When machine translation isn’t enough: How global teams avoid costly miscommunication
As remote and hybrid work becomes the default for many organisations, more teams are collaborating across borders, time zones and languages. Video calls have made it easier to meet face to face, but they have also made one problem more visible: miscommunication scales fast when language barriers exist.
Most global teams have tried built-in translation tools at some point. They are convenient, and in simple conversations they can be genuinely helpful. But there is a gap between translating words and translating meaning, especially when the conversation includes technical details, legal implications or business critical decisions.
Understanding where automated translation performs well, and where it falls short, is one of the most practical skills a globally distributed team can develop.
The hidden costs of “close enough” translation
In day-to-day conversations, minor translation errors can be brushed aside. In professional settings, small inaccuracies can lead to rework, delays and strained relationships. The cost rarely appears as a single dramatic failure. Instead, it shows up in subtle ways:
Project timelines stretch because requirements were misunderstood.
Teams repeat meetings to clarify what was already discussed.
Stakeholders lose confidence when details keep changing.
People disengage because it feels harder to contribute.
These issues are particularly common when teams assume translation is a simple add-on, rather than a core part of how global collaboration works.
Where built-in meeting translation helps, and where it struggles
Tools like Google Meet have improved accessibility for global teams. Automated captions and translation features can support understanding in the moment, especially for straightforward topics and familiar vocabulary.
However, the same tools can struggle in scenarios that are common in business settings:
Industry-specific terminology, such as engineering components, medical terms or legal language.
Complex sentence structures, where the meaning depends on context rather than individual words.
Accents, audio issues or multiple speakers talking over each other.
Cultural nuance, where literal translation misses intent or tone.
It is not that automated translation is useless. It is that it has limits, and teams need a plan for what happens when those limits are reached.
Why technical language is different
Technical communication is unforgiving. In many industries, a small wording change can alter meaning dramatically. A product specification might depend on a measurement unit. A safety instruction might require exact phrasing. A contract clause might hinge on one ambiguous term.
When translation involves engineering, manufacturing, software, pharmaceuticals, or regulated sectors, accuracy is not just a preference. It is risk management.
In these cases, teams often move from general translation tools to specialised workflows, including glossaries, review processes and professional input, particularly when technical translation services are required to maintain consistency across documents, systems and stakeholder communication.
Building a translation workflow that scales
The strongest global teams do not treat translation as a one-off task. They build a workflow that fits their reality.
That workflow usually includes a few core elements.
First, teams define which types of communication can rely on automated translation, and which require a higher level of accuracy. A casual internal check-in is not the same as a client negotiation, a compliance review, or a technical handover.
Second, they standardise terminology. A shared glossary might sound basic, but it is one of the fastest ways to reduce confusion across teams. This is especially important when multiple departments use different words for the same thing.
Third, they create a review step for high stakes content. That review can be internal if the team has language expertise, or external when the subject matter is specialised and accuracy is critical.
Finally, they store translated materials in a way that is easy to reuse. Re-translating the same concepts repeatedly is one of the most common and avoidable sources of waste.
How AI assistants can support meetings without replacing expertise
AI meeting assistants have become popular because they reduce admin work. Transcription, summarisation and action item extraction can help teams capture what happened and what to do next.
These tools can also support multilingual meetings by creating cleaner written records, which makes it easier to verify meaning after the call. A written transcript allows participants to review terms, clarify misunderstandings, and align on next steps in a less pressured environment.
Where teams need to be careful is assuming that a transcript equals accuracy. If the underlying translation is flawed, an AI summary can simply compress the error and spread it faster.
AI can support the workflow, but it does not replace the need for human review when precision matters.
Best practices for teams working across languages
Global collaboration improves quickly when teams adopt a few simple habits. Speak in shorter sentences, especially when discussing complex topics. Avoid idioms and culturally specific phrasing that may not translate well. Confirm critical decisions in writing, including owners and deadlines. Use consistent terminology across meetings and documents. Flag discussions that involve legal, medical, financial or technical risk, and treat them differently from casual updates.
These practices do not slow teams down. They reduce the time spent cleaning up misunderstandings later.
Operate responsibly across borders
Translation tools have made global work easier, but they have also created a new temptation: treating “good enough” as safe.
The reality is that many global teams operate in environments where small misunderstandings create large costs. Knowing when to use automated translation, and when to use more structured workflows, is part of operating responsibly across borders.
The teams that scale best are not the ones with the most tools. They are the ones that build clarity into the way they communicate, especially when language and technical complexity intersect.
