Could AI End the Need for Bilingual Public Servants in Washington?

Every Tuesday afternoon in an Ottawa government office building, a manager is seated across from a public employee conducting a performance review in French. The manager can communicate in French. Perhaps “approximate” would be a better description. Everything is filtered, including the employee’s career, the nuances of their accomplishments and areas of difficulty, and the things that are difficult to express in your second language and more difficult to hear in someone else’s. No one brings it up. There has always been a filter.

Daniel Quan-Watson, a former federal deputy minister who worked in Canada’s senior public service for almost fifteen years, wrote a column for the Ottawa Citizen in October 2025 that raised the question of whether AI could eliminate that filter or render it obsolete in a different and more concerning way. A public employee had written to the column with a pointed question: given the speed at which AI translation tools were developing, was it possible that the federal government could adopt the technology, eliminating language-based career barriers and saving billions on training expenditures? Quan-Watson’s response was cautious and, in some respects, more difficult to reject than a simple rejection.

His main contention was that the question was being posed incorrectly rather than that AI translation is technically impossible (he acknowledged that it is constantly getting better). He claimed that framing bilingualism as a financial issue yields a single set of solutions. When it is framed as a question of what values a public service must uphold in order to serve its citizens directly, the results are completely different. He presented a more pointed version of the same question: would any of the people suggesting AI translation for their peers consent to having their own promotions, disciplinary actions, or performance reviews carried out by a machine that couldn’t comprehend a word they said? No one had replied in the affirmative.

A slightly different version of the same argument is being made in Washington State, which is located across the border. Early in 2026, the Washington Department of Licensing introduced an AI-driven self-service option that covered ten languages. The response was mixed and, looking back, predictable: the system performed well for routine transactions but faltered in ways that were embarrassing to the public. Since then, a state task force has suggested bolstering safeguards for public workers whose jobs are at risk due to AI displacement. There is no clear solution to the conflict between using AI to serve a multilingual public and safeguarding the employees who currently provide that service.

The nation’s most senior public servant prior to his retirement, Michael Wernick, the former Clerk of the Privy Council of Canada, has been more forthright than most about what he anticipates. He has openly stated that the idea of teaching people to be bilingual or multilingual may soon become obsolete and that programs like DeepL and Google Translate are getting close to a level of translation that makes it difficult to justify spending millions on language instruction. Currently, bilingualism is mandatory for about one-third of Canadian federal employees. He believes that requirement might not withstand contact with AI that is capable enough.

Whether that forecast comes true in a decade or two is still up in the air. Observing the discussion in Ottawa and Washington, as well as in the r/CanadaPublicServants threads, where public employees are debating this issue more openly than their supervisors usually permit, makes it evident that the question is no longer speculative. In the words of one commenter, bilingual staffing is a solution to a political issue rather than a technical one. The ability to hear a citizen directly, in their own voice, without a machine in between, carries a kind of weight that efficiency metrics were never intended to measure. This distinction is doing a lot of work in the current conversation and maps onto something Quan-Watson identified without quite naming it.

Reading the arguments on both sides gives the impression that the organizations engaged in this discussion are unsure of what bilingualism truly entails: a cultural commitment, a skill requirement, or a statement of values about the government’s perceived accountability. The clarification is being forced by AI. For the next generation of workers and the citizens they will serve, the answer will have a significant impact on what public service entails.