Multilingual Conversational AI Opening Up Global Markets
One of the most significant barriers to going global is language. Most of our approaches serving customers in other countries that speak different languages are clunky at best – translating a couple of key web pages, launching a micro-site, or just hoping Google translate does the job for our non-English speaking visitors.
Enter conversational AI.
Using AI to learn new languages quickly, businesses have the opportunity to skip the translation approach – one that is fraught with scalability issues.
Without digging too deep into the ins-and-outs of conversational AI (you can learn more in my article – Conversational AI is Eating the Web), let’s explore how AI can deliver a better, faster multilingual customer service experience.
Unlike more traditional multilingual marketing, call center, or IVR (interactive voice response) experiences, AI is not constrained by the need to translate some English-first version of our desired customer experience. As we know, language is full of cultural nuance. These subtle nuances can make even the best translations cringeworthy.
Learning instead of translating foreign languages
In contrast, conversational AI attacks the problem via a much different vector. Instead of translating, Conversational AI uses machine learning and deep learning techniques to learn the language from native crowdsourced conversations.
You can imagine that this process is much like how we might learn a new language if we were living in a foreign country or an immersive program. As you learn with this approach, you are very likely to use this new language in a more naturally and natively.
This approach opens up an incredible revolution in chatbot technology, allowing a single chatbot to learn and support conversations in multiple languages seamlessly.
Global market expansion just got easier
Chatbots are already being used by and often preferred by customers.
Forrester reports that 57% of marketing firms are using chatbots or will soon. In another survey, 69% of consumers said they prefer chatbots for quick interactions with companies. And technologies like Amazon’ Alexa, Google Home and Assistant, Apple’s Siri, and Facebook Messenger are surrounding consumers with AI assistants.
Launching your brand into the AI assistant ecosystem is a no-brainer. But, the strategic move is to also leverage the multilingual capabilities of an enterprise conversation AI platform to launch into global markets simultaneously.
How Does Deep Learning Enhance Natural Language Processing?
Deep learning is the ability to use neural networks to layer and adapt learning across multiple representations of data. This technology is particularly useful when with are trying to produce natural language understanding, processing, and responses from open-source and crowd-sourced conversational data.
Deep learning can help conversational AI platforms to more effectively build text and speech categorization, extract information, do sentiment analysis, and generate relevant responses. In all of these use cases and others that contribute to the natural language processing, the underlying language is irrelevant to the process. Therefore, given a variety of language models, the bot can learn and serve a variety of languages simultaneously.
Exciting Possibilities for Multilingual AI Assistants
The proliferation of AI assistants, the related explosion of conversational data sets, and advances in deep learning are bringing us very close to the promise of a Babelfish.
- Clinc Launches Second Largest Mobile Banking Voice Assistant in the World, Finovate
- 8 Keys to a Successful Conversational AI Deployment, Clinc
- How to build a multilingual chatbot for billions of users, VentureBeat