Our Analysts' Insights

After we published our recent Q3 2024 update on market sizing for the language sector, which was also covered in a public webinar, this blog addresses some of the questions we have received from clients, prospects, and investors. This quarterly update for Q3 2024 is noteworthy because it includes our final annual market sizing numbers for 2023, which are based on a representative sample of LSPs and produced only after careful examination of vetted revenue data following the close of the year. Th...

In today’s interconnected world, a global enterprise’s success hinges on its ability to produce, refine, and deliver content across multiple languages and cultures. Imagine your content creation process as a sophisticated manufacturing production line, where various components from different departments – legal, logistics, marketing, product development, support, and training – come together to create a polished, market-ready product. In this Post-Localization Era, there's a new tool in ...

Partnering with localization teams to achieve internationalization compliance on time every time means working closely together – especially as your processes and theirs integrate (Gen)AI. Open an ongoing dialog with localization, testing, and design colleagues. They are sure to have other suggestions for how to close the gap between you and the multilingual products that grow out of original coding. And, if you let them, the localization team may even offer to generate your developer notes aut...

It Depends As your organization pivots toward integrating generative AI (GenAI) into more of its business processes, you may be wondering if it’s time to engage GenAI talent to join your staff. The answer depends on several factors.

In October 2023, we argued that the future of AI would be in “focused large language models” (FLLMs). These are purpose-built language models that target a specific industry, set of languages, or task and that are correspondingly smaller than the large language models (LLMs) being created by OpenAI, Google, Meta, and others. Those massive models – GPT-4o has over 175 billion parameters – are like Swiss Army knives: They are prepared to handle almost any task, from creating a haiku to drawing...

The topic of automation has taken the interpreting industry by storm. On the one hand, enthusiasts believe in artificial intelligence as a way to broaden language access at an affordable cost. On the other hand, skeptics worry about all the things that could go wrong in the implementation. But where does it all settle when it comes to organization-level implementations?

Back in the day when I first began working in localization, we didn’t have a translation management system (TMS) – they didn’t exist – and our LSP was refusing to use translation memory because it created “too much overhead” for the first venture into producing a customer care website in more than one language. Knowing that the advent of the internet was likely to produce masses of new content in – hopefully – all the languages in which the company operated, we took a huge risk. We found...

When friends and family hear what I’m working on these days, they typically ask: 1) won’t AI eliminate the need for translators and interpreters? and 2) won’t that eradication of the language sector do away with your job, too? The first question has taken the air out of the room in a lot of discussions over the last couple of years, especially given media coverage of humans in the loop that amounts to job descriptions that are little more than janitors cleaning up after bad MT outcomes.

Some people feel that using artificial intelligence (AI) to interpret human speech is a curse because individuals using the service could suffer harm due to mistranslations or because interpreters might lose their livelihood. Others embrace AI for all the possibilities it creates, first and foremost the ability to offer language access on a much greater scale and at a reduced cost. So which camp is right? Like so many techno-ethical questions, there is no right or wrong. It really depends on th...

Whether or not product managers have direct responsibility for the international success of their products, they still want them to do well worldwide and are often held responsible for international revenue numbers. But what if they have little or no international experience, don’t understand how to build the multilingual training data required by their product, or simply lack the resources to research international markets, analyze product functionality, and beta test properly?