Our Analysts' Insights

Upselling and cross-selling aren’t just buzzwords—they’re critical strategies that allow LSPs to deliver more value to their clients while driving growth. For project managers and account managers, knowing when and how to offer additional services can transform client relationships from transactional to strategic partnerships.

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...

For language service providers, finding the right way to stand out in a crowded marketplace is a lot like navigating the breakfast cereal aisle at your local grocery store. This aisle is a tapestry of colors, characters, flavors, and promises, each box vying for your attention and attempting to persuade you of its unique benefits. Whether it’s the health-conscious options that emphasize whole grains and fiber, the kid-friendly boxes with cartoon mascot illustrations, or the luxury granolas that...

CSA Research has been running a survey about the procurement of language access, services, and technology. We are looking for input from supply chain managers across various vertical markets and global organizations. They can tell us something significant about language enablement processes at major consumers of these products and services. 

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.

The rising frequency of discussions about AI has led to much unease among interpreting service providers about the impact of automated solutions on language access and on what that means to the future of their profession. In this blog, we explore the concept of meaningful language access, why automated interpreting struggles with that concept, and how it relates to deciding and defining the role of the interpreter.

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...