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The global impact of artificial intelligence (AI) will be profound — to some extent, it already has been, and much more development lies ahead.
Table of Contents
- What is Artificial Intelligence?
- Key Components of AI
- Famous Examples of AI in History
- Types of AI
- The Impact of AI on the World Economy
- How AI Is Helping Companies Expand Globally
- How Will AI Impact Global Expansion in the Future?
- Learn More About Globalization Partners’ Global Expansion Platform
International business growth, artificial intelligence, and global expansion often go hand in hand. The McKinsey Global Institute recently analyzed economic data from the United Nations, the World Bank, and the World Economic Forum and reported that by 2030, AI has the potential to add 16% — or about $13 trillion — to the global economy. It could also boost the global gross domestic product (GDP) by up to 26%. McKinsey also reports that by the same year, at least 70% of companies are likely to have adopted at least one form of AI technology — perhaps computer vision, advanced machine learning, natural langauge, robotic process automation, or virtual assistants.
But what is AI exactly, and how does it contribute to global expansion? Below, we’ll explain how AI works, how it impacts economic growth, and how it is likely to contribute to global expansion both now and into the future.
What Is Artificial Intelligence?
The father of artificial intelligence is widely considered to be Alan Turing, perhaps most famous for his codebreaking computer that helped the Allies during World War II. Turing also proposed what later became known as the Turing test – a test that a computer could pass only if its responses to questions were indistinguishable from human responses.
Since the 1950s, scientific minds have puzzled over what constitutes “thinking” and “intelligence” when it comes to machines and the algorithms they use to interpret data and answer questions. AI can be tricky to define, but typically, a machine is thought to have artificial intelligence if it responds to queries in the way humans would generally respond and if it is capable of making decisions that require a human level of intelligence.
Key Components of AI
A recent paper from the Brookings Institution argues that AI has three main qualities:
- Intentionality: In terms of AI, intentionality means that the system makes decisions, rather than merely being programmed with predetermined responses. Intentionality requires AI to understand the meaning of a query and parse data with the intent to determine the answer. To do so, AI systems must combine data from different sources, analyze it immediately, and act on the conclusions it draws.
- Intelligence: The intelligence of AI often comes from its interaction with machine learning and data analytics programs. The combination of these technologies enables what we think of as intelligent decision making. For example, an AI system that assigns students to different school systems cannot rely on rote computations — it must also weigh values such as equity and justice to create beneficial outcomes for all.
- Adaptability: Adaptability means AI systems adjust as they receive new data, make decisions, and interpret the outcomes. If financial or environmental conditions change — or, in the case of self-driving cars, if road conditions deteriorate — AI can take the new data into account and adjust its decision making accordingly.
Additionally, AI incorporates machine learning and deep neural networks.
Machine learning uses vast quantities of data and sophisticated computer algorithms to make predictions. Enormous quantities of data are crucial because the machine-learning algorithms need to evaluate as many past outcomes as possible to produce correct predictions. As AI develops and becomes more capable, it is likely to become able to predict with better and better accuracy.
Machine learning can be either supervised or unsupervised. In supervised machine learning, the data comes with helpful information, such as labels or interpreted facts — say that water freezes at 0 degrees Celsius — to help the machine learning progress more quickly. Unsupervised machine learning provides only data without associated labels or facts, so the algorithm must learn to interpret patterns and make correct interpretations on its own. Unsupervised machine learning includes what is known as reinforcement learning, in which algorithms choose and produce their own data as they learn.
AI also works by using what are known as deep neural networks, or DNNs. Deep neural networks combine several learning tasks into one package to create general-purpose machine learning, or GPML. The benefit of GPML is that it can easily make sense of an assortment of inputs, such as video, audio, and textual information.
Famous Examples of AI in History
AI systems have made headlines over the past couple of decades. IBM’s Deep Blue computer could beat grandmasters at chess over 20 years ago, and more recently, its Watson has beaten Jeopardy champions at Jeopardy.
IBM’s AI experiments have existed for decades, but they have gradually become more sophisticated with the creation of more powerful algorithms. Winning at Jeopardy, for example, is a bigger challenge than winning at chess because Jeopardy is a language-based game. So the machine has to parse the infinite nuances of language, idioms, cultural references, and other aspects of human communication rather than merely strategizing about movements on a board.
One reason AI programs are so adept at chess is that they approach the problem differently than humans do. When we play chess, we typically employ a mixture of pattern recognition and intuition as part of our strategy. A computer also performs pattern recognition, but in seconds, it can also search through a massive database of possible positions and outcomes to figure out its best move. When Watson learns to play Jeopardy, it does much the same thing — it uses statistical and rules-oriented approaches to interpret the questions and narrow down the answers. Then it incorporates feedback from the results it gets — so that gradually it can determine which algorithms work best and under what circumstances. This “learning” helps the system find answers more accurately in the future.
Of course, most businesses don’t need their computers to play chess or win trivia games. But the same qualities that help AI win chess matches and Jeopardy games can translate into professional applications as well.
For example, replacing doctors with AI systems is unlikely because many of the nuances of diagnosis, disease treatment, and the patient-physician relationship are hard to encapsulate in a data set. But AI can provide a helpful complement to a human physician. An AI program could scroll through hundreds of thousands of potential diagnoses or treatment protocols and offer suggestions in a medical setting. Watson has already proved useful in this way because of its speech-recognition and machine vision capabilities. It can analyze radiological images, for example, and communicate its findings to doctors.
Types of AI
AI can be broken down into two types: narrow AI and general AI. Narrow AI, which exists in many applications today, is the AI built to accomplish specific, defined tasks. It can be found in chatbots, speech-recognition programs, automatic translation services, and self-driving cars. Computers at companies like Amazon, Google, and Netflix also use AI to analyze consumers’ browsing, buying, and watching patterns and use those patterns to make personalized recommendations.
General AI, which we have the potential to develop into a more powerful tool in the future, encompasses machine learning systems that can be used in a broad variety of applications. In its ideal form, general AI can learn more quickly than humans, and its abilities can surpass human capabilities on intellectual and performance tasks.
Computers cannot currently communicate exactly like humans, and they also have limitations in terms of how well they can “explain” their choices or recommendations when queried. But they offer many advantages in professional settings.
The Impact of AI on the World Economy
The global economic impact of AI is already substantial. AI impacts economic growth in a variety of ways:
- Increasing productivity and trade opportunities: One of AI’s impacts on economic growth comes through its macroeconomic effects. For instance, when AI increases productivity growth, that productivity growth increases economic growth as well. It also increases opportunities for international trade.
- Better management of complex production units: AI helps businesses better handle complex, far-flung production units by providing a centralized management system. For example, a business can use AI to manage its warehouses more efficiently, predict consumer demand, and improve the accuracy of its quick-turnaround and delivery systems.
- Expansion of digital platforms: Trade via digital platforms is possible because of AI technology. The online auction site eBay, for example, uses AI to automate its operations.
How AI Is Helping Companies Expand Globally
Global expansion and AI have had a fruitful partnership. AI helps companies expand globally in numerous ways:
- Easy expansion through digital platforms: AI automation via digital platforms provides a convenient way for companies to expand internationally. In the United States, 97% of small businesses that are active on eBay, which uses AI, export some of their products. In comparison, only 4% of offline businesses that do not use AI export their products.
- Accurate translation services: AI also provides instantaneous, accurate translation services that improve dialogue, decrease miscommunications, and make international cooperation much more streamlined and effective. Using AI translations in business has been shown to have a positive effect on trade revenues — an effect that’s equivalent to decreasing the distance between the countries by over 35%.
- Improving trade negotiations: AI doesn’t just enhance communications — it enhances their outcomes as well. AI can be used to analyze the economic paths of negotiating partners in various scenarios, predict how different variables in the trade scenario will affect outcomes, and predict trade responses from countries that are not party to the negotiation. Brazil, for example, has developed an Intelligent Tech + Trade Initiative that emphasizes including AI as a component of trade negotiations.
- Supply chain management: AI systems can also respond to the supply chain in real time. They can detect patterns and trends, and they can predict where and when demand will increase. They can also automatically increase production to meet that demand — or they can decrease production to respond to decreased demand, thus reducing wasted labor and surplus inventory. For expanding businesses that need a way to figure out how to supply the optimal number of products to a new market, AI has proven invaluable.
- Automating routine tasks: When companies expand, they typically want to focus their energies on higher-level tasks like strategy and less on lower-level tasks like bureaucratic matters. AI can help by automating routine, bureaucratic tasks. For example, as companies incorporate new workers in different countries, they may struggle to manage tasks such as payroll and providing benefits. AI can help automate these tasks and save human workers from hassle and frustration.
- Increased efficiency and accuracy: AI can also streamline different processes within a company by making them more efficient and accurate. If a human employee is doing payroll tasks or enrolling employees in health insurance plans, he might make a mistake or two, leading to delays, incorrect payments, or lack of coverage. With an automated system that never gets tired or distracted, the likelihood of errors becomes much smaller. And an AI algorithm can complete its computations and data entries faster than a human employee can, increasing efficiency as well.
How Will AI Impact Global Expansion in the Future?
AI is likely to impact global expansion in the future much more forcefully than it does today. One reason is that adopting new technologies and incorporating them effectively takes time. As companies use AI more and understand better what AI can do for them, they will be able to make more effective use of its capabilities. And as AI improves, its impact will increase as well. The McKinsey Global Institute estimates that because the effects of AI are likely to show logistic growth, increasing along an S-shaped curve, AI’s impact on the global economy will be three times higher in 2030 than it is today.
In the future, AI is likely to impact global expansion in the following ways:
- Improving predictions of future trends: Much of an international business’s success is predicated on its ability to predict and respond to future trends. AI can help in this area by accurately forecasting those trends with predictive modeling and allowing companies to make more informed decisions as they expand into international markets.
- Increased smart manufacturing: Smart manufacturing requires interconnectivity between sensor systems, cybersystems, and physical machinery. As AI becomes more sophisticated, it can improve smart manufacturing tremendously by specializing and streamlining specific processes. AI can also allow for constant manufacturing, at any hour of the day or night, to increase productivity. And its sensory systems — along with its decreased reliance on human operators — can increase safety on the manufacturing floor as well.
- Increased ability to parse evidence and make conclusions: In the future, AI may be used to read and interpret huge volumes of data. In the legal field, for example, AI could function in much the way a paralegal does, but at much higher speeds — reading large amounts of case precedent and collecting relevant information for use on current cases.
- Increased automation of tasks: In human resources (HR) processes, for instance, automation can help streamline recruiting, onboarding, training, payroll, and providing benefits. And more sophisticated forms of AI will likely be able to parse tax law and international regulations much more quickly and effectively than humans can. So automating these processes through AI will help companies save time and labor and decrease their vulnerability to penalties.
- More reliable autonomous vehicles: Autonomous vehicles, or self-driving cars, have many sensors — specifically, radar and light detectors — that gather information about objects around the vehicle. The AI system uses this data to make instantaneous decisions about how close objects are, whether there are hazards on the roadway, and what path it needs to take to avoid them. Autonomous vehicles can help companies save money as they expand — they won’t have to pay drivers. They also won’t have to train employees to learn new road rules in different countries since an AI system can learn them instantly. And developments in AI can make these vehicles safer and increase their presence on the road.
- Increased accuracy and efficiency: We may think our AI processes are relatively efficient now, but some bugs exist in AI systems. Have you ever called the automated pharmacy line to refill a prescription, only to have the AI system get confused and transfer you to a human to straighten out the problem? Or have you ever interacted with a chatbot that couldn’t provide useful information? In the future, more sophisticated systems will lead to improved, almost humanlike performance and a reduction in errors and limitations.
- Increased focus on business innovations: When companies must focus less on routine, day-to-day processes, their mental and creative resources become free to do higher-level work. In the future, as AI becomes more sophisticated, it will take on more and more administrative roles within a company. So it will free up company brainpower to take on more intellectual challenges and make creative leaps. An increased focus on vision and creativity will likely lead to international success.
- Cost-effectiveness: AI is more cost-effective than human employees because it requires only a purchase cost. It does not need a salary, raises, health benefits, or retirement contributions. It also never gets sick, misses work, or experiences losses in productivity. As companies adopt AI more and more, they are likely to see their operating costs decrease and profit soar. These two facts combine to free much-needed capital for global expansion ventures.
Learn More About Globalization Partners’ Global Expansion Platform™
When you’re ready to use AI technology to automate your HR processes and cut through legal bureaucracy with ease, turn to a professional employer organization (PEO) like Globalization Partners. Our Global Expansion Platform™ can perform recruiting, payroll, and benefits tasks efficiently and accurately, so your best movers and thinkers can get back to the critical work of strategizing and developing creative new ideas to help you expand.
Contact us today to learn more. For more tips regarding international expansion, be sure to download our guide on the top 10 International Expansion Mistakes to Avoid