The update began late Tuesday, with the company saying posts in any language can now be translated automatically for users across the platform.

The feature, powered by Grok, the AI model developed by xAI, has a translation quality which has “improved substantially over the last couple months.”

Users can still manage the feature as they want. Any user who prefers reading posts in their original language, can turn off auto-translate through the settings icon on each translated post.

At the same time, X is adding new image editing tools within its iOS app. The update brings basic options like drawing and text, along with a blur tool that allows users to hide parts of an image, including faces or sensitive details.

There is also a new option to edit images using simple written instructions. Users can ask Grok to change how a picture looks.

For example, they can request a version styled like a painting, and the system will generate it.

The company says the update will reach Android devices later, though no date has been confirmed.

These changes place X alongside other tech companies already providing similar tools. Reddit has been testing translation features for some time, while Google and Adobe have introduced image editing systems that respond to written prompts.

The rollout of auto-translate also comes after the issues raised against X and its owner Elon Musk. At the time, regulators and users complained about misuse of AI tools, especially where images were altered without consent.

The company later limited some of those features to paying users.

• Translation quality is “mission-critical” for 96% of companies, but only 57% say their brand voice is consistent across languages.
• AI translation has moved into core enterprise infrastructure, with 79% saying it’s now part of broader AI transformation led by IT.
• Despite rapid AI adoption, enterprises aren’t letting go of humans: only 1.8% ship raw AI translations without review.
• Vendor sprawl is hurting results: 34% report quality issues and 31% report communication breakdowns across multiple translation providers.
• AI translation ROI is split down the middle, with 48% see speed and cost gains, while 52% say the benefits still aren’t there.
• Translation is no longer a back-office task: 69% say it now sits at the heart of customer support.
• Translation has become a growth lever, not just an accessibility tool, with 53% linking it directly to revenue and market expansion.

AI Translation is Now Business-Critical, But most companies admit quality isn’t keeping up

AI translation has quietly crossed a threshold inside global organizations. What was once treated as a support function or experimental efficiency tool is now embedded in core business operations, spanning IT, customer support, product, and revenue teams.

But a 2026 survey suggests adoption has outpaced execution.

According to new data from more than 400 translation decision-makers, conducted by Zogby Analytics on behalf of LILT, an AI-powered translation and localization platform, nearly all organizations now view translation quality as mission-critical, yet only about half believe they are delivering consistent multilingual output.

The result is a growing gap between how essential translation has become and how confidently companies can scale it.

Quality Is Non-Negotiable, Yet Consistency Is Elusive

The survey found that 96% of respondents say translation quality is mission-critical, underscoring how closely language accuracy is tied to brand credibility, trust, and performance in global markets. Quality also ranked higher than speed and cost across roles, particularly within IT and technology teams.

However, when asked whether their current translation process maintains a consistent brand voice across languages, only 57% said yes.

This disconnect reveals a structural issue rather than a lack of intent. Organizations overwhelmingly want high-quality translation, but many lack the governance, workflows, or oversight required to achieve it at scale.

“What this data shows is that translation has moved into the same category as security, infrastructure, and reliability,” says Spence Green, CEO of LILT. “Teams understand the risk of getting it wrong, but many are still relying on fragmented tools and processes that weren’t designed for enterprise-scale quality.”

AI Translation Has Moved Into the Core AI Stack

AI is no longer being tested on the margins of translation workflows. The survey found that 79% of organizations say AI translation is part of their broader AI transformation, led primarily by IT and technology teams rather than marketing or localization departments.

Half of respondents expect their use of AI translation to increase significantly over the next one to two years, signaling that translation has become a long-term infrastructure decision rather than a short-term experiment.

This shift reflects a broader trend in enterprise AI adoption, where leaders are prioritizing systems that reduce time-to-market, support global operations, and integrate across existing platforms.

“Translation is no longer a back-office task,” Green explains. “It’s a growth enabler. When teams can launch products, support customers, and communicate internally across languages without friction, it directly affects speed, revenue, and competitiveness.”

Humans Remain Central to Translation Quality

Despite rising AI adoption, organizations are not removing human oversight from translation workflows. In fact, the opposite is true.

The survey found that 79% of respondents plan to keep a human-in-the-loop, and 52% rely on in-house linguists for post-editing AI output. Only 1.8% reported using raw AI translations without any quality checks.

These findings suggest that enterprises see AI as a productivity engine – not a substitute for accountability, nuance, or cultural understanding.

“AI is excellent at scale and speed, but it still needs guidance,” says Green. “Human expertise is what protects brand voice, tone, and intent. The organizations getting this right are treating AI and humans as a system, not as replacements.”

Vendor Sprawl Is Undermining Results

One of the most consistent pain points uncovered by the survey is vendor complexity. Nearly 70% of respondents use multiple translation vendors, often alongside general-purpose tools such as large language models.

Among those organizations:

• 34% report quality issues
• 31% struggle with communication
• More than half cite increased administrative burden and delivery challenges

Rather than improving outcomes, fragmented vendor ecosystems are creating silos, inconsistencies, and operational drag, especially as translation volumes increase.

“When translation is spread across too many tools and providers, no one owns quality end-to-end,” Green says. “That’s where inconsistencies and delays creep in, even when the underlying technology is strong.”

Translation Is Now Tied Directly to Revenue and Customer Support

The survey also highlights how translation has moved beyond compliance or accessibility into core business performance.

• 69% of respondents say translation is central to customer support
• 53% directly attribute revenue growth and market expansion to translation efforts
• 52% say translation is essential for internal and external communication

For many organizations, multilingual communication is now inseparable from customer experience, retention, and global growth strategies.

“If customers can’t understand you, they can’t trust you,” Green notes. “Translation has become one of the clearest links between operational efficiency and customer loyalty.”