How AI can help overcome international business challenges

According to Harvard Business Review, two of the top challenges of international business are language barriers and cultural differences. This highlights the fact that effective multilingual communication is at the heart of a successful international business strategy. However, communicating across cultures can be tricky, and it requires well-formed plans and precise tactics to succeed.  

Luckily, fully manual and time-consuming solutions to these language and cultural challenges are becoming a thing of the past. Many applications of artificial intelligence (AI), such as Machine Translation (MT), can help provide resolutions to some of the challenges of new market entry. We will look at a few after giving a quick rundown of what these AI technologies look like.  

A quick summary of AI technologies

AI is a family of technologies that enable software to do something that normally requires human intelligence, such as recognizing objects, understanding language, or solving complex problems. Machine learning (ML) is a subset of AI in which algorithms are trained on datasets so the software can learn to do specific tasks on its own by analyzing training data and improving its own performance over time.  

Machine learning and the advent of deep learning

Deep learning is a subset of machine learning that uses neural networks to produce results that can be equivalent, in some cases, to human output. The design of artificial neural networks is inspired by the node-based structure of neurons in animals. Like biological neurons, artificial neurons are connected via “synapses,” allowing for flexibility and complex information processing. “Deep” refers to the multi-layered structure of these neural networks.

Natural language processing: training computers to learn and process human languages

Natural language processing (NLP) is the ability of a computer to process, interpret, and understand spoken or written languages. Neural networks have vastly improved NLP, making it more adept at processing and generating natural language input. Current algorithms can produce models that are very robust even with unfamiliar input such as grammatical structures it has not encountered before. 

AI tools in global business: creating efficiencies in localization

AI, and especially NLP, has had a major impact on localization workflows and processes, which include things like preparing text for translation, pre-translating with a translation memory, performing quality checks, and moving translations through reviews. Because there is such strong promise, public and private organizations worldwide are investing large sums in machine learning and machine translation research and development. 

For example, the Technology Innovation Institute of Abu Dhabi, a global scientific research center, released the world’s largest Arabic language processing model, called Noor. With all this activity and investment, the industry will see more and more refinement in AI tools and increased advantages in using them to create efficiencies in translation and localization processes.

Let’s explore four key ways in which localization AI tools help drive international business. 

Major improvements in machine translation

The advent of sophisticated neural networks for machine learning has led to massive improvements in the accuracy and quality of MT, while also achieving higher speeds and lower costs than ever before. 

MT has become more capable of handling the immense amount of nuance involved in translation, such as in figures of speech, humor, sarcasm, and irony. This is possible because MT engines have been trained on vast amounts of high-quality language data from native speakers. 

In fact, over one-third of marketers are now embracing MT, and many state that it is delivering the quality that they need. Companies are getting very compelling results: eBay-based exports to Latin America increased by 17.5% as a result of eBay’s machine translation service. 

The rise of multilingual conversational AI 

Chatbots and voice assistants can now deliver interactions across multiple languages in a way that is more natural and conversational than ever before. Companies are building these into important customer touchpoints such as inquiries, help requests, and other customer support functions. 

For example, the use of a multilingual chatbot can supplement the work of customer support teams by providing real-time machine translation to help customers in their native language at the moment of need. It can do this 24/7/365, unlike humans. 

Huge efficiency increases with AI-driven localization processes

AI technologies can be used to eliminate many repetitive, manual tasks, simplifying workflows for localization professionals and freeing up more time for value-add activities. These workflow improvements can provide substantial cost and time savings for global enterprises. 

Faster localization helps resolve those tricky communication and cultural barriers more quickly. For example, AI can be applied to localization workflows such as translation and quality assurance. Here’s a specific use case: AI-driven approaches allow localization to be approached via content segments so that multiple resources can work on the same content at the same time.  

AI for localization processes is more adaptable and sophisticated than ever before and continues to evolve and improve to increase productivity and efficiency. 

Better localization strategy with AI-generated local market insights 

Insights on your local market, based on deep research, are key to any localization strategy, as we well know from the highly public brand failures. Remember the story about how “Got Milk” was translated to “Are you lactating”? But again, AI can help here: advances in AI are changing the way brands find and act on consumer insights. 

AI can sift through, synthesize, and analyze the vast amounts of data out there. This provides a faster and more budget-friendly way to learn your audience’s preferences and needs. For example, AI, when guided by a human who cross-checks for bias, can gather insights that will support and drive strategy for creating culturally relevant communication and effective hyper-local experiences. 

AI can also help you assess online sentiment and chatter, identify your ideal target customer, and figure out the best times and ways to engage with them. 

What does AI look like in international business today? 

Now that you understand some of the ways AI is used in global business, it’s interesting to see how it looks in practice. Here are some examples of how global businesses today are taking advantage of AI to solve language-related business problems: 

• Facebook runs 20 billion translations every day in its news feed. Its “see translation” button, which translates a friend’s post, uses MT. 
• With NLP, Alibaba automatically generates product descriptions for the site. The platform currently supports 16 languages.  
• Google Duplex, an AI System for accomplishing tasks over the phone, uses NLP to enable an AI voice interface to make phone calls and schedule appointments on your behalf. It debuted in Spain in 2020. 
• Alexa is available in 8 languages and a few different dialects, and largely depends on AI intelligence, automation, and ML to provide the responses we are used to getting. 

Great progress, isn’t it? But these incredible advances and applications of technology are not without their challenges. 

Challenges ahead in AI development

Despite all the exciting achievements, there is still a way to go, and there are some barriers to getting there.   

Access to data for machine learning purposes

Very large datasets are needed to train AI. If you are creating a customer service chatbot, the data could include all the ways in all languages in both text and audio to ask, “what is my account balance?” 

The quality of the datasets is also critical: garbage in is garbage out. Also, training data isn’t labeled or collected on its own, and this can be complicated and time-consuming. Usually, the process requires using a platform that collects the data and contributors to label or annotate it. A final challenge with datasets is that there can be cross-border restrictions on data flow that may have the most impact on smaller, developing countries. 

AI and data privacy 

Consumers are concerned about privacy, and restrictions on data flow largely stem from this. The challenges that arise related to AI and data privacy include ethical considerations, legal frameworks, and best practices for protecting personal data. 

For example, GDPR greatly restricts transfers of personal data to countries with less stringent protections. Under GDPR, data can only be used for the original purpose for which it was collected, precluding its use for training machine learning models. Applications of AI are only responsible when they safeguard individual privacy rights.

AI and IP protection

AI development raises intellectual property issues internationally. Data used in training machine learning models needs to be copied and edited, which can potentially involve unauthorized copying of protected works. In the U.S., fair use exemptions may cover some of these cases. However, these do not exist in all countries or regions, such as the EU and Australia. 

Key takeaways on using AI to drive international business expansion

AI can automate processes, enhance customer service, enable personalization, translate, increase productivity, and analyze data, all of which help businesses grow globally. Research shows that 35% of companies are using AI today and 42% more are exploring it for future implementation. 91.5% of leading businesses invest in AI on an ongoing basis. 

Specifically for businesses who need to localize, MT, AI-assisted workflows, conversational AI, and market-insight research are the key areas where AI can help drive international growth.  

Even when faced with the IP, data privacy, and dataset challenges we talk about above, these technologies are here to stay, and will only continue to improve with ongoing research and funding worldwide. In order to stay competitive, expand globally, boost revenue, understand your audiences, and provide personalized content (including written or spoken) for all audiences, your business will likely need to look to AI.  

Moving forward with AI to resolve international business challenges

Now that you’ve learned more about the exciting developments (and challenges) related to AI applications in localization you may be convinced that you should integrate the technologies into your business. For example, you might want to start looking at using Machine Translation or figuring out a smoother localization process. 

At Acclaro, we simplify what may seem like complex processes to help you scale global growth and create lasting value for your customers. We’ve created strategies that help the world’s leading companies succeed across cultures, and we’re ready to help you create yours.

Want to learn more about how Acclaro can help you achieve your business goals? Get started today.