Why Enterprise Companies Need an AI-Based Translation Service

juni 22, 2021

Enterprise Companies need AI-based machine translation: customer service agents with headsets, connected across the globe

Today, enterprise companies that serve a global customer base have a range of options for how they approach language translation. They might use native-speaking teams, outsourced translators, business process outsourcing (BPO) partners, translation software, or a combination of multiple solutions.

However, not nearly enough enterprise companies are taking advantage of the major leaps that have been made in machine translation. Driven by artificial intelligence, machine translation services can make language translation far more efficient and cost-effective than any other solutions available today.

Here are five reasons why every enterprise organization should be using an AI-based translation service to achieve maximum growth and profitability.

Cost-effectively penetrate new markets

As companies look to bring their products or services into new markets, it can be difficult to justify the cost of hiring full-time native-speaking teams when consistent demand isn’t there yet. In these situations, AI-based machine translation can be an effective way to test out a market and decide whether or not you want to pursue full-on expansion in that region.

Breaking into and maintaining new markets through traditional translation services is an expensive and cumbersome endeavor. It requires the replication of translation processes and teams on a per-language basis. With a translation-as-a-service AI software solution such as Unbabel, organizations can leverage cutting-edge neural technology combined with enterprise-specific glossaries and human expertise to roll out new languages en masse.

For example, at Unbabel, we’ve helped enterprise companies achieve results such as

  • decreasing the time it takes to break into new markets (TuneCore)

  • adding new languages at a fraction of the cost of a native speaking team (GetYourGuide)

  • being able to immediately offer 24/7 ticketing support in new territories (Oorlogsvoering)

  • quickly rolling out support across several different languages (25 languages for Zenly)

Nail the long tail

A machine translation service is also useful for addressing the linguistic long tail, i.e., the languages that are less frequently translated or localized. Recruiting translators for long-tail languages can be difficult and expensive, and translation software that relies solely on AI is often less accurate for these languages.

Human-in-the-loop AI, on the other hand, is a reliable way to produce fast, high-quality, on-brand translations for the long tail. According to Gartner, “[b]y 2025, 75% of the work of translators will shift from being focused on creating translations to reviewing and editing targeted sections that were machine translated.”

This approach really does provide the best of both worlds. When mobile gaming giant King wanted to address long-tail languages such as Vietnamese and Polish, they tried pure machine translation — but the quality just wasn’t there. With Unbabel, they’re now able to offer full, high-quality support for these languages at scale without having to increase staffing.

Provide a stellar CX across the globe

When it comes to cultivating lifelong fans of your brand, consistency in customer experience (CX) is everything. The goal is for every customer to receive the same A+ level of service no matter where they’re located. Even small slip-ups can have serious consequences: According to research from PwC, one in three global consumers say they will walk away from a brand they love after just one bad experience. In regions like Latin America, the ratio jumps to one in two.

Our What’s Top of Mind in 2021 survey discovered that 45% of global customer service leaders believe providing a consistent experience for international customers is their #1 challenge this year. When asked for additional information about this hurdle, customer service leaders said they

  • would like to offer 24/7 support across time zones, but find it expensive to operate in multiple locations.

  • are trying to expand support globally, but the logistics of hiring locally make it challenging and expensive.

  • have unpredictable volumes of requests across languages, making it difficult to develop agile customer service operations.

  • want to manage multilingual support but lack an end-to-end solution.

An AI-powered translation platform can help address those issues by solving the challenge of sourcing local language experts, lowering the cost of expansion into new markets, and helping companies support both high- and low-volume languages. That’s why 79% of respondents to the above-mentioned survey said that they find AI-powered solutions to be “very or extremely valuable.”

Communicate across multinational teams

Today, many enterprise companies still rely on a siloed approach to cross-border communication and localization. For example, the product team might use traditional online language translation services, while the sales team hires native speakers, and customer service uses a BPO center.

At Unbabel, we’re pioneering a way to use an AI-based translation platform to connect teams all across the globe: Language Operations (LangOps). A LangOps strategy focuses on how to conduct business efficiently in every market, taking into account the cross-departmental needs of the entire organization. LangOps does this by centralizing all language translation operations for the enterprise via an easy-to-manage portal that abstracts away the technical complexity so employees can focus on leveraging their unique skills and making better business decisions.

We think that more enterprises will start hiring LangOps teams soon, and that’s a smart approach. LangOps teams can be responsible for providing both technical and linguistic support for their international workforce, partners, and customers. This ensures high quality and a consistent experience for customers and employees alike.

Rev up revenue potential

Over the past few years, many enterprise organizations have found that LangOps driven by an AI-based translation management system has real revenue-building potential. In addition to the operational cost savings that can be realized through AI-based translation software, providing multilingual support is a proven method to build customer trust and loyalty around the world.

With an enhanced brand reputation, enterprise companies can expand global reach, tap into new markets, and develop fresh revenue streams. As the discipline of LangOps continues to evolve, we’re seeing new opportunities for companies to increase language efficiency in customer service, marketing, sales, product localization, and beyond. By the end of this year, I think we’ll have discovered many more ways to leverage LangOps to support enterprise revenue growth.

From breaking into new markets to providing a unified customer experience and operational structure, there’s no shortage of reasons for enterprise companies to use an AI-based translation platform. It doesn’t require a big up-front investment and can be strategically tested before implementing at scale.

The results that both mid-size companies and household name brands have achieved using Unbabel’s AI translation solution speak for themselves. Are you ready to get on board?

About the Author

Profile Photo of Alon Lavie
Alon Lavie

Alon Lavie is the VP of Language Technologies at Unbabel, where he leads and manages the US AI lab based in Pittsburgh, and provides strategic leadership for Unbabel's AI R&D teams company-wide.From June 2015 to March 2019, Alon was a senior manager at Amazon, where he led and managed the Amazon Machine Translation R&D group in Pittsburgh.In 2009, he co-founded a technology start-up company by the name of "Safaba Translation Solutions"​, and served the company as Chairman of the Board, President and CTO. Safaba developed automated translation solutions for large global enterprises that allowed them to migrate and maintain large volumes of content in all the languages of their markets. Safaba's approach focused on generating client-adapted high-quality translations using machine-learning-based technology. In late June 2015, Safaba was acquired by Amazon.For almost 20 years (1996-2015), Alon was a Research Professor at the Language Technologies Institute at Carnegie Mellon University. He now continues to serve as an adjunct Consulting Professor at CMU. His main research interests and activities focus on Machine Translation adaptation approaches with and without human feedback, applied to both high-resource language pairs as well as low-resource and minority languages. Additional interests include automated metrics for MT evaluation (specifically, the METEOR and COMET metrics), translation Quality Estimation, and methods for multi-engine MT system combination. Alon has authored or co-authored over 120 peer-reviewed papers and publications (Google Scholar h-index of 45 and i10-index of 122).Alon served as the President of the International Association for Machine Translation (IAMT) (2013-2015). Prior to that, he was president of the Association for Machine Translation in the Americas (AMTA) (2008-2012), and was General Chair of the AMTA 2010 and 2012 conferences. Alon is also a member of the Association for Computational Linguistics (ACL), where he was president of SIGParse - ACL's special interest group on parsing (2008-2013).In August 2021, at the 18th biennial Machine Translation Summit conference, Alon was awarded with the 2021 Makoto Nagao IAMT Award of Honour for his contributions to the field of Machine Translation.

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