it has become easier for people to access certain data services. This means the use of mobile applications has increased, which has facilitated the use of chatbots. Digital platforms such as Expedia, YouTube and Facebook have changed how people disseminate information. As a consequence, digital platforms provide continually updated information. This enables sellers and buyers to exchange knowledge in the hope of facilitating a good outcome.

Automated Planning and Scheduling

Artificial intelligence solutions involve providing predictive estimates regarding supply chains and other types of management. This enables better customer personalization and effective services, thereby saving time and enabling manual and repetitive tasks to be automated. Artificial intelligence algorithms involve analysing data and then predicting behaviour. By doing so, patterns can emerge that would previously be undetected. This information can be used to detect a range of issues, such as customer demand (Rana, 2020). To adjust to the use of artificial intelligence, business managers need to reskill and retrain. This involves acquiring new knowledge about its usage and how to implement it in their organization. Artificial intelligence, when used in the right way, does not necessarily replace humans but can be used as a complementary service, thereby not substituting human interaction but leveraging computing capabilities.

Artificial intelligence is a technology that performs tasks that require a form of intelligence. This means the task goes beyond simple repetitive actions and requires interaction with the immediate environment. Thus, artificial intelligence uses higher-order cognitive processes that occur in humans and include skills such as decision-making, planning and reasoning. The key feature of artificial intelligence is that it can facilitate situational and environmental awareness to strategize.

Robotics

Robotics are automated devices that are programmed to perform certain tasks. They typically carry out physical activities and move based on sensory needs. Goel et al. (2022) found that, increasingly, robotics as a form of artificial intelligence are being used in the hospitality and tourism sectors. Recently, robots have become more advanced as they can do more complex tasks that require some form of human–computer interaction (Kim et al., 2022). This is due to the COVID-19 pandemic necessitating the use of robots in hotels, restaurants and other high-touch environments. Robotic technology is used in a range of industries, including service, education and travel.

Knowledge-based Systems

Knowledge-based systems involve artificial intelligence based on existing knowledge. This means decisions are made based on inferring information based on the storage of knowledge. Knowledge can come in a variety of formats, including documented cases or experiences. Machines utilizing knowledge-based systems have a large depository of information that is continually added to. The main types of knowledge-based systems are expert systems, intelligent agents, case-based reasoning and linked systems (Abioye et al., 2021). Expert systems involve specialized knowledge derived from a highly knowledgeable person. It is task-specific and detailed information that is required to make decisions. Intelligent agents are machines that use technology to teach themselves. This means acquiring information but knowing its strategic importance in the marketplace.

Case-based reasoning involves accessing knowledge that helps to understand a particular situation. This means previous occurrences of a problem have occurred that can be utilized to explain new situations. The knowledge obtained can help critique, explain or predict new cases. Linked systems utilize knowledge that is dependent on other forms of information, thereby enabling knowledge sources to be applied in a seamless and unified manner.

Natural Language Processing

Natural language processing can be text- or speech-based. It involves mimicking the linguistic capabilities of a human through artificial intelligence. Amazon Alexa is a virtual assistant technology device that completes tasks when spoken to. It is used to do tasks such as playing music or providing other real-time information. Increasingly, Amazon Alexa is being utilized to control smart devices and other connected electronic equipment. Artificial intelligence technologies are quickly changing as they use synthetic computer cognition. Currently, many artificial intelligence systems are producing intelligent results but not surpassing human-level behaviour. This is because they are using knowledge from data and other information that can be processed through computers. In the future, it is expected that artificial intelligence will resemble human thinking and engage in arbitrary tasks. Strong artificial intelligence engages in concept comprehension (Surden, 2019).

Optimization

Artificial intelligence is altering human resource management practices in terms of talent acquisition and work–life balance. Humans and artificial intelligence systems are working together to maximize efficiency and ensure information is used in the right way. Companies are using artificial intelligence systems to recruit, train and manage employees. Candidates applying for positions are now screened through artificial intelligence systems. This additional help can be more objective and identify a more holistic understanding of candidates compared to subjective human-based assessments.

Future Research Suggestions

Currently, there have been many advances in artificial intelligence that are blurring the digital and physical worlds. Well-known examples of artificial intelligence requiring further research include big data applications that generate real-time information, social robots that act as conversational agents and online chatbots that answer questions. There tends to be a stereotype of artificial intelligence replacing humans, but this might not always be the case, as people still like human interaction. The artificial intelligence revolution does mean that international business is rapidly transforming in an unprecedented way, which means new research is required. The next subsections further discuss new research directions.

Fourth Industrial Revolution

We are in the Fourth Industrial Revolution, which is focusing on artificial intelligence and its usage in games, operating systems and simulations (David et al., 2022). The business applications of artificial intelligence are immense and have resulted in positive impacts through greater accessibility and usage of information, with the Fourth Industrial Revolution resulting in an upskilling of people through increased levels of digital literacy (Anshari & Hamdan, 2022). There are many current debates on how and when artificial intelligence will change business, particularly in terms of resulting in more efficiency, and more research is needed in this area. Research by Pedota and Piscitello (2022) suggests that there will be more technology-driven creativity due to the Fourth Industrial Revolution. Artificial intelligence technologies are a source of business model disruption, but research is required on how the changes are taking place. Companies are changing to a digital culture that emphasizes information and technology, but there are still many unknown implications for business that merit more research attention. The idea that the artificial intelligence economy will change the nature of work and how businesses achieve a competitive advantage is a topic of immense research interest.

Future Technology Trends

Every artificial intelligence researcher is interested in extending the current literature as a way of progressing the field. This means it is important to ask: What will be the next important topics in artificial intelligence research? Technology innovation underpins research trajectories based on business-to-business and customer interaction (Salamzadeh et al., 2022). Much of the emerging research is integrating new technology that deals with online data and information systems. It is anticipated that more research will focus on how technology leads to increased productivity gains. This means focusing on how quickly technological innovations regarding artificial intelligence will change societal conditions. How and when people use robots at home and at work merits more research attention. Particularly interesting is to examine how chatbots and other conversational agents are altering how people perceive artificial intelligence. Examples of potential research themes include looking into how dependent people are on voice-activated artificial intelligence and whether this is replacing or complementing human-to-human interaction.

Law and Policy Changes

Artificial intelligence has changed at such a fast pace in the past year that there is a lag with law and policy updates. Muhammad et al. (2022) found that the Fourth Industrial Revolution will mean that the fintech industry will rapidly change, resulting in a need for new government policies. This means more research is required on what lawmakers and policymakers can do regarding being proactive about potential security and ethics concerns. Ebekozien et al. (2022), in a study on sustainable cities, found that more government policy is needed around emerging technologies related to the Fourth Industrial Revolution. Research is required to bridge the gaps between the practice and implementation of artificial intelligence. Growing inequality gaps between the rich and poor regarding the use of artificial intelligence technologies merit more research. This could include focusing on developing new survey instruments and conducting case-study analysis on how governments can intervene in terms of enabling the use of more expensive artificial intelligence. It might be interesting to research whether subsidies or incentives to use artificial intelligence can contribute to better societal advancement.

Value Cocreation

Value cocreation is an area of interest, but currently, scholars are unsure as to where the research is headed regarding artificial intelligence. Topics of broad interest in the future relate to coproduction and technology infusion. This is due to augmented and virtual reality altering how people interact. More research is required on how technology users can cooperate with companies on developing new artificial intelligence, particularly regarding the use of digital technologies in emerging markets (Dana et al., 2022). Due to the availability of communication mechanisms and information on the Internet, it may be possible for future technology to be more quickly developed. Thus, research focusing on value cocreation across geographical borders and industry segments is needed. Research could focus on startups regarding artificial intelligence and resulting purchase behaviour (Ebrahimi et al., 2022). In particular, industries such as the education and sports sectors that have rapidly changed based on artificial intelligence should receive more research attention (Koronios et al., 2021a, 2021b).

Managerial Implications

Artificial intelligence represents a major change for international businesses in terms of replacing or improving human tasks. It affects all aspects of international business, including customer interaction and the management of organizations. Due to constrained managerial abilities, artificial intelligence can be deployed to reduce bureaucracies and inefficiencies. Artificial intelligence can be used to accelerate decision-making, detect trends and remove cognitive biases. This can lead to stronger market positions and improved project management. The introduction of new Internet and social media marketing has occurred with the growth of artificial intelligence applications. Artificial intelligence supports providing feedback to managers so they can save time for other tasks. As more business functions become digitalized, advanced artificial intelligence systems can be deployed.

Artificial intelligence is changing our way of doing business, particularly in the international marketplace. Its advent is altering how humans conduct business and has altered the way we think about work–life balance. The rapid advancement of artificial intelligence technologies in recent years has been alarming due to the rate of their development. Businesses are pondering how and to what extent they can implement artificial intelligence. Artificial intelligence is set to help improve international services and production facilities and enable better delivery options, thereby enabling international business managers to increase their productivity by boosting overall efficiencies.

Conclusion

Artificial intelligence technologies, in a broad sense, refer to techniques of artificial intelligence that comprise a technology system. Artificial intelligence is a disruptive general-purpose technology, and its evolution has relied on technological advances. International businesses utilize computing power and artificial intelligence technologies in many different ways. Given the prominence of artificial intelligence in business, this article sought to understand how it has facilitated internationalization. Artificial intelligence in international businesses can facilitate cost and time efficiencies, improve stakeholder engagement and become part of global business practices, thereby resulting in ethical challenges.

Footnotes

Declaration of Conflicting Interests

The author declared no potential conflicts of interest with respect to the research, authorship and/or publication of this article.

Funding

The author received no financial support for the research, authorship and/or publication of this article.

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