AI LQA Reality Check

AI LQA sounds like the shortcut every localization team wants, but the real story is more nuanced and a lot more useful. We sit down with Erik Vogt to define AI-driven linguistic quality assurance and quality estimation in plain terms, then follow the practical question that matters: how do you use AI to review more content without blowing up cost, time, or trust? If you manage translation quality in a TMS or CAT tool environment, this conversation gives you a grounded map of what works today and what still breaks.We dig into the most common AI LQA use cases: scoring segments so you can skip “likely good” content, isolating the worst segments so reviewers spend time where risk is highest, and using QE as an early go or no-go signal. Eric explains why the human baseline is messy too, including the reality of reviewer disagreement under MQM style frameworks, and why AI’s consistency can still speed up human review even when it cannot match human judgement end to end. We also talk about the impressive results teams sometimes see when the AI has the right glossary and guidance and why false positives can quietly erase those gains.From there, we get tactical: why turnkey solutions often disappoint, how to break QA into narrow sequential checks, and how prompt engineering and tuning can improve reliability across languages. We close with what to expect next, including faster throughput, more transparency around AI compute costs, and better comparative data on foundation models for localization quality workflows. If you’re evaluating AI LQA tools, subscribe, share this with your localization team, and leave a review with the biggest question you still have about automated translation QA.