Global M&A value climbed to $4.8 trillion in 2025, the second-highest total on record according to Bain & Company, and international transactions alone accounted for roughly $1.46 trillion of that figure, up 29% year-on-year. Every one of those international deals generated a pool of documents, including contracts, disclosure schedules, financial statements, and regulatory filings, that existed at some point in the process in a language one side of the table could not read natively. Due diligence teams have always solved that problem with translators. What has changed in recent years is which translator, human or machine, gets the first look at a document.
With AI now firmly established in the translation industry, serious questions have emerged about its influence on businesses and markets. To shed some light on what is actually happening, we gathered insights from an industry professional, The Word Point, a translation company working with legal, financial, and corporate development teams on business translation, who will guide us through the most important questions.
Translation Is Now a Digital Product
Physical data and documents, shipped between advisers, printed disclosure schedules, and couriered overnight, are close to extinct in serious M&A practice. Virtual data and digital materials handle the overwhelming majority of due diligence document exchange today, and the translation work sitting alongside them has followed the same path. Translation is requested, delivered, and reviewed almost entirely online, with certified and business translation now functioning as a fully digital service rather than a paper-based one.
A digital-first translation workflow behaves differently from a paper-based one in one important respect: speed invites volume, and volume invites shortcuts. When a machine translation engine returns a rough version in under a minute, the instinct is to paste it in and move on. That instinct is where the risk lives, not in the technology itself, but in the absence of a deliberate decision about which deal documents are permitted to skip human review.
How Deal Teams Actually Restructured Their Translation Workflow
The shift toward AI-assisted translation in corporate transactions has not been uniform, and the split roughly tracks the split in risk tolerance that already exists inside most deal teams. Machine Translation Post-Editing, MTPE, where an AI engine produces a first draft and a qualified linguist corrects and finalises it, has become the default for high-volume, lower-stakes categories, such as internal correspondence swept up in document review, historical operational records, marketing collateral belonging to the target company, and the long tail of routine vendor contracts that rarely affect valuation. For that tier of content, MTPE genuinely compresses timelines that used to take weeks out of a due diligence period.
The documents that determine whether a deal closes on the terms both sides believe they agreed to sit in a different category entirely. Harmonizing HR policy across a newly acquired entity or reconciling two employee handbooks is where MTPE works best, provided the final legally operative version still passes through a linguist who understands both the language and the labor law framework it needs to hold up under.
The Risk That Doesn’t Show Up in the Purchase Price
Ask a corporate development team what could go wrong with AI-assisted translation during due diligence and the answer is usually about accuracy – a mistranslated liability clause, a currency figure rendered incorrectly, a warranty that reads differently in the target’s language than in the buyer’s. Those risks are real. The less discussed risk sits beneath the words themselves, in what happens to the underlying data once a document enters a translation tool that was never vetted as part of the deal’s confidentiality architecture.
A deal team pasting a disclosure schedule into a general-purpose AI chatbot is not just risking accuracy, it may be creating a data exposure event that breaches the very confidentiality agreements the deal is conducted under. A signed Data Processing Agreement is the standard expectation for any translation provider touching deal-sensitive material, whether that provider is a dedicated professional translation service or an AI subscription an associate happens to have open in another tab.
Why Human Translation Still Closes the Deal
Machine Translation Post-Editing genuinely accelerates the volume of documentation a deal team can process, and for the categories of content where the cost of an error is a minor correction rather than a renegotiated warranty, that speed is a legitimate competitive advantage in a market where deal timelines are compressing.
Human translation, performed by a linguist with legal and financial subject-matter fluency in both languages, is what closes that gap, and it’s also what arbitrators and courts expect to see evidence of when a translated contract’s validity is challenged, a named professional, a defensible review process, and a document that was treated as a legal instrument rather than a block of text.
Businesses chasing the best translation rates by routing every document through the cheapest available pipeline tend to discover, usually during post-closing disputes rather than before them, that the saving was considerably smaller than the eventual legal review bill.
The Bottom Line
AI-assisted translation, deployed deliberately and tiered by the stakes of each document category, gives deal teams a genuine capacity advantage in a market where 2026’s dealmakers are managing more transactions, larger transactions, and tighter timelines than in recent years. Deployed without that tiering, the same tools quietly convert a routine due diligence task into a confidentiality breach and a contract enforceability question that surfaces at the worst possible moment, after signing.
The deal teams managing this well aren’t the ones avoiding AI, and they aren’t the ones handing every document to it either. They’re the ones who decided, exactly which categories of transaction documentation get machine-assisted speed and which ones require a qualified human translator’s name on the final page.
