How Do We Support Non-English-Speaking Staff With AI Training?

Many London financial services operations, settlements and customer service functions rely on staff for whom English is not a first language, including staff based overseas in shared service centres.

When AI literacy training is delivered only in standard English, at a pace and register designed for native speakers, comprehension gaps appear.

These gaps are not always visible in training completion records, since staff may complete a course without genuinely understanding it.

In a regulated environment, this creates real risk: staff who do not understand the limitations of an AI tool are more likely to over-trust its output or misuse it in ways that create compliance exposure.

This article sets out a practical approach to designing AI literacy training that works across a linguistically diverse workforce.

Why Language Gaps Matter in AI Training

AI literacy training is intended to give staff a working understanding of what an AI tool can and cannot do, where its outputs need scrutiny, and when to escalate rather than accept a result at face value.

If that message does not land because of a language barrier, the training has failed even though the completion record shows otherwise.

This matters more in financial services than in many other sectors. Operations, settlements and customer service staff are often the people closest to the point where an AI tool's output gets acted on: a reconciliation break gets accepted, a customer query gets answered, a flag gets cleared. Misunderstanding the tool's limitations at that point is not a training inconvenience. It is an operational and compliance exposure.the completion rate.

Standard training completion metrics do not distinguish between a staff member who understood the material and one who clicked through it without full comprehension. That distinction only becomes visible later, when something goes wrong.

Traditional Approaches to Inclusive Training

Organisations have long used a range of methods to make training accessible across a multilingual workforce.

Professional translation of manuals and course materials produces accurate, polished content, but it is slow and expensive, and it needs to be redone every time the source material changes.

Interpreter-led training sessions allow staff to ask questions in their own language in real time, which is valuable for nuanced topics, but interpreters need to be booked, briefed and paid, which limits how often this can be repeated.

Bilingual buddy systems, where a more experienced bilingual colleague supports a peer through training, are effective and low-cost, but they depend on the availability and goodwill of specific individuals and do not scale well across large teams.

Plain-language material design, simplifying English sentence structure and avoiding idiom, helps everyone, including native speakers, but it does not remove the language barrier for staff who are not confident in English at all.

Each of these approaches remains valuable. The limitation is not effectiveness but scale: they are difficult to deploy quickly and repeatedly across a large, distributed workforce.

Where AI Helps

AI translation and simplification tools change what is practical to produce, not what is acceptable to publish without review.

An AI tool can take an existing English-language training module and produce a first-pass translation into another language in minutes rather than days. It can also generate a simplified plain-language summary of a complex topic, which is useful even for materials that stay in English. Some AI tools can support real-time interpretation during live training sessions, reducing the need to book a human interpreter for every delivery.

This speeds up the part of the process that used to be the bottleneck: producing a usable first draft in multiple languages. It does not remove the need for a fluent, subject-matter-aware reviewer to check that draft before it reaches staff.

Technical and regulatory terms are where AI translation is most likely to go wrong. A term like "break" in a settlements context has a specific meaning that a general-purpose translation tool may render as an ordinary word in another language, changing its meaning entirely. This is exactly the kind of error a fluent human reviewer will catch and an unchecked AI output will not.

Operational Considerations for Getting This Right

A small number of governance steps make the difference between AI-assisted localisation that works and localisation that quietly introduces errors.

Maintain a glossary of agreed translations for technical and regulatory terms, and require that AI-generated translations use it. This prevents the same term being translated inconsistently across different materials or training rounds.

Require that any AI-translated or AI-simplified material is reviewed by someone fluent in the target language who also understands the subject matter, before it is used in training. This review does not need to be exhaustive proofreading; it needs to focus on technical accuracy and meaning.

Test comprehension directly rather than relying on course completion records. Scenario-based discussion, short applied exercises, or simply asking staff to explain a concept back in their own words will surface misunderstandings that a translated document alone would not catch.

Treat the glossary and reviewed materials as a reusable asset. Once a term has been correctly translated and checked, it should not need to be re-translated and re-checked for the next training rollout.

Example

A London-based clearing operation runs a settlements team split between London and an overseas service centre, where several staff are more confident in their first language than in English.

As the firm rolls out AI literacy training on using an AI-assisted reconciliation tool, the L&D lead uses an AI translation tool to produce a first-pass localised version of the training materials in two additional languages, then asks bilingual senior settlements staff to review the drafts for accuracy, particularly around technical terms like "break" and "fail".

The reviewed materials are delivered alongside scenario-based discussion sessions, where staff talk through examples in their preferred language to confirm understanding.

The overseas team completes training with materials that are both linguistically accurate and technically correct, and the discussion sessions surface two points of genuine confusion that would not have been caught by a translated document alone. The firm retains a reviewed glossary of terms for future training rollouts.

FAQs

• Can we just use an AI translation tool to translate our existing AI training course?

  AI translation is a useful starting point, not a finished product. General-purpose translation tools often mishandle technical and regulatory terms, changing their meaning. Treat the AI output as a first draft that a fluent, subject-matter-aware reviewer checks before it is used in training.

• How do we know if non-English-speaking staff have actually understood the training?

  Completion records are not reliable evidence of comprehension. Direct methods, such as scenario discussions, Q&A sessions or short applied exercises conducted in the staff member's preferred language, give a much more accurate picture of whether the training has landed.

• Should we deliver AI training in staff members' first language or in English?

  There is no single right answer. It depends on the operational context, the role and the staff member's confidence level. A blended approach often works best: localised materials for concepts and explanations, combined with consistent English-language terminology for regulatory and technical terms that staff will encounter in systems and documentation.