From compliance deadlines to dubbing at scale, localization drives AI adoption in broadcast

Localization was among the first areas where AI moved from experimentation into daily broadcast and media operations, and it remains one of the most widely cited examples of measurable workflow impact. Across a three-part Industry Insights roundtable on AI and media workflows, contributors described captioning, subtitling, translation and dubbing as the use cases where that shift has been most consistent and most broadly adopted.

The convergence of mature speech-to-text technology, measurable cost comparisons and clear regulatory requirements has made localization easier to evaluate than most other AI deployments – and easier to justify.

A decade ago, localization meant sending content to an outside service, waiting for captions or translations to return and then manually matching them to the original. For international distribution, that process could multiply across 10 to 20 language versions, each requiring the same cycle of handoffs and reconciliation.

That model has changed substantially through automation and new workflows created with AI-driven tools. These have compressed timelines and reduced the reliance on external vendors for workflows that were once almost entirely outsourced. But the shift, at least in dubbing, was already underway before AI entered the conversation.

“The localization industry was already changing before AI showed up. There’s a lot more content to localize now, and the delivery schedules have become shorter. Dubbing studios were moving from manual operations to something more structured. AI is just one part of that,” said Anton Dvorkovich, CEO of Dubformer.

Dvorkovich described much of the traditional manual work in dubbing as centered on logistics: coordinating voice talent, booking studio time, managing handoffs. Remote recording, which became standard during the COVID-19 pandemic, had already removed some of those bottlenecks. What followed was not a reduction in skilled roles but a shift in what those roles look like.

“Proof listeners, cultural adaptation experts, people with fifteen years at dubbing studios are now directing AI voices instead of managing bookings. These roles barely existed two years ago,” Dvorkovich said.

For organizations operating at scale, that shift opens distribution possibilities that traditional dubbing economics could not support.

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“The problem is everything that never gets dubbed because the economics don’t allow it. Live sports in 60 languages. You can’t hire commentators for that. Secondary catalogues, small-language markets. This isn’t about cutting costs on what already gets dubbed. There’s a huge amount of content that people want to watch and can’t because it’s not in their language,” Dvorkovich said.

“Compared to sending content out for captioning or translation, the savings are immense,” said Charlie Dunn, executive vice president, products, Telestream. “Within Vantage workflows, we are generating captioning and/or subtitling using AI, and if needed, we can create translations into 120 different languages of those captions and subtitles into an IMF or similar package.”

Why localization succeeded where other use cases have not

Contributors in our recent roundtable pointed to several reasons localization has proven more durable than other AI deployments. The inputs and outputs are well defined. The ROI comparison against manual workflows is straightforward. And the technology underpinning speech recognition and translation has been in development long enough to produce reliable results at scale.

“What I am seeing as fully in production and pretty standard industry-wide are localization workflows and QC — language detection, speech-to-text, translation, subtitle generation, automated QC flags,” said Clara Aler, head of marketing, Knox Media Hub. “There’s a clear ROI: faster localization workflows, easier to deliver content globally, and the human review process is infinitely faster compared to entirely manual translation. AI has a long and successful history in this domain, making it a reliable area for good results.”

Rosen placed localization alongside transcription and captioning as the areas where AI had most clearly crossed from pilot into production. Those deployments, he noted, share a common characteristic: they reduce friction without requiring AI to make creative decisions.

The observation reflects a broader pattern across the roundtable.

AI deployments that handle high-volume, well-defined tasks with clear inputs tend to perform more predictably than those applied to editorial judgment or creative output. Localization fits that profile more cleanly than most.

Regulatory compliance as a driver

Localization also benefits from external pressure that other AI use cases lack. Captioning and subtitling requirements are governed by regulation in many markets, creating a compliance deadline that accelerates adoption in ways that internally driven efficiency goals often do not.

“For some use cases, such as semi-automated caption creation, accuracy is critical in order to satisfy regulatory guidelines, but in others, having any data at all is vastly superior to having none, even if it’s not 100% accurate,” said Geoff Stedman, chief marketing officer, SDVI.

In Europe, that regulatory pressure extends specifically to language access. Dvorkovich described a gap between what broadcasters are required to offer and what traditional dubbing economics can deliver.

“Regulation already says broadcasters need to offer content in local languages, subtitles or voiceover for local audiences. For smaller languages, the economics of traditional dubbing just don’t work. You have audiences there, but nobody’s paying for dubbing into those languages the traditional way. That’s where AI comes in,” Dvorkovich said.

He also addressed a set of questions that he said every client raises around AI dubbing: whether vendor systems train on client content, who has access to synthesized voices and what happens to source material.

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“It’s all copyright, identity, consent. And there are models out there where it’s genuinely unclear what they were trained on,” Dvorkovich said.

That distinction shapes how organizations approach quality thresholds in localization workflows.

Captioning for broadcast requires a higher standard of accuracy than, for example, generating a rough transcript for internal archive search. AI deployments that serve compliance functions are typically designed with tighter review requirements than those supporting discovery or tagging workflows.

Dubbing and voice: capability and limits

While captioning and subtitling are the most established applications, dubbing represents the area of most active development – and the widest gap between what AI can do consistently and what audiences expect from a full performance.

Steph Lone, global leader, solutions architecture, media and entertainment, games and sports, Amazon Web Services, described generative AI being applied to localization at scale, producing contextually appropriate voices and captioning across large content libraries. Localization and dubbing, she added, were among the clearest areas where AI was accelerating workflows and saving teams money.

Dvorkovich framed the quality question as one of delivery as much as generation. Standard text-to-speech systems, he said, produce output without reference to how the original was performed, resulting in dubbed content that carries the same tone and energy regardless of what the source material requires.

“A nature documentary and a sports broadcast come out with the same energy. We use the original phrase as the reference instead. When a narrator drops their voice for something quiet, the dubbed version drops too. That’s what a voice actor does in a traditional studio,” Dvorkovich said.

He was direct about where the technology still falls short.

“Complex emotional scenes are hard for everyone right now. We’re not there yet, but for a lot of content, the results are airing,” Dvorkovich said.

Human review remains part of the process

Human review remains a consistent element of every workflow described, though the nature of that review has changed.

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“Multi-language media indexing enables fast discovery of thousands of hours of interviews, overseen by librarians to ensure accuracy and consistent editorial voice,” said Phil Petitpont, co-founder and CEO, Moments Lab, describing deployments at Asharq News.

Stedman noted that when captions for a given language already exist, creating a translated version for operators to validate provides significant efficiency gains compared to building from scratch. The human role shifts from performing the work to verifying it, a distinction that contributors described as the design condition that makes AI-assisted localization sustainable at scale.

Santiago Miralles, founder and CEO, Knox Media Hub, described the balance in operational terms. Having one or two people validate AI-generated outputs in localization, editing and versioning workflows was significantly faster than fully manual processes, he said, without removing editorial accountability.

The business case for AI-assisted localization strengthens as distribution windows expand.

Streaming platforms, social media distribution and global licensing deals have increased both the volume of content requiring localization and the speed at which it needs to be delivered.

For organizations managing large content libraries across multiple languages and platforms, the compounding effect of faster turnaround, lower per-unit cost and reduced dependence on outside vendors has made localization one of the more straightforward cases for AI investment. The question for most organizations is no longer whether to automate localization workflows, but how far to extend automation before human review becomes essential.