Customer Service
Multilingual customer support drafting
Detect the language of incoming customer messages and draft responses in the customer's language using AI translation and generation. Agent reviews and sends. Enables support across multiple markets without multilingual staff.
50–70%
reduction in multilingual handling cost
Opportunity assessment
Minor improvement. Small efficiency gain with limited effect on overall turnover or bottom line.
Relatively straightforward. Off-the-shelf tools handle the core task. Implementation is well-understood with good examples to follow.
Minimal data complexity. Works with whatever is readily to hand. No special data infrastructure needed. Most businesses can start immediately.
Moderate risk. Some customer or external exposure. Errors create rework or reputational impact but are recoverable.
Light people impact. A new tool slots into an existing workflow. Minimal training needed. Most people will adapt quickly.
Tooling required
Things to consider
Modern LLMs handle major European and Asian languages well, but accuracy varies for less common languages and regional dialects. Validate on your actual incoming language mix before deploying.
Human review of AI-translated responses is essential, ideally by a native speaker. Using monolingual agents to review translations they cannot read defeats the purpose.
Brand tone is difficult to preserve across languages — what reads as warm and direct in English may read as blunt or informal in another language. Get native speaker feedback on tone, not just accuracy.
Consider the legal context: in some markets (Quebec, Wales) there are language rights obligations. AI translation does not automatically satisfy these, and errors in customer-facing legal or contractual language can create liability.
Experiment starter: Identify your top three non-English inquiry languages from the last six months. Run 30 historical tickets in each language through an LLM to generate translated responses and ask a native speaker reviewer to assess accuracy and tone. A pass rate above 85% on both dimensions justifies a live pilot for those language pairs.
Go deeper in the playbook
Section — Opportunity Identification, including the AI Opportunity Assessment ScorecardSection — The Experimentation FrameworkThis is an augmentation opportunity — see the relevant playbook section for how to approach it.More in Customer Service
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Customer feedback synthesis
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AI Transformation Playbook
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