As Indian filmmakers increasingly chase pan-India and global audiences, artificial intelligence (AI) is emerging as a powerful tool to make multilingual releases faster, cheaper and commercially viable.
The technology is moving beyond experimentation into real-world production, with upcoming big-budget films such as Ramayana exploring dubbed versions in more than 40 languages. While AI could dramatically expand the reach of Indian content, industry experts caution that distribution challenges, voice rights and legal safeguards will determine how quickly the technology scales.
“Traditionally, dubbing involves script adaptation, voice casting, recording, editing and synchronization, making it both time-consuming and expensive. AI-powered technologies are increasingly assisting with translation, voice synthesis, lip synchronization and localization, helping streamline several parts of the process,” said Kaushik Das, founder and CEO of OTT platform AAO NXT.
This could enable content owners to launch films and series simultaneously in multiple languages, including those that may not have been commercially viable earlier, Das added.
Beyond experiments
The industry has already moved beyond basic experimentation and is now entering what can be described as the pilot-to-production phase, according to Unnatie Tussar, founder of language intelligence platform Ant Systemz.
Core technologies such as AI-powered translation, speech synthesis, voice cloning, speech-to-speech conversion and lip synchronization are already being explored by studios, streaming platforms and localization companies globally.
“Earlier AI applications focused primarily on subtitling and machine translation. Today, the focus has expanded to preserving actor voices, maintaining emotional authenticity, adapting content culturally, and creating multilingual versions that feel native to the target audience,” Tussar pointed out.
How AI helps
Large language models (LLMs) can understand the context, tone, character dynamics and genre of a film, enabling more nuanced translations instead of word-for-word conversions.
Neural voice models can recreate an actor's vocal characteristics across multiple languages while preserving emotional tone and character identity. AI can also analyse pacing, intonation, pauses and emotional intensity to produce more natural-sounding dubbed dialogue.
Computer vision models can synchronize translated dialogue with on-screen lip movements, reducing the disconnect often associated with dubbed content. AI can also assist with lyric adaptation, vocal recreation and maintaining rhythm and timing across languages.
“India is not a single film market—it is a market making content in 22 languages and we have been leaving most of that audience on the table because traditional dubbing was too expensive and too slow to justify going beyond four or five languages. AI changes that equation entirely,” said Varun Mathur, founder of film studio Connekkt Media.
Connekkt's Cinematic AI Lab is already producing cinematic content using frontier AI tools, creating films and series where the original asset has either been generated or substantially completed using AI.
“When the content and the dubbing pipeline are both AI-native, the ability to produce a multilingual, globally distributed film at a cost structure that was previously unthinkable becomes a reality. The content creation and the localisation become one continuous workflow rather than two separate expensive processes,” Mathur added.
Challenges remain
The technology may be ready, but the industry's regulatory and commercial frameworks are still evolving.
For one, the displacement of voice artists is a growing concern. Mathur said the industry needs conversations involving studios, streaming platforms and artists around consent, residual payments and reskilling.
He also pointed to another challenge that receives less attention—the economics of smaller language markets.
The AI models perform well for the six or seven largest Indian languages where training data is abundant. However, delivering the same quality for languages such as Odia, Assamese or Manipuri will require significant investment in language data infrastructure.
Finally, as AI-generated content itself becomes more common, authenticity will become increasingly important.
Audiences may embrace AI dubbing when it enhances storytelling seamlessly, but they are unlikely to accept it if the technology appears to replace creative craftsmanship rather than support it.
“India's linguistic diversity makes it a natural market for such innovation, and the industry is already exploring AI-enabled localisation solutions. However, the opportunity must be balanced against important legal and creative considerations, including performer consent, voice rights, personality rights and fair compensation. The long-term success of this technology will ultimately depend on how effectively the industry balances innovation with authenticity and appropriate legal safeguards,” said Ketan Mukhija, partner and co-head, PE (private equity) and VC (venture capital), Kochhar & Co.