Nobody budgets for this. There is no line item called “extra minutes because the customer and the agent were working in different languages,” so the cost shows up somewhere else — in staffing models, in queue times, in a handle-time average that crept up eight percent last year and nobody could explain. Language barriers in customer support are one of the few operational costs that never appear under their own name.

They are also easy to underestimate, because the failure mode is not dramatic. The call does not go wrong. It just takes longer, and then it takes longer again next week.

Five ways language barriers in customer support add minutes

1. Description takes longer than diagnosis. Before an agent can solve anything, the customer has to explain what happened. A customer working in a second language reaches for the words, restates, approximates. The agent asks clarifying questions that would not have been necessary. Two minutes gone before the actual problem is on the table.
2. Verification slows down. Account numbers, addresses, spelling a surname aloud — the parts of a call that are pure information transfer are exactly where a language gap bites hardest, because there is no context to fill the gaps with.
3. Agents over-confirm. A good agent who suspects they have not been fully understood will repeat back, rephrase, and confirm again. It is correct behavior, it adds time, and QA reads it as thoroughness.
4. Transfers multiply. When a bilingual agent is available but not the one who answered, the call gets moved. The customer re-explains from the beginning to the second agent. Handle time is now being counted twice for one problem.
5. Wrap-up expands. Notes written from a conversation the agent is not certain they understood take longer and land less accurately, which damages the next interaction on the same account.

The closest hard measurement comes from healthcare

Contact centers rarely measure this. Hospitals do, because the stakes forced them to.

A study in the Journal of General Internal Medicine by Lindholm, Hargraves, Ferguson and Reed examined 3,071 patients with limited English proficiency at a tertiary care hospital and found that those who did not receive professional interpretation at admission, or at both admission and discharge, had lengths of stay between 0.75 and 1.47 days longer than patients who had an interpreter at both points. The gap was not caused by sicker patients; the models controlled for illness severity. It was caused by communication.

A hospital stay is not a support call and the analogy should not be pushed further than it goes. But the mechanism is identical: when information transfer between two people degrades, the process that depends on it takes longer. A hospital measures that in days. A contact center measures it in minutes, thousands of times a month, and mostly does not notice.

The compounding part

Extra minutes are the visible half. The invisible half is the second contact.

An interaction that ends with the customer only partly sure of what was agreed produces a follow-up call. That call arrives as new volume, gets its own handle time, and lands in repeat-contact rate rather than anywhere near a language field — assuming the CRM captures language preference at all, which most do not.

Which means the real figure is not “handle time on calls from second-language customers.” It is total minutes per resolved issue, and the gap between those two numbers is where the argument for language coverage actually lives. Teams still comparing support partners on hourly rate are measuring the wrong side of that equation.

“Just use AI translation” is not yet the answer

It is the obvious objection and deserves a straight answer: real-time machine translation is improving fast and will absorb part of this problem. It has not absorbed it yet. Natterbox’s State of the Contact Center 2026 report, published in May 2026, drawing on 58.2 million calls and a survey of 178 contact center leaders, identifies multilingual coverage through AI translation as the use case earliest in its adoption curve among the four where AI is producing measurable gains. The same research found leaders assign 91% of high-stakes and emotional interactions to human ownership.

Those two findings sit together for a reason. The interactions where a language gap costs the most are precisely the emotional, high-stakes ones — a disputed charge, a denied claim, a cancellation. That is the category leaders are least willing to hand to automation, and it is the category where drawing the self-service line too aggressively backfires fastest.

What to do about it

Start by measuring. Add a language-preference field, segment handle time and repeat-contact rate by it, and compare within the same issue categories rather than across the queue. If second-language customers show the same handle time but a higher repeat rate, the minutes are hiding in the second call.

Then price the coverage against the queue rather than against the rate card. Teams that run that comparison usually end up weighing the bilingual call center outsourcing benefits of a partner in Tijuana, Guadalajara or Monterrey against the cost of the inflated queue they already have — and the arithmetic shifts once repeat contacts land on the correct side of the ledger.

One caution: fluency is not coverage. A bilingual agent available only during core hours produces a queue that behaves normally until 3 p.m. and badly afterward — averaging into a number that looks fine and describes nothing.

FAQ: How Language Barriers in Customer Support Quietly Inflate Handle Times