Every RFP in language technology has a “trust question,” and every vendor tries to deliver the perfect answer. Yet six months into production, things still go wrong. Data ends up in the wrong jurisdiction. Model updates happen without notification. Costs triple beyond projections. The technology didn’t fail—but each stakeholder understood “trust” and “quality” to mean something completely different.
In a recent episode of the New Fluency podcast, Slator’s Managing Director Florian Faes and Head of Advisory Alex Edwards unpacked what enterprise buyers actually mean when they talk about trust. Their analysis reveals five distinct dimensions of trust that most organizations fail to differentiate—and the consequences of that failure can be severe.
1. Accuracy Trust: The One Everyone Asks About
Accuracy is the starting point of every trust conversation. Paradoxically, it’s the dimension that deserves the least time today because the baseline has fundamentally shifted. “Four years ago, people would say, oh, it’s AI, there must be 100% errors,” Florian explained. “Today, trust in AI output in general has risen dramatically. And that means trust in AI translation has risen dramatically too.”
The real question is no longer whether AI translation can be accurate—it’s whether the output is sufficiently accurate for a specific use case. A pharmaceutical label, a user-generated product review, and an internal FYI summary each demand very different accuracy thresholds. The challenge for buyers is assessing accuracy in terms that match their actual content portfolio, not a single abstract benchmark score that may have no relevance to their specific needs.
2. Consistency Trust: The Real Concern for Regulated Buyers
For financial services, pharmaceutical, and legal teams, perfect accuracy in a single output is table stakes. The harder question is about system reliability over time, not snapshot quality. A vendor that updates its model in Q2 without notifying you is a vendor you cannot trust for regulatory filings. A system whose terminology shifts subtly between versions introduces risk that compounds with every document processed.
Consistency trust means predictable, auditable behavior across updates, across document types, across the entire lifecycle of a localization program. Buyers in regulated industries need to know not just that today’s output is good, but that tomorrow’s output will be comparable—and that they will be informed when it won’t be.
3. Security Trust: The One That Kills Deals During InfoSec Review
Where does the data live? Who can see it? Alex identified a class of questions that tends to surface late in the evaluation cycle: “The trust about how localization content is being produced—whether by humans or AI—the transparency around that information, together with audit capabilities and security measures, all constitute trust. And then you have where the data sits, how the data is processed.”
This is the requirement that emerges late. Functional evaluation is complete, stakeholders are aligned, and then the InfoSec team asks a question nobody thought to include in the RFP. At that point, the deal either survives or dies based on whether the vendor’s data handling architecture can satisfy enterprise security standards—GDPR compliance, data residency options, encryption at rest and in transit, and documented sub-processor relationships.
4. Scale Trust: “It Worked in the Demo” Is Not a Guarantee
“People need to trust that AI translation works at scale. It’s not a toy, not something that only works in a demo or a pilot project—when you feed it millions of words, it has to actually work, or it will collapse in production.” This is the dimension where cost enters the picture in ways buyers rarely anticipate upfront.
Florian added the cost dimension that buyers typically overlook: “We’ve heard from companies that spent $1.5 million on Claude tokens in the first month alone.” That isn’t a vendor failure per se—it’s a scale trust failure. The system technically works, but the economic model breaks at production volume. Scale trust means the service performs predictably—technically and financially—when processing the full volume of content an enterprise actually generates.
5. Outcome Trust: The Requirement Most Buyers Completely Overlook
Most enterprise buyers evaluate language AI through output quality metrics: MTQE scores, reviewer assessments, error category counts. But they rarely measure whether differences in translation quality actually drive business outcomes. The metrics that matter more are: conversion rates on multilingual websites, changes in customer support ticket volume, and Net Promoter Score gaps between native-language and translated-content users.
Buyers who build this measurement infrastructure now will be able to make vendor decisions based on real signals rather than proxy metrics. They won’t just know which translation is “better” in the abstract—they’ll know which translation generates more revenue, reduces more costs, or improves more customer relationships. That’s the level at which trust becomes a competitive advantage rather than a check-the-box exercise.
“Almost everyone’s trust baseline is accuracy,” Alex concluded. “But it depends on what kind of buyer you’re talking to.” That’s exactly the problem. Every supplier answers the quality question at the accuracy level because that’s the question they are asked. The other four requirements remain in slide deck territory, unaddressed, until production reveals them.