The Linguist

Chartered Institute of Linguists AUTUMN 2026 The Linguist 27 FEATURES not changed, only the time and compensation allocated to achieve that quality. This contradiction reveals the productivity myth. AI changes how translators work, but it does not reduce the expertise, cognitive effort or critical judgement required to produce a reliable translation. Even so, many productivity models expect linguists to validate thousands of words of AI-generated content per hour while maintaining professional quality standards at low cost. AI may accelerate drafting; it does not proportionally accelerate professional review, nor does it diminish the value of human expertise. Replacing editing with QA No professional publisher would eliminate editing simply because the first draft was easier to produce. Still, as AI becomes more widely adopted, many LSPs have shortened, merged or eliminated the editing stage. The result, as illustrated above, is a condensed sequence where editing and proofreading disappear as distinct steps. This shift assumes that MTPE can absorb the function of editing. Yet MTPE and editing are fundamentally different tasks. In compressed workflows, MTPE aims to make machine-generated text understandable; with pre-allocated time and lower compensation, the translation cannot be fully accurate, natural or appropriate for its intended audience. In fact, editing is what transforms an acceptable translation into an effective one. Therefore, treating MTPE as a substitute for editing – and QA as a substitute for both – removes one of the profession's most important safeguards. A translation may pass QA because its terminology, formatting and consistency meet specifications, while problems with clarity, tone, style or register remain unresolved. Each stage of the workflow answers a different question. MTPE asks, 'Is the translation acceptable?' Editing asks, 'Does it communicate effectively?' Proofreading asks, 'Does the translation read naturally?' QA asks, 'Does it meet the required specifications?' One stage cannot replace another. Reducing or eliminating editing may improve productivity metrics, but it also increases the risk that formal equivalence is mistaken for effective communication. Multiple reviewers create successive opportunities to identify and correct errors, yet many of these safeguards are being reduced. AI does not make those decisions. It does not remove editors, shorten deadlines, eliminate resources or reduce budgets. LSPs design those workflows. Lesser-resourced languages Although such failures affect every language pair, lesser-resourced languages expose them sooner because they have fewer opportunities to recover, and the resulting errors are more likely to persist. Even with carefully designed prompts, AI is less likely to identify and correct its own errors because fewer linguistic resources exist to detect and resolve them.As a result, translators rely more heavily on professional judgement and community- developed resources. With regards to Haitian Creole, when translators, reviewers or clients favour different English- or French-influenced conventions, it can become especially difficult to reach a consensus because there is often no widely accepted authority to resolve competing linguistic choices. In that vacuum, allowing AI to become that authority is no substitute for linguistic consensus. Consequently, AI is often blamed for errors rooted in limited linguistic resources, unrealistic productivity expectations and weakened human oversight. Setting AI up to succeed AI is already improving efficiency, expanding multilingual access and increasing the digital visibility of lesser-resourced languages. These advances should be welcomed. However, better AI does not guarantee better translations. That requires equally sound linguistic infrastructure and professional workflows that support the technology. LSPs must invest not only in improved models, but also in linguistic resources and rigorous review processes that clearly distinguish MTPE, editing and quality assurance. AI makes mistakes. Translation workflows are designed to catch them. When they fail, the issue is no longer solely technological. It is professional, operational and systemic. And when quality declines, we should ask not only 'What did the AI get wrong?', but also 'What conditions were created for both AI and linguists to succeed?' AI does not remove editors, shorten deadlines, eliminate resources or reduce budgets © SHUTTERSTOCK

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