Artificial intelligence’s impact on international business
The Fourth Industrial Revolution will dramatically change the way people live, work and connect with one another around the world. Unprecedented technological advances are redefining human activities and even question what it means to be human in the 21st century. The last decade has witnessed incredible advances in artificial intelligence (Al). From the automation of production lines to driverless cars, these technological innovations contribute to increased economic growth and create major shifts in the way firms operate both domestically and internationally.
The Encyclopaedia Britannica defines Al as “the ability of a digital computer or computer-controlled robot to perform tasks commonly associated with intelligent beings”. Such tasks include reasoning, discovering meaning, generalising and learning from past experience. The term “machine learning” refers to digital systems’ ability to use the data derived from these intellectual processes to predict what customers want and deliver that information quickly.
In international business, data analytics and translation services are already reducing cross-border barriers to trade. It remains that Al’s further development in this area still faces a number of challenges. Neither utopia nor dystopia, Al is likely to resolve certain problems but also create new ones relating to social inequality, MNE configurations, data privacy, global governance and human resources.
Insofar as Al’s development boosts productivity, it is likely to have a positive impact on economic growth and therefore create new cross-border trade opportunities. The problem is that countries may not reap Al’s productive benefits in the short run because of the time it takes to leverage the benefits of the investments in training or in new and better business practices.
Al is also likely to affect economic growth by helping countries accelerate their transition into a service economy. Drawing on the VRIO framework (see Chapter 7), Al gives prominence to those skills that add production value (exemplified by the growing tendency towards embedding services into goods sold internationally). In this way.
it helps to make global value chains more efficient. On the other hand, there is widespread worry about the rise of Al-induced unemployment, especially where low-skilled or traditional blue-collar manufacturingjobs are concerned. With Al as with so many other innovations affecting the future of international business, the disadvantages need to be analysed in conjunction with the benefits.
Global supply chains
Al helps MNEs manage supply chain risks by improving both warehouse inventory management and delivery accuracy in lean manufacturing regimes. Similarly, the deployment of the Internet of Things (loT), which interconnects computers, materials, supplies and customers, enables production systems to self-regulate and thereby become more flexible in terms of their ability to meet market demand. loT also further improves communication and maintenance up and down the supply chain, ensuring that production is more closely tailored to customer specifications — always a difficult proposition in international business.
At the same time, the arrival of suppliers with advanced capabilities in robotics, design and R&D could have a significant effect on MNEs’ existing configurations. There is every chance that Al will partially reverse the offshoring and international outsourcing trends that have dominated international business for the past 30 or 40 years. This is because when automation and 3D-printing shape the production process, the negative aspects of having internationalised supply chains (including logistics, quality and control problems) become more apparent, especially where products reliant on low-cost labour and economies of scale are involved.
Cross-border digital trade
Al also enables SMEs to compete globally thanks to digital platforms like eBay, as well as digital translation services. This latter facility is particularly interesting because of its potential for driving demand by facilitating communications with all sorts of foreign language speakers. One example is the 17.5% increase in eBay’s US exports to Spanishspeaking Latin-America Another was Google’s 2018 announcement that its deep learning-based Neural Machine Translation (NMT) system could reduce translation errors by 55%—85% compared to the previous generation of Google Translate. In short, Al may support SMEs’ internationalisation by reducing language barriers and enabling greater access to customers worldwide. At the same time, managers do need to remain aware of how crucial cultural awareness and language skills remain when conducting business internationally (see Chapter 5). Google Translate or NMT are not yet able to host a meeting with a Spanish executive or Indonesian partner.
Data and trade negotiations
Data has turned into a factor of prime importance in modern international businesses due to the economies of scale it generates. Statistical predictions from Google or Alibaba, for instance, improve with the quality and quantity of data available. By having access to more data, Al helps MNEs to discern potential variations in international customers’ intent with greater accuracy. The same applies to analysis of large product, tariff and trade policy databases - exemplified by Brazil’slntel-ligent Technology and Trade Initiative, which uses Al to improve trade negotiations by calculating different trade scenarios together with their economic impact.
Data alone does not give meaningful results, however. Instead, it is the the type of data (and accumulated searches from previous customers) that affects the quality of search results. The virtuous circle for data — reliant MNEs therefore starts with getting more customers to generate more data; improving in turn service quality; and using this improvement to further grow market share. The economies of scales achieved through this mechanism derive from positive direct network effects (also called network externalities) where the value of a service increases as the number of users rises. This is redolent at a technological level to the international business version of Granovetter’s Network Theory (see Chapter 6), where clusters develop in large part because success breeds success.
Note that the Al model contrasts with existing trade theories since economies of scale are viewed here as being based on maximal exploitation of existing fixed costs and/or on maximum matching of market supply and demand. Traditional trade models are not particularly relevant to the world of Al, however. The emphasis here is on the nature of competition and access to data.
Global regulation of Al
Managing cross-border data flows intimates a need to subject this whole domain to regulations that are also global in nature. Customer privacy and data security have received enormous worldwide attention in recent years, with the so-called GAFA Big Tech MNEs
The future of international business 143 (Google, Apple, Facebook, and Amazon) all being closely scrutinised in different national jurisdictions for the way they collect and use personal data. Customers around the world seem to have become increasingly cautious about sharing private information and fear losing control over this asset. MNEs and other businesses, together with national and international institutions, are constantly under threat of seeing their information and data systems breached, potentially resulting in a fraudulent use of customer data. Hence the emergence of a number of data protection policies and regulations, with one leading example being the EU’s General Data Protection Regulation (GDPR) protocol. Despite these steps, however, there is still a dearth of global regulation as regards the cross-border availability, anonymisation and circulation of data.
Al and international business practitioners
Developing a national workforce with Al skills has become a priority strategy for many policy-makers worldwide. Due to the global shortage of talent in this area, MNEs seeking competitive advantage have increasingly joined and sometimes even superseded these state initiatives in a bid to attract, develop and retain the kinds of talents who are capable of sharing the tacit knowledge and collaborative innovation with which Al is associated. Cities everywhere are devising public-private initiatives that they hope will turn them into Al hubs and global talent magnets. These investments can vary from vocational training and skills enhancement (with its STEM science, technology, engineering and mathematics focus) to the kind of life-long learning needed to help mature professionals adapt to digitally enhanced work and business practices.
It is crucial to remember that international business is ultimately not a quantitative discipline but instead a social science, with all this implies about mastering human beings’ so-called softer skills set, starting with creativity, independence and dynamism. In the end, international business practitioners never really manage companies — they manage people. And that is a whole other business.