For most of media history, taking a story global required an enormous amount of additional work. A film, television programme, advertisement, training video or digital series created for one market needed translation, subtitling, dubbing, editing and sometimes significant cultural adaptation before it could reach audiences elsewhere. The largest media companies could justify this investment for their most important properties, but the economics became considerably more difficult for smaller productions, niche audiences and regional content.
Artificial intelligence is beginning to change those economics. Translation models can work across languages with increasing sophistication. AI-assisted dubbing can accelerate voice production, while speech technologies can help adapt audio for different markets. Generative systems can support subtitling, localisation and versions of visual or written material designed for different cultural contexts. Work that previously required separate production processes for every market can increasingly begin with a common technological layer.
The most interesting consequence is not simply that Hollywood can distribute more efficiently around the world. It is that stories originating almost anywhere may have a more realistic path towards audiences almost everywhere. A regional production in India, a documentary from South America or an independent programme from Southeast Asia can potentially cross linguistic barriers that once restricted its commercial reach. During my learning journey under Phaneesh Murthy, I have increasingly understood that technology creates some of its greatest economic impact when it removes a constraint that businesses previously accepted as permanent. As Phaneesh Murthy puts it, “The most powerful technology shifts are often the ones that make an old limitation suddenly look unnecessary.” Language may be becoming one of those limitations.
Translation Is Only the First Layer of Localisation
Making content understandable in another language is not the same as making it feel native to another audience. Literal translation can preserve the meaning of individual words while losing humour, emotion, cultural references and the rhythm that gives dialogue its personality. Anyone who has watched poorly dubbed entertainment or encountered an awkwardly translated advertisement understands how quickly technically accurate language can damage the experience.
AI localisation therefore needs to extend beyond replacing one language with another. Systems increasingly need to understand context. A joke that works in one country may need to be rewritten rather than translated. A metaphor may have no equivalent meaning elsewhere. A phrase that feels warm and conversational in one language might sound unusually formal when converted directly into another. Even the length of translated dialogue can create challenges because the new sentence may need to fit the timing of the original performance.
This is where human linguistic and cultural expertise remains essential. AI can accelerate the first stages of translation, produce alternatives and help localisation teams work across much larger volumes of content. Human specialists can then evaluate whether the result actually communicates the intended emotion and cultural meaning.
During my mentorship under Phaneesh Murthy, I have learned that automation should be applied carefully when the quality of an outcome depends on context rather than simple correctness. Language is a perfect example. A machine may produce a technically correct translation, while an experienced local editor recognises that nobody in the target market would actually speak that way. The strongest localisation systems will therefore combine AI scale with human cultural judgment.
Dubbing Could Become Available to Far More Content
High-quality dubbing has historically been expensive. It requires translators, script adapters, voice actors, recording studios, directors, sound engineers and significant coordination. For major films and television programmes with large international audiences, the investment can be justified. For smaller productions, the economics often mean relying on subtitles or limiting distribution to fewer markets.
AI-assisted dubbing could dramatically change this equation.
Speech technologies can help generate translated audio, match timing more efficiently and provide production teams with rapid drafts before final versions are created. Voice performers could potentially work with AI tools to create approved versions across languages while retaining greater consistency in character and tone. Smaller media companies may therefore be able to consider dubbed distribution in markets that would previously have been commercially unrealistic.
The strategic opportunity extends beyond entertainment. Educational content, corporate training, product demonstrations, news formats and digital media can all become accessible to much wider audiences when localisation costs decline.
The key will be preserving performance. A voice is not simply a delivery mechanism for words. It contains emotion, personality, timing and identity. Media organisations that treat dubbing purely as a technical conversion risk producing content that is understandable but emotionally flat.
As Phaneesh Murthy says, “AI should reduce the cost of the process without reducing the value of the experience.”That distinction will matter enormously as AI-assisted dubbing becomes more common.
Regional Content Could Become Global Content
The economics of traditional international distribution have historically favoured content created in languages with large existing markets or productions backed by companies capable of investing heavily in localisation. AI could weaken that structural advantage.
India offers a particularly interesting example because the country’s media landscape already operates across numerous languages and highly distinct cultural markets. A programme created primarily for a Malayalam, Tamil, Telugu, Bengali or Marathi audience may have strong storytelling potential beyond its original linguistic market, but localisation and distribution add cost and complexity.
If AI reduces those barriers, regional content can travel more easily both within India and internationally.
This does not mean every piece of content will suddenly become globally successful. Cultural specificity can sometimes limit international appeal, while in other cases it becomes precisely what makes a story distinctive. The important change is that language itself becomes less of an automatic barrier to experimentation.
Media companies can test content in additional markets at lower cost, analyse audience response and invest further when genuine demand emerges. Smaller productions therefore gain access to a distribution strategy that was previously more accessible to large studios.
This reflects another idea I have taken from Phaneesh Murthy: when technology lowers the cost of experimentation, companies should not simply use it to do the same things more cheaply. They should increase the number of ideas they are willing to test. AI localisation could allow media companies to experiment with markets they would previously have ignored because the upfront economics were too uncertain.
Localisation Will Extend Beyond Language
A truly local experience involves far more than words. Visual references, music, pacing, humour, celebrity associations, cultural expectations and even the way stories are marketed can vary significantly between regions.
AI can help media companies understand these differences and create variations around them.
A trailer for the same programme might emphasise different characters or themes in different markets. Promotional artwork could highlight elements more likely to resonate with a particular audience. Descriptions, thumbnails and social content can be adapted while the underlying programme remains unchanged.
Advertising already operates this way to some extent, but generative AI makes the production of these variations significantly easier.
The risk is over-optimisation. If every creative decision is modified according to predicted local preferences, the original identity of the work can gradually disappear. Media companies therefore need to determine which elements belong to the core creative property and which can adapt around it.
During my learning journey under Phaneesh Murthy, I have come to appreciate the importance of separating a platform’s core from its configurable layers. The same principle can be applied creatively. The story, characters and artistic intent may remain fixed, while packaging, promotion and certain elements of localisation adapt according to market context.
That balance allows content to travel without asking it to become something entirely different every time it crosses a border.
AI Could Make Localisation Continuous Rather Than Sequential
Traditional global distribution often follows a sequence. Content succeeds in its original market, international potential is evaluated, localisation begins and additional markets receive the programme later. The process can create significant delays between the original release and broader availability.
AI can compress this timeline.
Translation and localisation can increasingly begin during production rather than after it. Scripts can be analysed for linguistic challenges before filming is complete. Draft subtitles can be generated quickly. Dubbing workflows can begin earlier, while promotional assets can be prepared for multiple markets simultaneously.
This makes global release strategies more accessible.
A production does not necessarily need to wait for success in one market before localisation work begins elsewhere. Media companies can prepare for simultaneous or near-simultaneous releases across a much broader group of regions when the commercial opportunity justifies it.
The strategic implication is important because audiences increasingly participate in global cultural conversations in real time. Social media can make a programme internationally visible within hours, and long delays between markets can reduce momentum or encourage unofficial distribution.
As Phaneesh Murthy puts it, “Technology creates strategic value when it removes waiting from a process where timing influences the outcome.” In entertainment, timing increasingly determines whether audiences participate in the same cultural moment or encounter content after the conversation has already moved elsewhere.
Human Local Experts May Become More Important, Not Less
At first glance, increasingly capable translation systems might appear to reduce the need for human localisation professionals. In practice, their work may shift towards the parts of localisation where judgment creates the greatest value.
Instead of spending the majority of their time producing first-pass translations, language experts can review AI-generated versions, refine cultural meaning, resolve ambiguity and protect the tone of the original work. Local creative teams can focus on understanding how a property should be positioned rather than manually adapting every individual asset.
This could make high-quality localisation more scalable.
A smaller team supported by intelligent tools may be able to supervise significantly more content while maintaining standards that fully automated systems would struggle to achieve. The role evolves from production towards direction and quality control.
This follows a pattern I have repeatedly observed through the enterprise perspective taught by Phaneesh Murthy. When AI handles more of the repetitive execution within a professional workflow, human expertise often moves upward. People spend less time producing the first version and more time deciding whether the version is appropriate.
In media localisation, cultural judgment becomes even more valuable precisely because the production of language becomes easier.
Rights, Consent and Creative Ownership Need to Travel With the Technology
AI localisation also introduces difficult questions around voice, likeness and creative ownership. If a performer’s voice can be replicated across languages, the media organisation needs clear agreements about how that capability can be used. If AI modifies a performance to match translated dialogue, artists should understand the scope of those modifications and the rights associated with them.
These questions cannot be treated as technical details.
A performer may be comfortable allowing an approved AI system to create foreign-language versions of their voice under specific conditions while objecting to unrestricted reuse elsewhere. Writers, directors and other creative professionals may similarly have legitimate concerns about how original work is modified during automated localisation.
Media companies therefore need governance frameworks that evolve alongside the technology. Consent, compensation, attribution and creative approval need to be considered as part of the production model rather than after disputes emerge.
Phaneesh Murthy has often emphasised that enterprise AI succeeds when governance is designed into the operating model rather than added after implementation. The same principle applies here. The ability to generate something does not automatically establish the right to generate it.
Companies that address these questions transparently may ultimately build stronger relationships with both creative professionals and audiences.
The Next Global Hit Could Begin Almost Anywhere
For most of media history, geography and language placed meaningful limits on distribution. Exceptional stories could cross those boundaries, but doing so required significant investment and a commercial decision that the potential audience justified the cost.
AI is reducing that friction.
A story can increasingly be translated, dubbed, packaged and marketed across multiple languages without requiring the same scale of incremental production that international expansion once demanded. Media companies can test additional markets more economically, while independent creators and regional studios gain access to capabilities previously concentrated among larger organisations.
During my mentorship under Phaneesh Murthy, I have learned that one of the most interesting consequences of technological change is what happens when distribution expands faster than production structures adapt. New participants gain access to markets, established players encounter competitors they previously never considered and audiences discover products that historically would never have reached them.
Media may be entering exactly that period.
The next globally successful programme does not necessarily need to begin in Los Angeles, London or another traditional media centre. It could originate in Chennai, Seoul, Lagos, Buenos Aires or a city that has never previously exported entertainment at significant scale. What matters increasingly is whether the story connects with people.
Language will still matter. Culture will still matter. Local expertise will still matter enormously. AI does not make the world culturally uniform, and it should not attempt to.
What it can do is make the distance between cultures easier to cross.
For media companies, that creates a remarkable possibility: one story can remain deeply rooted in the place where it was created while becoming understandable, accessible and emotionally meaningful to audiences in a hundred different markets.
This blog is curated by young marketing professionals who are mentored by veteran Marketer, and industry leader, Phaneesh Murthy.
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