A few weeks ago, I attended a meeting where multiple technical teams gathered to discuss an internal AI translation tool. The conversation covered architecture, integrations, and how to scale the solution across the organization.
All valid topics. But then someone asked a question that stopped the room: "What results are we actually getting?"
The answer was honest and uncomfortable: "We don't know where we stand right now."
This situation is far more common than most organizations would like to admit. And it points to a fundamental gap in how companies approach AI translation workflows.
The Rush to Deploy, the Delay to Measure
When organizations adopt AI for translation, the focus tends to be on speed, cost reduction, and scalability. These are legitimate priorities. AI translation can process content at volumes that would be impossible for human teams alone.
But in the rush to deploy, governance often becomes an afterthought.
Governance in this context means having clear answers to basic questions:
- What is our current quality level?
- How do we define "good enough" for different content types?
- Which metrics are we tracking, and how often?
- Who is accountable when quality drops?
Without this framework, organizations end up with powerful tools they cannot properly evaluate. They may be producing great results. They may be producing problematic ones. The issue is they simply do not know.
The Legal Dimension
During that same meeting, someone from the legal team raised a point that shifted the conversation: publishing translations for international markets carries regulatory risk.
This is not hypothetical. Mistranslated product descriptions, incorrect legal disclaimers, or inconsistent terminology can expose organizations to compliance issues, customer complaints, and reputational damage.
Quality in AI translation is not just about user experience. It is about risk management.
When you cannot measure quality, you cannot manage risk. And when you cannot manage risk, you are one bad translation away from a problem that could have been prevented.
What Good Governance Looks Like
The meeting I attended also included teams that had made progress on this front. Their experience points to several practices that make a difference:
1. Define quality metrics before you scale.
Terminology accuracy, style consistency, context adherence. These are not abstract concepts. They are measurable dimensions that should be tracked from day one. Waiting until you have scaled to millions of words makes the problem exponentially harder to solve.
2. Train the system with curated content.
Teams that invest in glossaries, terminology databases, and blacklists see measurable improvements. One team reported significant quality gains after training their engine with campaign content from the previous year. The lesson: AI translation improves when you feed it structured knowledge about your brand.
3. Sample and validate regularly.
Even with AI, periodic human review provides a reality check. Taking random samples and having them reviewed by professional linguists creates a feedback loop that keeps the system honest. Without sampling, errors can compound silently.
4. Treat scalability and visibility as complementary, not competing.
Building a solution for the entire organization does not mean sacrificing control. In fact, the larger the scale, the more critical governance becomes. A problem that affects 1% of translations at low volume becomes thousands of errors when you process millions of words.
The Irony of Data-Rich, Insight-Poor Operations
Most organizations running AI translation workflows have access to enormous amounts of data. Every translation, every edit, every piece of feedback is logged somewhere.
The irony is that this data often sits unused. Companies know how many words they translated last month. They rarely know how well they translated them.
Building a governance framework is not about adding bureaucracy. It is about extracting value from data you already have. It is about moving from "we translated X million words" to "we translated X million words at Y% accuracy, with Z% requiring revision."
Before Your Next AI Translation Project
If your organization is deploying or expanding AI translation capabilities, consider this checklist before you go live:
- Can we measure quality today? If not, that is priority one.
- Have we defined what "acceptable quality" means for each content type?
- Do we have a sampling and review process in place?
- Is someone accountable for monitoring quality over time?
- Have we trained the system with our specific terminology and brand guidelines?
If the answer to any of these is no, governance should be your first workstream, not something you add later.
The Bottom Line
AI translation is a powerful capability. But power without visibility is risk.
The organizations that will succeed with AI translation are not necessarily those with the most advanced models or the highest throughput. They are the ones that can answer a simple question at any moment: Where do we stand right now?
If you cannot answer that question today, it is time to make governance a priority.
At Kobalt, we help organizations build localization operations with visibility and control built in from day one. If quality measurement is a challenge you are facing, we would be happy to share how we approach it.
Photo by Andrew Neel on Unsplash
