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R E S E A R CH A R T I C L E

Artificial intelligence for international business: Its use, challenges, and suggestions for future research and practice

Jane Menzies1 | Bianka Sabert1 | Rohail Hassan2,3 | Prince Kofi Mensah4

1School of Business and Creative Industries,

University of the Sunshine Coast, Sippy

Downs, Queensland, Australia

2Othman Yeop Abdullah Graduate School of

Business (OYAGSB), Universiti Utara Malaysia,

Kuala Lumpur, Malaysia

3University of Economics and Human Sciences

in Warsaw, Warsaw, Poland

4University of Ghana Business School,

University of Ghana, Accra, Ghana

Correspondence

Jane Menzies, School of Business and Creative

Industries, University of the Sunshine Coast,

Sippy Downs 4556, QLD, Australia.

Email: [email protected]

Abstract

The emergence of artificial intelligence (AI) has transformed global business, aiding

operational efficiency and innovation. It utilizes machine learning and big data analyt-

ics, driving predictive market trends and strategic decision-making. However, despite

the rising discussion and accessibility of AI tools, understanding its impact on interna-

tional business remains limited. This article explores AI's potential in international

business strategies, practices, and activities. To address this aim, we reviewed 37 arti-

cles in the existing literature to critically explore AI within the context of international

business. More specifically, we explored how AI can be applied to innovation

approaches in international business, international market selection, entry modes,

foreign exchange, international human resource management, international supply

chains, managing across cultures, and more topics. AI has necessitated changes in

workplace configurations and the need for organizational and employee adjustments

in response to this technology. As a result of the foregoing issues on AI integration

within international business, our analysis provided an exploratory discussion around

its use, challenges, managerial implications, and suggested areas requiring future

studies.

K E YWORD S

artificial intelligence, challenges, international business, practices, strategies

1 | INTRODUCTION

Artificial intelligence (AI) has emerged as a pivotal force reshaping the

international business (IB) landscape. In an era of digital revolution, AI

technologies have become essential tools for enhancing operational

efficiency, decision-making, and innovation globally. AI encompasses

diverse technologies, including machine learning, robotics, natural lan-

guage processing, and artificial neural networks (Ciulli & Kolk, 2023;

Soori et al., 2023a). AI can be instrumental in addressing the complex

challenges associated with global operations. For instance, machine

learning has been utilized to improve the understanding of cultural

heterogeneity within a country (Messner, 2022). Natural language

processing enables systems to comprehend complex human language,

understand nuanced contexts, and interpret varied meanings

(Jarrahi, 2018). Based on the content the natural language processing

system is trained on, it can engage in tasks such as copywriting for

national and international professions (Jung et al., 2017), which has

even led to screenwriter strikes in Hollywood, who feared to be

replaced by AI (Alvarez-Mitchell, 2023).

AI has been considered as a computer system that conducts its

operations like human beings, thus mimicking human intellect

(Fleck, 2021; Prentice et al., 2020). The seminal work of McCarthy

et al. (1955) defines AI as “the science and engineering of making

intelligent machines.” The realm of AI encompasses diverse

DOI: 10.1002/tie.22370

This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium,

provided the original work is properly cited.

© 2024 The Authors. Thunderbird International Business Review published by Wiley Periodicals LLC.

Thunderbird Int. Bus. Rev. 2024;66:185–200. wileyonlinelibrary.com/journal/tie 185

capabilities—perception, reasoning, learning, and decision-making—at

individual or institutional levels (Manning, 2020; Xie & He, 2022). A

contemporary definition by the OECD (2022, p. 4) characterizes AI as

“a machine-based system that can, for a given set of human-defined

objectives, make predictions, recommendations, or decisions influenc-

ing real or virtual environments. AI systems are designed to operate

with varying levels of autonomy.” Meltzer (2018) explains that AI is

self-learning systems that can learn from experience with humanlike

aspects and can sometimes surpass human performance on tasks.

According to Tambe et al. (2019), AI is a broad category of technolo-

gies that enables a machine to carry out tasks that normally need

human cognition. AI exhibits several skills, including the ability to com-

prehend natural language, spot patterns in data, solve problems, and

adjust to novel circumstances without the need for explicit program-

ming. There is a range of practical AI applications, including smart

assistants such as Alexa or Google Home, translation, transcription,

self-driving cars, medical diagnosis, robotics and more (OECD, 2022).

Using AI in business is critical for improving productivity, compet-

itiveness, and decision-making, especially in the context of multina-

tional enterprises (MNEs) (Bag et al., 2021; Wamba-Taguimdje

et al., 2020). AI integration significantly empowers businesses in navi-

gating intricate global environments by facilitating advanced predic-

tive analytics, task automation, and leveraging data-driven insights

(Allal-Chérif et al., 2021; Bag et al., 2021; Satheesh et al., 2020;

Wamba-Taguimdje et al., 2020). Adoption of AI in company opera-

tions can result in significant economic gains. It is predicted that there

will be an additional $13 trillion in global economic activity by 2030

as a result of AI use, according to a McKinsey report (Bughin

et al., 2018). It is, therefore, imperative that IBs have an AI strategy.

Through a combination of AI and data analytics, IBs are in a better

position to predict market demands and mitigate risks, optimize sup-

ply chain management, streamline logistics, and enhance cross-border

transactions (Li et al., 2021; Shi, 2022; Zhu, 2021). AI-powered lan-

guage translation, utilizing natural language processing, has the poten-

tial to significantly enhance cross-cultural and cross-linguistic

communication and negotiation by offering real-time, simultaneous

translation, bridging language barriers, thereby fostering stronger and

more effective international partnerships (Elhadi, 2023). AI-driven

machine translation (MT) has been highlighted for its potential to

lower language barriers in trade, notably aiding e-retailers in strength-

ening exports (OECD, 2022). AI is also poised to help reduce transac-

tion costs generally (Chen & Kamal, 2016).

Undoubtedly, over 2023, the proliferation of discussion about AI

has increased with the introduction and open accessibility of Open

AI's Chat-GPT and other similar programs such as Microsoft's Bing

and Google's Bard. This accessibility has propelled AI into the main-

stream, fostering widespread curiosity about its impact on work and

business landscapes. With documented potential to transform cross-

border trade and collaboration, AI's influence extends to governmen-

tal levels in international trade negotiations (Li et al., 2021;

OECD, 2022; Zhu, 2021). Despite this, a substantial knowledge gap

exists regarding AI's application across various facets of IB strategies,

practices, and activities. There is a rising discussion in academic

research that investigates various sorts of AI digital tools and tech-

niques, as well as whether businesses can benefit from such solutions

(Castellacci & Viñas-Bardolet, 2019). In this regard, the recent calls for

academic scholarship on AI in IB have received considerable attention

in premier IB journals, including other related disciplines such as inter-

national management, general management and human resource

management (Malik, Budhwar & Kazmi, 2023).

For this reason, research conducted at the intersection of AI and

IB takes on an increasingly multidisciplinary nature (Jarrahi, 2018).

According to Jones (2023), there is still a lack of understanding in the

literature on AI and IB regarding how AI and related technologies can

provide solutions for effective IB and subfunctional areas, as well as

how AI-enabled IB functions link to other operational tasks to deliver

improved outcomes for their organizations. This article explores AI's

potential roles within IB frameworks to address this gap.

Given the importance of this technology to IB in general, we

explore the relevance and application of AI for IB in this article. By

doing this, we contribute to the research literature in several impor-

tant ways. First, there are no other articles that have systematically

reviewed the current literature on AI and IB. Second, there are no

papers that have examined how AI can be used in the various strate-

gies, activities, and practices of IBs. We also explore the various chal-

lenges for the implementation of AI, which can help IBs appreciate the

issues involved in AI implementation. Furthermore, we offer sugges-

tions for future researchers to examine the issues around the imple-

mentation of AI in IBs and the practical implications of doing so. As a

result, this serves as a guide for MNEs, IBs, researchers, academic

educators, and students on how IB can benefit from AI

implementation.

The structure of our article is organized for clarity and depth of

understanding. Section 2 outlines the Methodology, detailing the sys-

tematic approach adopted for our review. Following this, Section 3

presents the results, focusing on the diverse applications of AI in IB

strategies, practices, and activities. The discussion in Section 4 shifts

to exploring the challenges faced by IBs in implementing AI, offering

critical insights. This section also recommends that future research

broaden its scope by incorporating various theories and perspectives

on AI and IB issues. Additionally, we provide a series of managerial

implications, guiding managers on effectively integrating AI into their

IB operations.

2 | METHODOLOGY

In this article, we conducted a traditional/narrative literature review

of prior literature (Grant & Booth, 2009) to examine how AI can be

integrated into the various strategies, practices, and activities of the

IB. Our methodology adhered to the standards outlined by Grant and

Booth (2009), which involves gathering research, methodically exclud-

ing works of inferior quality, and synthesizing the findings published

in the field. This approach, as emphasized by Grant and Booth (2009),

allowed us to cover a broad spectrum of topics with varying degrees

of depth and breadth, adopting a thematic lens for our analysis.

186 MENZIES ET AL.

Moreover, the essence of a literature review is the synthesis of

prior studies into coherent forms—be it textual, tabular, or graphical—

and the subsequent analysis of their findings and contributions (Paul

et al., 2021). It is essential in academic research for uncovering and

building upon existing findings by identifying gaps in the literature,

thus guiding future research directions and aiding researchers in rec-

ognizing unexplored areas requiring further investigation (Paul

et al., 2021). A literature review, therefore, allows for knowledge

advancement (Palmatier et al., 2017; Paul & Menzies, 2023) as new

directions for research can be posed.

We started with a systematic approach by searching the Web of

Science (WOS) on “AI” and “IB.” This resulted in 21 articles. We then

took on the strategy of searching for articles in each IB domain area.

For example, we searched WOS for “AI” and “foreign exchange” (24 articles), “international human resource management” (4), “foreign market analysis” (0), “entry mode” (5), “international trade negotia-

tions” (2), “IB process” (0), “international marketing” (5), and “global supply chains” (21), which resulted in a total of 82 articles. There were

no duplicates and our initial screening of the articles based on the

abstracts resulted in the exclusion of 33 articles, leaving us with

49 articles. We excluded articles if they were conference papers, were

not on an IB topic, or were irrelevant to the study. We then down-

loaded the full text of all articles and excluded 12 more for lack of rel-

evance, leaving us with 37 articles. We did a thematic analysis of

these 37 articles to develop our literature review and answer our

research questions. The following PRISMA diagram provides details of

our search strategy (Figure 1).

3 | THE USE OF AI IN IB

In this section, we explore the diverse applications of AI across

the various aspects of IB, and we answer the question, “How

do IBs pursue opportunities around AI for their various strate-

gies, activities, and practices?” To help guide our discussion in

the next section, we provide Figure 2, which highlights how AI

can be used in the various strategies, activities, and practices

of IBs.

3.1 | Innovation approaches in international business

As the OECD (2022) argues, AI has the potential to spur innova-

tion; it can help organizations create value from data and reduce

trade costs through more efficient operations and supply chains.

AI can augment human creativity by opening up new avenues for

developing innovative products, services, processes, and business

model (Haan & Watts, 2023). From an innovation perspective, AI

presents significant opportunities for MNEs to enhance both qual-

ity and performance in their operations, including finding and fix-

ing bugs, enhancing features, and increasing reliability

(Nuttal, 2022). AI has the potential to improve innovation, opera-

tional agility, and strategic decision-making, giving a company a

competitive edge in the fast-paced, globally integrated market

(OECD, 2022).

F IGURE 1 PRISMA diagram for the search strategy for artificial intelligence (AI) and international business (IB). [Color figure can be viewed at wileyonlinelibrary.com]

MENZIES ET AL. 187

F IGURE 2 How artificial intelligence (AI) can be used in the various strategies, activities, and practices of international businesses (IBs).

188 MENZIES ET AL.

3.2 | International market selection

Decisions on strategy and technology complexities have largely influ-

enced the nature of cross-border market entry (Benito et al., 2022).

With the ease of information sharing across borders and globally,

technology drives much more global business engagements, even at a

lower cost (Benito et al., 2022; Stallkamp & Schotter, 2021). Benito

et al. (2022) suggest that there is a need for IBs to examine within-

country and cross-country network externalities. Technological

advancements such as AI can play a role in international market selec-

tion (IMS). IMS is defined as “the process of establishing criteria for

selecting (country) markets, investigating market potentials, classifying

them according to the agreed criteria and selecting which markets

should be addressed first and those suitable for later development

(Andersen & Strandskov, 1998, p. 67).” AI can allow MNEs greater

access to international markets and increase international opportunity

recognition (Dillon et al., 2020). AI enables MNEs to efficiently moni-

tor emerging trends and opportunities in overseas markets without

making substantial resource commitments to local marketing affiliates

(Luo & Zahra, 2023). Through AI-enabled data analytics and visualiza-

tion tools, MNEs can benefit from analyzing market trends and oppor-

tunities by gaining insights and recommendations on a foreign

market's current and future state (Meltzer, 2018).

Furthermore, AI could assist with creating a business plan for for-

eign market entry, as it may assist with comparing cultural and psychic

distances between countries. Generative AI tools such as ChatGPT,

Bing, and Bard excel in swiftly producing comprehensive business

plans (Hughes, 2023). It could also provide insights into the political,

economic, legal, and regulatory environments, market, industry, and

competitor information essential when analyzing foreign countries for

market entry. AI could also allow for creating country, industry, and

competitor profiles through Chat-GPT, Bing Chat, or Google Bard.

Machine learning applications can identify cultural and market sub-

groups instead of suitable nations only (Messner, 2022). It could assist

with identifying suppliers and other actors within an IB ecosystem.

When conducting IMS, it is also essential for the MNEs that use AI

technologies to consider the digital capability of the country they

enter because that can either contribute to or detract from their abil-

ity to implement digital strategies in that country operationally.

Fish and Ruby (2009) developed an AI model specifically for small

and medium enterprises (SMEs), offering a resource-efficient solution

to aid in their IMS due to the limited resources often available to

SMEs. Neubert (2018) asserts that AI algorithms can aid in predicting

future international market attractiveness by lean global startups.

More accurate forecasts improve the effectiveness and caliber of mar-

ket selection choices as well as the chance to take part in the expan-

sion of the market in the future (Neubert, 2018).

3.3 | Entry mode choices

Entry modes have been defined by Sharma and Erramilli (2004, p. 4)

as “a structural agreement that allows a firm to implement its product

market strategy in a host country either by carrying out only the mar-

keting operations, or both production and marketing operations there

by itself or in partnership with others.” Technology can allow for bet-

ter entry mode choice and partnership improvement of global connec-

tions through partners and stakeholders. AI is hypothesized to

increase productivity growth, increase economic growth, and provide

new opportunities for international trade (Meltzer, 2018). AI pushes

firms to consider an expanded global web of loosely coupled external

communities through digital platforms and ecosystems, alliances,

global freelancers, open-source communities, online innovation,

crowdsourcing, and the like (Nambisan & Luo, 2022).

AI possesses the capability to efficiently manage corporate rela-

tionships, a critical component for success in IB. Furthermore, AI can

facilitate networking as well as learning (Neubert & Van der

Krogt, 2018) playing a pivotal role in supporting the firms' internation-

alization process. Research also suggests that there might be more

back-shoring activity of MNEs using AI, and Kinkel et al. (2023) exam-

ined this issue for cross-border engagements. For better decision-

making, Kinkel et al. (2023) found a significant positive association

between AI companies' offshoring and back-shoring decisions. Com-

panies' digital competencies and international ambidexterity further

moderate this association between AI and back-shoring.

When exporting, the OECD (2022) reports that AI can be used to

read and understand the descriptions of commercial goods and clas-

sify these against customs codes against the Harmonized Tariff

Schedule. The technology employed here is likely similar to the

machine learning capability utilized for identifying characters on

license plates amid complex backgrounds (Liu et al., 2022). It may also

assist companies to identify their requirements around customs pro-

cedures and duties. Furthermore, it can also be used to identify coun-

terfeited goods.

3.4 | Foreign exchange in international business

Understanding currency trends, movements, and forecasts is paramount

to successful IB decision-making (Güler & Tepecik, 2019). The value of

the currency at any given point in time may result in the IB either mak-

ing a currency loss or gain, either costing or benefiting a firm (Güler &

Tepecik, 2019). When analyzing foreign countries for market analysis, it

is also possible to use AI to predict and understand currency trends and

movements (Güler & Tepecik, 2019; Roth, 2019). For example, the Nik-

kei claimed that an AI system trained on Nikkei data from articles, long-

term dollar-yen trends, commodity prices, and other market indicators

could have the potential to predict future currency trends (Roth, 2019).

Greater accuracy of exchange rate forecasting indeed makes IB currency

decisions more reliable (Güler & Tepecik, 2019).

3.5 | Trade negotiations at an international level

AI also has the potential to be used to improve outcomes from inter-

national trade negotiations (OECD, 2022). For instance, AI could

MENZIES ET AL. 189

better analyze the economic trajectories of different negotiating part-

ners at the country level under different assumptions, including out-

comes contingent on trade negotiation and different country

requirements around economic growth pathways under various forms

of trade liberalization. It can also analyze how these outcomes are

affected in a multiplayer scenario where trade barriers are adjusted

down at different rates, as well as predict the trade response from

countries not party to the negotiation. Brazil has already established

an Intelligent Tech & Trade Initiative that uses AI to improve trade

negotiations (OECD, 2022).

3.6 | Global supply chains and international operations

Frequently used AI technologies in supply chain management (SCM)

are artificial neural networks, agent-based systems, and genetic algo-

rithms (Toorajipour et al., 2021). Artificial neural networks are

software-based AI technologies that simulate the structure of the

human brain, consisting of multiple interconnected neurons in which

information is stored (Soori et al., 2023b). Most of the information is

stored in hidden layers of neurons, which reside between the input

layer and the output layer (Sang, 2021). In the training phase, the arti-

ficial neural networks are fed with a large number of scenarios (Stair

et al., 2021). Once the artificial neural networks has been sufficiently

trained, it can be fed with new scenarios and will calculate the out-

come for each new scenario (Stair et al., 2021). Utilizing this AI tech-

nology has assisted SCM with demand forecasting, inventory and

route optimization, production scheduling, logistics planning, evaluat-

ing and monitoring supplier metrics such as quality, delivery times,

and pricing and risk management (Soori et al., 2023b). For instance,

Zhu and Liu et al. (2022) studied the effectiveness of artificial neural

networks in the risk management of supply chains for prefabricated

buildings. The study first analyzed risk recognition and assessment to

establish an indicator system for risk factors. Based on this informa-

tion, the artificial neural networks predict the risk in the supply chain

with 96–100% accuracy (Zhu & Liu, 2022). The integration of artificial

neural networks within international SCM presents a multifaceted

approach that enhances diverse operations, including demand fore-

casting, inventory optimization, risk management, and supplier metric

evaluation, exemplified by their high predictive accuracy in risk

assessment, illustrating their potential to fortify international supply

chains significantly.

Another important AI technique used in SCM is agent-based sys-

tems (Toorajipour et al., 2021). Agent-based systems are computational

models based on the schema, that is, simple decision rules (Massari &

Giannoccaro, 2021), that simulate the interactions of individual entities,

called agents, along the supply chain that autonomously take action

(Toorajipour et al., 2021). In SCM, the agents act as suppliers, manufac-

turers, distributors, and retailers, allowing the agent-based system to

capture the complexities and emergent behavior of the supply chain and

enable decision-makers to identify potential bottlenecks, predict supply

and demand fluctuations, and design more resilient and efficient supply

chains (Massari & Giannoccaro, 2021). More specifically, Blos et al.

(2018) developed an agent-based system for supply chain disruption

management. The agent-based system was tested for the supply chain

of an aircraft equipment manufacturer, shipping equipment from China

to Brazil via the United States. The agents were trained on a variety of

potential disruption events. It was found that the accuracy of the agent-

based system is highly dependent on the accuracy of the data provided

by the organization for which the agent-based system is developed.

While the agent-based system can be employed by other organizations,

it is necessary to program it with the disruption data from the relevant

organization (Blos et al., 2018). The implementation of agent-based sys-

tems within international SCM offers a sophisticated framework to sim-

ulate and optimize intricate interactions among supply chain entities,

highlighting their potential to fortify and tailor international supply

chains for resilience and adaptability.

Genetic algorithm represents another AI technique that signifi-

cantly improves SCM (Toorajipour et al., 2021). Genetic algorithm is a

computational approach inspired by the principles of natural selection

and biological reproduction (Sang, 2021). Much like how species

evolve and adapt in nature, genetic algorithms work by iteratively

generating a population of potential solutions to a problem and select-

ing the best among them to produce subsequent generations

(Alhijawi & Awajan, 2023). They utilize a combination of mathematical

formulations and learning from previous generations to favor the

best-performing components or genes, which leads to improved solu-

tions over time (Alhijawi & Awajan, 2023). While genetic algorithms at

their core are not AI but analytics techniques when enhanced with AI,

they are able to tackle a wide variety of combinations in decision

problems within complex SCM operations around selling, sourcing,

manufacturing, and delivering goods or services (Toorajipour

et al., 2021). For instance, Nezamoddini et al. (2020) developed an AI-

enhanced genetic algorithm for integrated supply chains to improve

risk management. The study defined risk as unmet demand and con-

sidered three levels of decisions: strategic, tactical, and operational. In

comparison to the regular genetic algorithm, the AI-enhanced genetic

algorithm led to a 30% increase in profits and managed to keep inven-

tory levels at their lowest (Nezamoddini et al., 2020). This example

demonstrates that AI-enhanced genetic algorithms offer a distinctive

approach to optimizing international supply chains to empower

decision-makers to address multifaceted complexities within SCM.

New technologies associated with AI provide opportunities for

operations that can help create production efficiencies, lower costs,

and add value to customers (Beltrami et al., 2021). Smart factories

may use real-time data and AI to run autonomously and flexibly. Using

AI, smart factories can adapt and support different production scenar-

ios and manage different variable production configurations, demand

volumes, and manufacturing technologies (Ancarani et al., 2019). Data

from subsidiary activities can turn into improved business process

automation, productivity, agility, and actionable insights through a

wealth of contextualized machines, sensors, and AI. The proliferation

of AI technologies has driven the digital transformation of IB.

AI can play an important role in optimizing global value chains

(GVCs), as AI can assist in predicting market trends, ensure timely

190 MENZIES ET AL.

delivery of goods, and give businesses a competitive edge on the

global stage (Luo & Zahra, 2023). AI can also assist with enabling orga-

nizations to make data-driven decisions and improve operational effi-

ciency (Luo & Zahra, 2023). Researchers argue that global operations'

ecosystems become digitally connected, allowing codification and AI

to run algorithms and find patterns (Luo & Zahra, 2023).

The OECD (2022) also suggests that AI can generate efficiencies

in the logistics sector by managing smart warehousing and logistics

through improved predictions, better coordination of activities within

warehouses, and better logistics function. From a logistical perspec-

tive, AI is one of the key components of autonomous vehicles, often

used in mining, manufacturing, distribution centers, or even techno-

logically advanced container terminals (Luo & Zahra, 2023). Among

complex Internet of Things (IoT) systems, autonomous vehicles use

machine learning systems to interpret road signs, read maps, and rec-

ognize and react to danger factors (OECD, 2022). Furthermore, AI can

be used in airfreight and shipping to improve scheduling and optimize

the use of load space and capacity (OECD, 2022). The adoption of AI

can, therefore, enable firms to reduce production costs and be nimbler

in responding to changes in consumer demand (Meltzer, 2018).

A particularly challenging task relates to managing inventories

and warehouse space, requiring foresight in identifying changing

trends and optimizing shipments and schedules. AI systems, combined

with IOT and data analytics, can help create “smart warehouses” that rely on sensors, computer vision, and automated processes to

inform decision-making processes related to storage and logistics

(Meltzer, 2018). AI can improve predictions of future trends, such as

changes in consumer demand, and better manage risk along the sup-

ply chain. By enabling businesses to more effectively manage complex

and geographically dispersed production units, such tools significantly

enhance efficiency of GVCs. For example, businesses can use AI to

improve warehouse management, demand prediction, and the accu-

racy of just-in-time manufacturing and delivery. Robotics can increase

productivity and efficiency in packing and inventory inspection and

maintenance of assets along supply chains (Meltzer, 2018).

Meltzer (2018) suggests that AI could also create a trend toward

back-shoring production, as the opportunities for automation and 3D

printing could reduce the need for global supply chains that rely on

low-cost labor in developing countries. Instead, increased production

automation could mean that MNEs could backshore production in

developed and advanced countries rather than in developing low-cost

countries. This phenomenon has been identified by other researchers

as well (Dachs et al., 2019). Therefore, AI can assist with identifying

where, when, and what global resources should be deployed, helping

MNEs to reconfigure, maneuver, and repurpose existing resources

and capabilities for global operations.

3.7 | Sustainability in international business

AI is not only a driver of efficiency but can also be a catalyst for sus-

tainability in IB. It can play a crucial role in advancing the United

Nations Sustainable Development Goals by enhancing sustainability

practices, such as reducing carbon footprints and promoting responsi-

ble consumption. AI technologies can also address complex global

challenges, including climate change, poverty, and food insecurity

(Ciulli & Kolk, 2023). It is said that digital technologies will empower

different stakeholder groups as they interact with MNEs, making

interactions more transparent and developing better expectations

around diversity, equality, and social and economic justice (van Zan-

ten & van Tulder, 2018). As a result, technologies such as AI can liber-

alize various groups within a supply chain. As Luo and Zahra (2023)

suggest, AI technologies can help make manufacturing more efficient,

reducing waste and decreasing carbon emission into the atmosphere.

AI may also play a role in lowering greenhouse gas emissions

across GVCs by delivering advanced data-driven insights on carbon

footprints and increasing the efficiency of automated operations at

transport terminals, mines, and so on (Tsolakis et al., 2023). AI tech-

nologies are frequently utilized to calculate the most efficient routes

for couriers, as well as for rubbish removal trucks (Bidgoli, 2021). AI

and enhanced data analytics can potentially improve the environmen-

tal impact of various logistical activities in the long term.

3.8 | International human resource management

International Human Resource Management (IHRM) is defined as the

“management of human resources consistent with the strategic direc-

tion of the MNE in a dynamic, interconnected, and highly competitive

global environment” (Tarique et al., 2022). From an employment per-

spective, individuals working in MNEs may ask themselves whether

manufacturing jobs for electronics will be replaced by machines

that produce electronics more efficiently than human beings

(Ginsberg, 2023). AI could assist with “supporting more informed deci-

sions to meet employees' needs” (Ahi et al., 2022). Research on the

use and integration of AI in IHRM is rising due to its recognized

potential for value creation worldwide (Chowdhury et al., 2023). This

trend signifies a transformative shift in employee management and

overall firm performance, leveraging the innovative capabilities of AI

technologies (Vrontis et al., 2021). In the global competition for top

talent, where traditional recruiting methods struggle to keep pace

with competitive demands, the imperative for employers to adopt

advanced recruiting tools becomes evident (Chen, 2023). Notably, AI

significantly enhances day-to-day HR operations, particularly in

recruitment, leading to improved recruiter efficiency (Chen, 2023). Lit-

erature by Budhwar et al. (2022) advanced that AI integration within

HRM practices is often manifested in human resource planning,

recruitment and selection, training and development, compensation

and benefits, and performance management. For instance, the long

time it takes for humans to undertake the HRM function of planning,

selection, and recruitment is replaced by AI with its ability to quickly

process complex data and provide accurate information (Budhwar

et al., 2022). In the view of Budhwar et al. (2022), human-AI configu-

ration centers on employee-customer engagement, peer-to-peer

interaction, especially within the workspace, and management-

to-team engagement. Simply, the adoption of AI within the IHRM

MENZIES ET AL. 191

function is seen as a decision-support tool (Pessach et al., 2020).

Despite these advancements, comprehensive research on the utiliza-

tion and implementation of AI in IHRM remains limited and fragmen-

ted, signaling a need for further exploration of this evolving field

(Budhwar et al., 2022). As with any emerging technology, adopting AI

in IHRM brings forth both opportunities and challenges (Vrontis

et al., 2021).

The benefits and opportunities of the adoption of AI in HRM are

notably seen in recruitment, improved HRM strategies, task automa-

tion, and performance enhancement. The AI technologies most

employed in HRM are natural language processing, artificial neural

networks, and deep learning. Deep learning refers to the ability of a

computer to perform tasks that are natural for humans; however,

doing so with accuracy is often higher than that of humans

(Chen, 2023). Artificial neural networks are software-based AI tech-

nologies that simulate the structure of the human brain, consisting of

multiple interconnected neurons in which the information is stored

(Soori et al., 2023b). The benefits of AI applications are utilized in each

stage of the recruitment process (Chen, 2023), that is, job promotion,

applicant screening, applicant assessment, and coordination (Black &

van Esch, 2020). In job advertisements, AI is utilized for scraping data

from Social Media platforms to ensure high-profile candidates who

are not actively looking for jobs are presented with pop-ups or ban-

ners showcasing the job opportunity (Campbell et al., 2020). AI facili-

tates application screening by establishing a model of the ideal

candidate based on previous successful candidates, thereby predicting

the most suitable candidate (Ore & Sposato, 2021). In candidate

assessment, AI technologies can assist with developing interview

questions and gamified tasks to assess characteristics such as risk pro-

pensity (Black & van Esch, 2020). In recruitment coordination, AI tech-

nology can assist in multiple ways, according to Black and van Esch

(2020). For instance, candidates may be provided with the opportu-

nity to submit their LinkedIn profile instead of a resume. The AI sys-

tem then completes the resume on behalf of the applicant.

Furthermore, chatbots may inform applicants about the stage of

their application and answer relevant questions about the company,

salary, appropriate attire for the interview, or the start date (Black &

van Esch, 2020). This shows that AI revolutionizes recruitment pro-

cesses, improves efficiency, and reduces response times

(Horodyski, 2023), which may result in better applicant engagement.

At the task level, AI enables a streamlined application process, improv-

ing candidate experiences and causing a ripple effect that fosters a

favorable employer image, boosting employer branding and enabling

more diversified recruitment strategies (Ore & Sposato, 2021). AI sys-

tems can potentially deal with cognitive selection biases under race,

gender, and sexual orientation (Budhwar et al., 2022). As MNEs often

deal with heterogeneous labor forces, it is only prudent that high

degrees of objectivity and neutrality are undertaken during selection

and recruitment processes, which is made possible by leveraging the

capabilities of AI (Budhwar et al., 2022).

Furthermore, the integration of AI into HRM has strategic impli-

cations, fundamentally reshaping job design and work dynamics within

the IB (Vrontis et al., 2021). Moreover, natural language processing

and machine learning, that is, machines learning from experience with-

out ongoing human intervention (Kaplan & Haenlein, 2019), facilitate

support for journalists by automating basic tasks like scriptwriting,

freeing up time for investigative reporting and generating news at a

faster pace and larger scale (Jung et al., 2017). Advancing to the job

level, entire job functions are starting to be replaced with AI technol-

ogy (Vrontis et al., 2021). Notably, the advent of virtual assistant soft-

ware such as Siri or Cortana exemplifies this shift, revolutionizing

customer support by providing 24/7 assistance, which is especially

advantageous for IBs seeking to penetrate foreign markets without

the need for a physical presence (Glavas et al., 2019).

From a workforce planning, training, and development perspec-

tive, AI algorithms can accurately assess and identify the skills needed

by existing employees. This, in essence, guides the drafting and devel-

opment of training and development programs and workforces

(Budhwar et al., 2022). In the same vein, AI can estimate and/or deter-

mine the right levels of employee compensation with fewer biases

and subjectivity (Budhwar et al., 2022).

3.9 | Managing across cultures

According to the OECD (2022), virtual assistants may have a strong

role in better managing relations between people of different cul-

tures. For instance, AI systems can utilize software and programs

that can rely on natural language processing to assist with transla-

tion, and this automation can facilitate simpler communications in

global virtual teams. Zoom has the capability to conduct simulta-

neous translation between languages to facilitate cross-cultural

meetings (Zoom, 2023). This is cheaper, more efficient, and more

effective than paying human translators to do the job. AI can also

respond to spoken or written commands and questions. Previous

work identifies that improving communication between people of

different cultures can increase the amount of IB occurring, and

therefore, AI should have beneficial effects on cross-cultural collabo-

rations (Szkudlarek et al., 2020).

3.10 | International marketing

The use of AI in international marketing is gaining increasing attention,

as explored by Kopalle et al. (2022). AI's role in this domain is multi-

faceted: it includes the deployment of virtual assistants and chatbots,

which help reduce costs, answer consumer inquiries, and enhance

customer relations. Moreover, AI aids in refining the customization

and personalization of services and products (OECD, 2022). AI sys-

tems are instrumental in augmenting consumer experiences and tailor-

ing content to individual preferences (Huang & Rust, 2022).

Additionally, they contribute to more precise predictions in targeted

advertising, optimizing the reach and effectiveness of marketing cam-

paigns for various products and services.

MT can help respond to increased demand for niche services

traded across borders, and it can assist with professional document

192 MENZIES ET AL.

translations, audio-visual content translation, remote events, and

meeting interpretations (Horváth, 2022). Ethical concerns may arise

when employing MT for formal documents like contracts due to

potential biases in the AI's training data, data processing by external

companies, and undisclosed decision-making processes within the AI

system (Horváth, 2022). Hence, in the context of legal contracts, the

issue of accountability for potential translation errors remains unre-

solved, and it is suggested that humans conduct back translation to

reduce computer errors.

MT can also assist when conducting market research and surveys

or improving the personalization and customization of services

(OECD, 2022). Previous research indicates the benefits of MT in

Spanish-speaking markets (Brynjolfsson et al., 2018). These

researchers find that automatic MT facilitates online sales to foreign

markets, as the electronic MT learns how to translate different lan-

guages and uses tailored engineering to make it easier for buyers to

search and understand the details of items that are not listed in their

language (Brynjolfsson et al., 2018). This further enables digital plat-

forms to drive international trade and business and increase sales, as

in the case of eBay (Meltzer, 2018).

AI may also assist in international marketing by managing a firm's

brand in global markets and helping with the globalization of products

(Kopalle et al., 2022). Chatbots are common for customer-centric

businesses; therefore, AI could assist IBs in delivering their communi-

cations to potential and actual clients in foreign countries. In this

regard, it may assist with answering questions or helping customers

order their products or services online and be a more efficient way of

engaging with customers.

4 | DISCUSSION

Overall, our literature review observed that a small body of literature

examines AI and IB-type issues. Popular types of work included for-

eign exchange studies and global supply chains. There was also some

interest from an IMS perspective. Yet, there could be more research

to explore this topic or develop models using AI to more accurately

predict foreign market choices, conduct market analysis, and suggest

entry mode choices when entering markets. There was also research

on how AI could be used in the various aspects of IHRM, managing

across cultures, and in international marketing. As these papers sug-

gested, AI has the potential to significantly enhance efficiency in vari-

ous tasks, make more accurate predictions (such as in foreign

exchange and IMS), and automate and augment activities. Although

the adoption of AI in IB offers numerous advantages, it also presents

challenges.

4.1 | Challenges of using AI in IBs

As we identify in Figure 3, the challenges of using AI in International

extends to an employee, country/international, and organizational

perspective.

From an employee perspective, concerns around potential job dis-

placement are among the challenges organizations face (Vrontis

et al., 2021). This may lead to a shift in work structure and the nature

of work (Ahi et al., 2022). However, the opportunities presented by

AI, such as automation of manual tasks, enhancing personalization,

and improving customer engagement, may outweigh the challenges

(Vrontis et al., 2021). Reconfiguring work because of AI-IHRM integra-

tion brings employee apathy and probable attrition leading to a short-

term decrease in worker productivity (Budhwar et al., 2022). Reskilling

to augment the required work reconfiguration also becomes problem-

atic, especially in the short run. At the global level of workforce distri-

bution, especially within MNEs, ethical issues become problematic in

the adoption of AI for IHRM productivity (Budhwar et al., 2022). The

nonavailability of global standards and regulations to govern AI adop-

tion brings many ethical challenges when handling different employee

groups within the workplace and their employee data (Stahl, 2021).

While concerns exist regarding AI's potential to replace recruiters

entirely, this is unlikely, given that AI systems have been designed to

relieve recruiters from mundane tasks in the recruitment process

(Chen, 2023). Instead, human-AI collaboration focuses on improving

decision-making (Vrontis et al., 2021), as AI systems possess far

greater analytical and computational information processing capacity

than humans, which allows them to swiftly analyze large datasets,

identify patterns, and derive insights. In contrast, humans utilize intui-

tive approaches to decision-making (Jarrahi, 2018). This underscores

the principle of intelligent augmentation, advocating for AI systems to

augment rather than replace human capabilities (Jarrahi, 2018).

Although AI promises to mitigate recruiter bias by eliminating subjec-

tive criteria, the potential amplification of bias arises from AI systems

trained on biased datasets (Chowdhury et al., 2023). This was evident

by Amazon's recruitment AI inevitably excluding female candidates

due to biased training data, as the AI system had been trained on

resumes from high-performing job incumbents over the past 10 years,

which happened to be only males (Dastin, 2018). The AI system solely

selected male candidates in line with the training data. Amazon has

since discontinued this AI system (Dastin, 2018). One negative for

employees may be a breach of privacy in the workplace, as organiza-

tions may use employee data to facilitate their machine learning and

data analytics.

From a country/international level, the OECD (2022) points out

challenges to using AI across borders, which include government pro-

tectionist policies preventing organizations from providing digital ser-

vices across countries. Trade agreements and regulations around AI

are also lacking or nonexistent, especially in developing countries that

may have other more pressing issues to attend to. Other challenges

pertain to the regulatory and policy environment around AI, including

cyber security and privacy of people's data, especially as these may

need to be shared across countries (Marwala, 2023). AI regulation at

an international level is important, and countries are currently discuss-

ing these issues at the World Trade Organization level or in regional

trade agreements on the implications of AI (Jones, 2023). As countries

grapple with different aspects of technology, the number of national

and international AI regulatory instruments will likely increase, which

MENZIES ET AL. 193

might impede innovation (OECD, 2022). Another challenge of AI is

the widening gap in the adoption and implementation of AI among

countries, companies, and workers, especially in developing versus

developed countries (Fornes & Altamira, 2023). This may mean some

countries will significantly benefit from the adoption of AI, while other

countries will lag.

From an organizational perspective, limited sector-specific data,

especially within industry and services sectors, has been found in the

literature to be one of the challenges to using AI in IB (Neubert & Van

der Krogt, 2018). Other hindrances to AI adoption have been the lack

of clarity on the benefits of the software solution, lack of firm-specific

relevance, specific firm-level AI management challenges, and

employee training costs (Neubert & Van der Krogt, 2018).

Access to trained IT professionals and programmers is key for

developing AI solutions, and there appear to be barriers to the move-

ment of people across countries with these skills. Lowering these bar-

riers would facilitate the transfer of AI-related knowledge and skills

(OECD, 2022). There also appear to be large talent gaps in AI-trained

staff, requiring combined solutions from governments, organizations,

and educational providers to address this issue (Tamayo et al., 2023).

Data localization measures that restrict the ability to move data glob-

ally will reduce the capacity to develop tailored AI capacities

(Meltzer, 2018).

For decades, AI has been expected to expand automation and

speed up job losses for low-skill, blue-collar workers in the

manufacturing field (Ginsberg, 2023). With the emergence of genera-

tive AI, such as ChatGPT, Bing, and Bard, a reversal has been initiated,

as these new AI technologies excel at accomplishing tasks traditionally

performed by knowledge workers, potentially rendering some of their

roles redundant (Cain Miller & Cox, 2023). It will be a continued chal-

lenge for organizations to transition these workers.

4.2 | Limitations and suggestions for future research

Our study, while thorough, encountered certain limitations that future

research could address. A key limitation is the scope of our database

search. Expanding future searches to include databases like Google

Scholar and Scopus additionally could uncover further insights.

Another limitation is the limited number of existing studies on AI in

IB. As AI is a rapidly evolving field, more studies are likely to emerge,

enriching future reviews with a broader spectrum of data and per-

spectives. It is essential for future research to continuously adapt to

the swift advancements in AI, ensuring that findings remain relevant

and reflective of the latest technological developments in the

realm of IB.

In this article, we explored past research examining how AI can be

used in the various IB strategies, practices, and activities. Despite this,

our review suggests many areas for future research that can explore

the role of emerging technologies, such as AI, for IB.

There has been a growing interest in the approaches of AI and

business; however, there has been limited attention to the bigger pic-

ture of how AI may require a range of organizational changes at the

organizational, behavioral, and managerial levels (Luo & Zahra, 2023).

AI may have the impact of reconfiguring the different types of inter-

national strategies and practices MNEs use, which deserves close

analysis for future study. Researchers could use theories of change,

such as Lewin's three-stage change model (Hussain et al., 2018) to

explore change around AI implementation in IB.

From an IMS perspective, research could examine how AI can

contribute to better IB data collection, decision-making, and market

selection choices. Can AI create better models, algorithms, learning,

and tools to make the most efficient and effective decisions around

F IGURE 3 Challenges of implementing artificial intelligence in international businesses.

194 MENZIES ET AL.

market choice? Researchers could use established IMS models such as

the CAGE distance model (Ghemawhat, 2001) or more recent models

(Ozturk et al., 2015) to develop AI-inspired models that enhance bet-

ter IMS decision-making. Future researchers could examine the extent

to which AI could be used to make decisions around entry mode

choice. For example, can Dunning's eclectic/OLI paradigm

(Dunning, 2001) be created into an AI model that helps MNEs decide

what entry mode to choose, given a particular country, the organiza-

tion's resources, and competitive conditions?

Raisch and Krakowski (2021) provide theoretical insights into the

automation versus augmentation paradox. MNEs will need to assess

the degree to which they wish to automate processes and activities

within their IB or whether they see AI augmenting work and examine

the extent to which different IB strategies and practices should be

automated or augmented (Raisch & Krakowski, 2021), from the per-

spective of AI.

Future studies could also examine how AI can assist in managing

cross-cultural relations between people of different countries in the

MNE. Can AI natural language processing improve global virtual

teams' relations, particularly natural language processing and transla-

tion? Questions about whether “AI can help reduce the possibility for

misunderstanding and increase the performance of global virtual

teams?” could be the focus of future research and theorizing.

There also appear to remain grey areas that need further investi-

gation by researchers when considering AI and IHRM. Empirically and

theoretically, the adoption of AI within digitalized co-working space

has received very little inroads (Grønsund & Aanestad, 2020). The

cross-country dynamics within co-working space and AI adoption

remain elusive and underresearched. While there have been inroads

on how AI can make the various IHRM practices more efficient, some

of the issues that staff might face due to AI implementation, such as

ethics and privacy (Stahl, 2021), could be the focus of future studies.

Future studies could also examine the ethical considerations of AI

adoption in IB, studying privacy, bias, and the societal impact of AI-

driven decisions. What are the best practices for managing and imple-

menting AI systems in a business? In addition, future researchers

could investigate the regulatory challenges, for example, data protec-

tion, intellectual property, and compliance with international laws

associated with AI deployment across different countries and regions

(Loureiro et al., 2021).

While some studies have presented a negative story about the

increased use of AI technology, others have focused on the advan-

tages without fully recognizing some adverse effects on society. In

the future, research may investigate the role that AI plays in easing

the operations of IB and fostering growth in emerging economies, tak-

ing into account the specific problems and opportunities given by

these diverse environments.

Future research could examine how AI technology can propel

innovation in IB, emphasizing product development, service delivery,

and market distinction. A relevant research question could be how

businesses can more effectively utilize deep learning generative

models to automate knowledge creation. How can this be diffused

throughout the MNE to create new products, services, and processes?

The diffusion of innovation theory (Rogers, 2003) may be a useful the-

oretical perspective to explore this.

4.3 | Managerial implications for IBs

Research suggests that changes in global organizations necessitate

changes in team composition and decision-making processes (Luo &

Zahra, 2023). Chief Technology Officers need to understand what is

required to implement technological change around AI and how staff

need to be trained to build this capability. Staff across the business

may require training in cloud computing, AI, and various software to

be digitally capable. Even staff who are not IT staff or programmers

may benefit from understanding the capabilities of AI. This knowl-

edge, training, and understanding may be a breeding ground for inno-

vation from an organizational, product, and process perspective. It

may be important for MNEs to develop digital mindfulness and trans-

formational leadership within their organizations as a change process.

It will be important for MNEs to create digital architectures and to

center on organizational architecture, with both mutually complemen-

tary and supportive of each other. The digital architecture should sup-

port interoperability, integration, and extension (Nambisan &

Luo, 2022), supported by data-driven, end-to-end business processes

based on industrial AI (Kohli & Melville, 2019). Given that highly

trained knowledge workers will dominate operations, MNE leaders

need to reconsider their cultures, create greater employee participa-

tion opportunities, and counteract employee concerns about privacy,

excessive organizational control, and dehumanization (Luo &

Zahra, 2023). MNEs must build their absorptive capacity because AI is

an emerging technology, and it builds on other emerging technologies

that will embody complex and radically new knowledge that is difficult

to comprehend and absorb.

Luo and Zahra (2023) provide insights on implementing technolo-

gies such as AI into IBs and suggest that it should be embedded into

business processes to assist with running operations. They suggest

that the combination of digitalization, intelligence, technology, and

innovation gives MNEs a plethora of opportunities as well as substan-

tial shifts in the manner in which they extend their worldwide reach,

facilitate connections with both internal and external stakeholders,

and streamline global operations in a manner that is both more effi-

cient and productive (Luo & Zahra, 2023). They further suggest orga-

nizations should build analytics and processes for IB in an

independent manner at the “Edge” of a central cloud infrastructure;

they recommended exploring the benefits of conducting analytics at

the edge for IB operations, enabling real-time decision-making at the

point of data generation. GVCs should be centered around digital con-

nectivity, AI, and blockchains, which should be combined with techno-

logical advancement and creative thinking. Digital platforms should be

created so that internal members (employees) are able to collaborate

with external members.

We also suggest employing AI professionals to enhance the orga-

nization's AI capability. This means it is important to undergo organi-

zational change and build up digital technologies and dynamic

MENZIES ET AL. 195

capabilities to help address and implement technological change. It is

also important to build up intelligent machines and smart automation

of business activities, advancing a workplace vision that values inter-

connectivity, smart automation, machine learning, and real-time data

(Ahi et al., 2022).

It will be important for managers to navigate the ethical and legal

landscape of AI, ensuring compliance with international standards and

resolving concerns connected to data privacy and security. Further-

more, the incorporation of AI may entail the workforce's upskilling

and the redefinition of job responsibilities. This scenario calls for pro-

active leadership to manage the transition and cultivate a culture of

continuous learning. In addition, as AI-driven breakthroughs continue

to transform industries, managers need to remain sensitive to disrup-

tions and adaptable in modifying company strategies to stay ahead of

the curve. Managers are positioned to be important orchestrators

of this technological growth within their organizations since the effi-

cient application of AI in IB requires strategic preparation, thoughtful

execution, and a commitment to ethical and privacy issues.

5 | CONCLUSIONS

AI is transforming many facets of operations and strategy, with signifi-

cant practical ramifications for MNEs and IBs. Decision-making is one

crucial area where AI-driven analytics leverage massive datasets to

produce actionable insights, assisting businesses in making timely and

well-informed decisions across international marketplaces

(Brynjolfsson & Mcafee, 2017; Davenport et al., 2020). Companies

may anticipate consumer behavior, market trends, and geopolitical

risks using AI-powered predictive analytics. This allows for proactive

measures to manage uncertainties in IB and investments

( _Zukrowska, 2021).

In this article, we contributed to the academic discourse by deliv-

ering a narrative literature review that explores the dynamic ways in

which IBs can integrate AI into their diverse strategies, activities, and

practices. This exploration is not only insightful but pivotal for IBs

aspiring to implement AI in their business models effectively. We

identify and address the multifaceted challenges associated with

implementing AI in IB, highlighting the complex implications at

employee, organizational, and international levels. Furthermore, our

work makes a substantial contribution to the field by offering sugges-

tions for future research directions, thereby setting a new agenda for

scholarly inquiry in this rapidly evolving domain.

The use of AI in IB is rapidly transforming global operations. AI

technologies are essential tools for optimizing GVCs, making

data-driven decisions, and enhancing sustainability practices.

While challenges exist, the opportunities offered by AI are immense,

and the adoption of these technologies is critical. As the digital revolu-

tion continues, IBs must harness the power of AI to thrive in an ever-

evolving global marketplace. MNEs/IBs need to look at how they can

build capabilities to be AI literate and how to implement AI for better

IB processes, efficiency, and success.

ACKNOWLEDGMENT

Open access publishing facilitated by University of the Sunshine

Coast, as part of the Wiley - University of the Sunshine Coast agree-

ment via the Council of Australian University Librarians.

DATA AVAILABILITY STATEMENT

Data sharing not applicable to this article as no datasets were gener-

ated or analysed during the current study.

ORCID

Jane Menzies https://orcid.org/0000-0002-7685-8586

Bianka Sabert https://orcid.org/0009-0001-2942-7660

Rohail Hassan https://orcid.org/0000-0002-7825-0283

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AUTHOR BIOGRAPHIES

Dr Jane Menzies is a senior lecturer in International Business, and

Program Coordinator for the Master of Business Administration

and Postgraduate Programs at University of the Sunshine Coast.

Her research interests are in internationalization of firms, transi-

tional issues of international students, women's entrepreneurship,

and gender issues in organizations. Jane has published in Interna-

tional Business Review, Gender & Education, European Management

Journal, Thunderbird International Business Review, International

Journal of Consumer Studies, International Journal of Conflict Man-

agement, Management International Review, Human Resource Man-

agement Review, International Trade Journal, Australian Journal of

Career Development, and a range of education journals. Jane pub-

lished a book with Routledge in 2018 on “Innovation and Interna-

tionalisation: Australian SMEs in China.” Jane has over the years

been awarded grants at the Australia China Council (2013) and

more recently at the Council for Australia Latin America Relations

(2022) both at the Department of Foreign Affairs and Trade.

Dr Bianka Sabert is a sessional academic at the University of the

Sunshine Coast. Her main teaching areas are in Management and

IT for Business. Her research primarily focuses on Strategic Man-

agement and International Non-Profit Organizations. However,

with over 10 years of experience in various IT positions in

Australia, Africa, United States and Europe, her research interests

extend into the emerging field of Artificial Intelligence. She has

published on the topic of Performance and Control in Interna-

tional Non-Profit Organizations and is currently developing her

research profile. She also delivers a short-course on Artificial Intel-

ligence for Business.

Dr Rohail Hassan is a Senior Lecturer of Corporate Governance

and Finance/Director of Accreditation, Rankings, and Reputation

Management at Othman Yeop Abdullah Graduate School of Busi-

ness, Universiti Utara Malaysia, and Chartered Fellow: FCMI by

CMI, UK. Besides, Rohail is working as an Adjunct Professor of

Management at the University of Economics and Human Sciences

in Warsaw, Poland. Rohail is working as an Expert at the

European Commission for the Evaluation of Proposals in the

HORIZON-MSCA-2023 Call. His research interests include corpo-

rate governance, board diversity, gender diversity, ESG, Block-

chain and Artificial Intelligence in business, big data and analytics,

and strategy. The main research has been published in leading

management journals and top-tier peer-reviewed journals ABS

ranked like (e.g., Journal of Business Research (3*), Asia Pacific Jour-

nal of Management (3*), IEEE Transactions on Engineering Manage-

ment (3*), Technological Forecasting and Social Change (3*),

International Journal of Information Management (2*), Journal of

Management and Organization (2*), Maritime Policy & Management

(2*), Journal of Cleaner Production (2*), Journal of Intellectual Capital

(2*), Thunderbird International Business Review). He is currently

working as a Series Editor for the work titled “Big Data for Industry

4.0: Challenges and Applications”—by Taylor & Francis Group.

Prince Kofi Mensah holds an MSc degree in Development

Finance from the University of Ghana, has over 10 years' experi-

ence in the private sector with focus on entrepreneurship,

finance, business development, research, and SMEs. He is the

Director of Business and Research at AAMG Business Solutions

Ghana, a course facilitator and research consultant at Skill Central

Ghana, a Research Consultant at New York University-Centre for

Technology and Economic Development Ghana, and a Project

MENZIES ET AL. 199

Consultant at the Chamber of Agribusiness Ghana. He has practi-

cal experience in both quantitative and qualitative research.

Prince has published in Journal of Comparative International Man-

agement and provided review services for Journal of Entrepreneurship

in Emerging Economies. Prince made two presentations during the

Financial Innovation and Enterprise 2023 Conference organized

at the University of Professional Studies Accra (UPSA) with focus

on: (1) Sustainable Entrepreneurial Finance: creating a research

agenda through a systematic literature review, and (2) Entrepreneur-

ial education and training: Creating a research agenda through a sys-

tematic literature review.

How to cite this article: Menzies, J., Sabert, B., Hassan, R., &

Mensah, P. K. (2024). Artificial intelligence for international

business: Its use, challenges, and suggestions for future

research and practice. Thunderbird International Business

Review, 66(2), 185–200. https://doi.org/10.1002/tie.22370

200 MENZIES ET AL.

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