Translation

Most translation POCs end the same way: inconclusive results, stakeholders with different takeaways, and no clear path forward. Here’s how to run one that actually produces a decision.

Every enterprise exploring AI for customer support eventually arrives at the same fork in the road. One path leads toward building something internally with a model like Gemini or ChatGPT. The other relies on whatever translation capability is already bundled inside the CRM or CCaaS platform. Engineering teams assume the problem is mostly API calls and prompts. Platform buyers assume the built-in feature will be “good enough.”

You have done the right things. You built the training programs. You created escalation paths. You brought in consultants and rolled out resilience curricula and made sure every agent knew what to do when a customer crossed the line. The intentions were good. The investment was real. And your burnout rate is still 59 percent.

When a global brand starts looking closely at its customer conversations, the first surprise is usually not what customers are saying, but how inconsistent the company sounds in response. This becomes exponentially more difficult to control at the scale of Vista, a global leader in design and marketing services for small businesses, where millions of customer interactions span countries, languages, and support channels.

Agent burnout isn’t a people problem. It’s a P&L problem and it’s hiding in plain-sight across your turnover costs, satisfaction scores, and the calls that close clean but shouldn’t have. This is what it actually costs when the infrastructure protecting your agents hasn’t kept pace with what they are being asked to absorb.

For years, enterprises have treated language support as something that happens after the fact. Documents get translated later. Tickets are reviewed and localized in batches. Voice calls are routed to native speakers or external interpreters when available. This model made sense when customer engagement moved slowly and interactions were easy to pause. That world no longer exists. Today, most meaningful customer conversations happen live, under time pressure, and with very little tolerance for friction.

Today’s generative AI models have revived the assumption that If it sounds good, it must be correct. But the reality is that translation accuracy depends heavily on context, not just linguistic ability.

The truth is simple: You can’t create great customer experiences if your agents are miserable. When an agent is stressed out, jumping between six different programs, and watching the clock tick, they can’t give customers the attention they deserve. All their energy goes into just managing the technology instead of actually helping people. If we want happy customers, we need to start by making life better for agents.

In English, we can be informal without being insulting. The language gives us a wide neutral zone. Korean does not. Korean forces you to choose a relationship, not just a sentence, and customers hear that choice immediately.