Most enterprise risk registers account for cybersecurity, GDPR, and supply chain disruption. Almost none account for the quiet reputational and contractual damage caused by unverified AI translation in day-to-day B2B correspondence. In 2026, that gap is no longer acceptable.

There is a moment every enterprise sales director has experienced but rarely documents. You send a proposal to a prospective partner in Munich or Tokyo. The follow-up goes cold. The deal dies without explanation. You write it off to “a cultural fit issue” or “the timing wasn’t right.”

What if the timing was fine? What if the problem was a mistranslated clause, a tone that read as dismissive, or a single term that carried an entirely different legal obligation in the target market, and nobody on either side of the table could tell?

This is the B2B correspondence problem nobody puts in the risk register. And according to the data emerging in 2025 and 2026, it is costing companies more than they realise.

How Big Is the AI Translation Market, and Why Does Scale Amplify Risk?

The global AI translation market is projected to reach $7.16 billion by 2029, growing at a compound annual rate of 25%. Adoption is not incremental.

It is structural. Gartner estimated that over 60% of enterprise content teams had integrated AI-based translation into at least one workflow by 2024, with the figure climbing significantly throughout 2025.

In finance alone, AI translation use jumped 700% between 2023 and 2024, according to Lokalise’s 2025 Localization Trends Report. Healthcare and legal sectors show similar trajectories.

The problem is not that AI translation is being used. The problem is that it is being used without verification, and at this scale, even a low error rate generates enormous real-world risk.

What Does “Unverified AI Output” Actually Mean in Practice?

The language technology community on Reddit has been particularly vocal about this. As one user summarised in a frequently-cited r/LanguageTechnology thread: “The biggest issue isn’t that AI makes mistakes. It’s that you can’t easily tell when it’s wrong unless you speak the target language.”

That observation cuts to the heart of the B2B risk problem. When a monolingual sales director sends an AI-translated proposal, they have no mechanism to verify what was actually communicated. They trust the output the way a passenger trusts a taxi driver they cannot see.

The consequences, documented across multiple 2025 studies, are concrete:

• AI translation tools carry hallucination rates of 10–18% for individual large language models, even with frontier models like GPT-4o and Claude 3.5 Sonnet
• A 2025 study found that up to 47% of contextual meaning is lost in conventional AI translations, particularly for cultural and historical depth
• Error rates for AI-only translation of legal and business documents can exceed 20–25%, creating liability exposure on every contract sent without review
• When customers encounter poorly translated content, 75% report decreased trust in the brand, and 64% say they are less likely to purchase, according to a 2024 consumer survey

Why Is This Not on the Risk Register?

The short answer: because the harm is invisible.

When a cyberattack occurs, there is a breach notification. When a contract is voided, there are legal proceedings. When an AI-translated proposal confuses or quietly offends a counterpart, the deal simply does not progress. The cause is never attributed to the translation.

Research published in 2025 by Tranquality identifies this as the “correspondence error asymmetry problem.” AI translation output can be fluent yet wrong in meaning, and this is undetectable to any monolingual reader. Fluency creates a false signal of accuracy. The document looks right. The sentence structure is clean. The error is invisible until someone downstream, in a different language, acts on a different understanding.

In B2B correspondence, this dynamic plays out across proposals, term sheets, partnership agreements, compliance notices, and client-facing communications: every category of document that shapes commercial relationships.

 

Is There a Structural Solution, Not Just a Workaround?

The industry’s current standard response is a tiered approach: use AI for low-stakes content, add human review for high-stakes content. This is sensible. But it has a practical flaw: in the middle tier, the high-volume, medium-stakes correspondence that defines enterprise sales operations, human review is rarely applied consistently, and there is no framework for verifying which outputs actually need it.

A more structurally sound approach is verified AI translation: not simply running text through an AI model, but verifying that output against multiple independent AI models before it is sent.

Imagine you are in a room with 22 translators. If one says a term means “binding obligation” but the other 21 say it means “recommendation,” you trust the majority. The single outlier was likely hallucinating. That is not merely an analogy. It is the mathematical principle behind consensus-based translation verification.

MachineTranslation.com SMART feature operationalizes this principle at scale. Rather than translating with a single AI model and accepting its output, SMART runs the text through 22 independent AI models simultaneously. It then selects the version that the majority agrees on, sentence by sentence. This is not aggregation; it is verification. The output is not the “best” translation from one model; it is the translation that 22 models independently corroborate.

The data support the approach. According to Intento’s 2025 State of Translation Automation report, multi-model validation workflows reduced errors by 80–90% compared to single-model baselines. SMART applies this principle to reduce hallucination rates to below 2%, compared to 10–18% for single-model solutions.

System	Hallucination Rate	Post-Edit Required
GPT-4o (single model)	12–18%	Often
Claude 3.5 Sonnet (single model)	10–15%	Sometimes
DeepL (single model)	6–9%	Sometimes
SMART (22-model verification)	<2%	Rarely

 

What Should Enterprise B2B Teams Actually Do?

The question is not whether to use AI translation. The AI translation market is growing at 25% CAGR because the efficiency gains are real and the cost advantages are significant. A 2025 DeepL survey found that 96% of B2B leaders reported a positive ROI from localization efforts, with 65% seeing at least a 3x return.

The question is whether the AI translation output leaving your organisation has been verified or whether it is simply trusted.

For SMEs in particular, this distinction matters disproportionately. A multinational with a dedicated language services team can absorb an occasional mistranslation in a proposal; the relationship has other touchpoints and resources to recover it. An SME operating in Germany, Japan, or the Gulf Cooperation Council for the first time does not have that buffer. One correspondence failure at the wrong moment can permanently close a market.

Practical steps worth embedding in standard operating procedure:

For outbound proposals and term sheets:

Use AI translation as a first draft, never as a final output. Apply a verified translation workflow in which output is cross-checked against multiple AI models before transmission.

For inbound correspondence:

Do not assume a counterpart’s AI-translated communication represents their precise intent. Where contracts depend on exact language, request the original document and verify the translation independently.

For compliance and regulatory communications: 3

The Tranquality AI Safety Report recommends what it terms “Professionally Verified Translation” as the standard for any document where undetected error has regulatory consequence. In 2026, this is worth formalising in procurement and legal workflows.

 

The Reputational Exposure That Risk Teams Are Missing

There is a harder point worth making directly. The Slator 2025 report found that accuracy concerns affect 72% of AI translation adopters, and quality concerns affect 68%. These are not abstract worries. They represent the professional consensus that AI translation output, as currently deployed in most organisations, is not consistently reliable for B2B correspondence.

What the risk register does not capture is the reputational exposure that accumulates before it becomes visible. ASTA-USA founder Alain J. Roy articulated the issue plainly in a 2025 global warning on AI translation risk: a single mistranslated clause can void a deal, and the damage to trust compounds because it is rarely attributed to its actual cause.

The B2B world runs on trust built through correspondence. Every email, proposal, and agreement is either adding to or subtracting from a relationship. Unverified AI translation introduces a structural variable into that process, one that is invisible to the sender, invisible to the risk team, and only visible to the recipient who reads a different meaning than was intended.

That is not a technology problem. It is a governance problem. And governance problems belong on the risk register.

The Emerging Professional Standard

The industry is moving. Intento’s 2025 research and Slator’s analysis both point to multi-model, requirements-based translation as the trajectory of enterprise adoption. The principle that no single AI model should be trusted without independent verification is becoming the baseline expectation in regulated sectors: finance, legal, and healthcare. It is migrating into general enterprise operations.

Organisations that implement verified AI translation workflows now are not ahead of the curve; they are aligned with where professional standards are converging. Organisations that continue to treat raw AI output as acceptable correspondence are carrying an unregistered risk, one that will become visible at the worst possible moment.

The risk register exists to prevent that moment. It is time to put B2B correspondence in it.

FAQs

What are the risks of using unverified AI translation in B2B? Unverified AI translation causes “invisible” risks, including legal liability, high hallucination rates, and silent reputational damage that kills international business deals.

2. Why should AI translation be on an enterprise risk register? It should be listed because mistranslated contracts or offensive tones create structural governance issues that standard cybersecurity or GDPR audits miss.

3. What is the hallucination rate for standard AI translation models? Leading frontier models like GPT-4o and Claude 3.5 show hallucination rates of 10–18%, which are undetectable to monolingual business users.

4. How does verified AI translation improve document accuracy? Verified workflows, like consensus-based multi-model validation, reduce hallucination rates to below 2% and decrease translation errors by up to 90%.

5. Can AI translation errors affect B2B sales and brand trust? Yes. Research shows 75% of customers lose trust in brands with poor translations, leading to unexplained drop-offs in global sales.

6. What is the “correspondence error asymmetry problem” in AI? This occurs when AI output appears fluent and professional but conveys the wrong meaning, misleading the sender regarding the document’s accuracy.

7. How should SMEs handle AI translation for international markets? SMEs should use verified workflows for outbound proposals and treat incoming AI-translated documents with caution, always seeking independent secondary verification.

8. What is the emerging professional standard for enterprise translation? The 2026 standard is migrating toward multi-model, requirements-based translation, ensuring no single AI model is trusted without independent algorithmic corroboration.