The pressure is real
If you work in - or with - localisation, you’ve likely heard two conflicting messages:
“Use more AI in your work. We need to cut costs!”
“Why is the quality so poor? Why is this taking so long?”
Caught between cost-cutting demands and local stakeholders frustrated by quality issues, localisation teams often feel stuck between a rock and a hard place. The truth is that both perspectives are valid; and that’s exactly why finding the right balance between humans and AI has become one of the most important questions in modern localisation.
The speed–cost–quality triangle is changing
For years, localisation professionals have worked within the constraints of the good–cheap–fast triangle. Traditionally, improving one dimension meant compromising another.
But AI has disrupted the equation. Machine translation and AI-driven workflows have:
Made cheap even cheaper by reducing reliance on manual effort.
Made fast even faster with near-instant draft translations.
Raised the baseline quality of the initial machine translation output compared to earlier technology.
Yet, despite these improvements, AI alone cannot guarantee the right tone of voice, compliance, or cultural nuance. That’s where humans remain essential.
Where humans still make the difference
Every human touchpoint in a workflow adds cost and time, but also unique value:
Translators ensure terminology is clear and consistent.
Proofreaders make sure tone of voice reflects the brand.
In-country reviewers, also known as validators, check for cultural fit and accuracy in context.
These roles matter most where errors carry significant risk: compliance, product safety, or brand trust. In these cases, “good enough” is not good enough.
The rise of AI agents
The future is not humans versus AI, but humans with AI. New approaches use multiple AI “agents” that mimic human roles in the workflow:
A translation agent generates content informed by terminology and past translations.
A quality agent evaluates the output, assigning quality scores.
An editing agent reworks weak translations, prompting improvements automatically.
This loop of generating, assessing, and refining elevates AI output quality without significant impact on speed or cost. It’s a sign of how workflows are evolving: smarter, more scalable, and increasingly hybrid.
Fit-for-purpose quality: deciding what matters
Not every piece of content needs the same level of review. The smartest localisation leaders tailor their workflows to fit the purpose of each content type: whether the content is compliance-driven, customer-facing, internal, etc. For example:
High-stakes content: legal contracts, compliance documents, safety instructions, or customer-facing brand messaging. These demand full human-in-the-loop review.
Lower-stakes content: internal documentation, FAQs, or informal updates. For these, post-edited machine translation or lightly reviewed AI output may be entirely sufficient.
By making conscious decisions about what requires “gold standard” quality and what does not, localisation teams gain the power to push back against unrealistic expectations.
Practical steps for localisation leaders
Audit your content: Classify it into critical and casual categories.
Design workflows: Align human effort with content importance.
Show ROI: Explain to stakeholders why not all content gets the same level of review.
Partner wisely: Work with providers who can help you navigate evolving AI capabilities while safeguarding quality.
A new balance of AI and humans
AI is not here to replace localisation professionals, but it is reshaping their roles. Instead of spending time on repetitive tasks, AI now takes care of the heavy lifting, from first drafts to basic quality checks. Humans step in where they create the most value: refining nuance, ensuring compliance, and safeguarding brand voice.
At the same time, the explosion of AI-generated content means there is more to review and localise than ever before. Far from reducing the need for localisation, AI has multiplied the volume - making the right mix of automation and human expertise more important than ever.
The real opportunity lies in finding the right balance. By consciously defining what “good enough” means for each content type, localisation teams can meet cost pressures without compromising on quality where it matters most.
At LanguageWire, we help enterprises strike that balance with scalable, hybrid workflows powered by AI and guided by human expertise.
Explore our translation solutions to see how we support end-to-end localisation.
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