Blog Articles

Microsoft has spent 2026 adding real interpretation muscle to Teams: automatic language detection, a consecutive mode for negotiations, support for dedicated room hardware. For an organisation working mainly in English and Spanish on a Copilot licence, that might cover most meetings on its own. For most global organisations it does not, and the gap between what Teams now offers natively and what a multilingual workforce actually needs is exactly where an interpretation platform earns its place. 

Walk into a large conference hall or auditorium today and you will often see a screen at the front of the room carrying more than slides. Underneath the speaker's name, or along the bottom third of the display, a live transcript of what is being said scrolls in real time. This is on-screen captioning, and it has moved from a niche accessibility feature to a standard expectation at well-run onsite events. 

Every AI speech translation session carries a conversation that someone considered worth having in real time, whether it's a board discussion, a diplomatic briefing, or a clinical consultation. That conversation gets captured as audio, processed by machine translation models, and often stored or logged somewhere along the way. If the underlying infrastructure isn't secured properly, the convenience of instant multilingual communication comes with a data protection risk that's easy to overlook until it's tested.

Most conversations about AI language performance still centre on English. A model scores well on a standard benchmark, gets praised as state-of-the-art, and is rolled out for global use. But a growing body of evidence, most recently the European Commission's new EU MMLU dataset, shows that strong English performance says very little about how a model handles French, Hungarian or Maltese. For anyone relying on AI live translation at international events, that gap is not an academic footnote. It is the difference between a delegate understanding a keynote and missing it entirely. 

Picture a training video recorded once in English that a global workforce needs in ten languages by Friday. A decade ago, that request meant hiring voice actors, booking studio time, and waiting weeks for each language. Today, it can mean uploading a file and downloading a dubbed version in hours. This shift is powered by AI dubbing.

Picture a product launch with attendees joining from twelve countries, each expecting to follow along in their own language without a hitch. A few years ago, delivering that experience meant booking interpreters, installing booths, and hoping the technology held up on the day. Now it often means choosing the right app.

A meeting can look successful in the moment and still fail the people in it. Everyone nods, the agenda gets through, and the call ends on time. Then the follow-up emails arrive, and it becomes clear that half the room understood something different from what was said. In multilingual settings, this gap is rarely caused by a single dramatic failure. It tends to come from a handful of small, hidden weaknesses in the speech translation platform and the way it is set up.

Quoting audio translation can feel like comparing apples to oranges. One provider prices by the minute, another by the language pair, a third bundles everything into a flat event fee. For an events manager trying to budget a multilingual conference or hybrid town hall, the lack of a standard pricing model makes it hard to know whether a quote is reasonable before you've gathered several.

Think about the last large event you attended or organised. How many of the people in that room — or joining virtually from around the world — were expected to simply follow along in a language that wasn't their own?