Loud, bright, messy, and utterly alive, Bollywood has always been a place where technology and storytelling meet in dramatic ways. Tune in to any mainstream film and you will find songs that stay in your head, set pieces that bend reality, and stars whose faces become larger than the screens they are on. Now add artificial intelligence into that mix, and you get something that looks equal parts thrilling and worrying. AI is already changing how films are written, how actors appear on screen, and how music is made. For anyone interested in the future of Indian cinema, this is not an optional conversation. It is the conversation.
Opening scene: where AI already sits in the filmmaking pipeline
Talk to filmmakers, VFX houses, music producers, or studio executives and you will find AI in their workflow in small but meaningful ways. Some of it is invisible, like machine learning models that make color grading and noise reduction faster. Some of it is obvious, like AI tools that suggest casting choices, run a quick script analysis, or generate a first-draft melody.
Two clear strands show up across most conversations. The first is augmentation, where AI speeds up mundane or repetitive work so people can focus on craft. The second is generation, where AI writes drafts, creates visuals, or composes music. Both matter, but they raise different questions. Augmentation is a familiar productivity story. Generation touches the core of what we mean by creative authorship. 
On the business and decision side, companies such as Cinelytic and ScriptBook have been selling the idea that data and machine learning can take some of the guesswork out of greenlighting projects and packaging talent. These tools analyze past box office patterns, star combinations, and script features to estimate commercial potential. Studios in the West have used platforms like these to inform decisions for a while, and the technology is now appearing in conversations in India as well. The pitch is simple: reduce financial risk by supplementing gut judgment with numbers. For producers operating with tight budgets, that promise is appealing.
Scripts: can AI write a Bollywood hit?
The idea that AI can write a movie is already a cliché in tech circles, but the reality is more modest and more interesting. AI is good at producing first drafts and at analyzing patterns. It does not, at present, truly understand emotion or lived cultural nuance the way a human writer does. What it does well is surface arcs, suggest beats, and generate dialogue that can then be shaped by human writers.
There are startups offering script analysis that claim high predictive accuracy. ScriptBook, for instance, analyzes screenplays using natural language processing to predict commercial and critical outcomes. Other tools can produce loglines, character sketches, or scene outlines in seconds. Those outputs can save time during development or provide fresh directions when a writer hits a blank wall. The most productive use case seen is pairing AI with an experienced writer, where the machine handles structure and data, and the writer brings emotion, cultural specificity, and voice.
There are limits. The unique cadence of Hindi-Urdu dialogue, the cultural weight of particular family dynamics, the way humor lands in Mumbai versus Lucknow, these are not things an off-the-shelf model will reliably capture. That means Bollywood content creators who want AI to help will often need to fine-tune models on local language data and on regional storytelling traditions. When that happens, the outputs are better, but so are the stakes, because the training data can include copyrighted scripts or copyrighted performances. That brings us to the legal debates later in this piece.
Casting, packaging, and the economics of talent
Casting is more than a list of names. It is an economic decision, a marketing problem, and a creative judgment. AI platforms can analyze combinations of talent, past performance, and demographic appeal to estimate the likely commercial payoff of different cast pairings. Cinelytic, a company that markets predictive analytics to studios and distributors, offers tools that do precisely that. The value proposition for producers is that you can run dozens of simulations quickly and surface combinations that maximize box office or streaming value. This is especially relevant in a market like India, where star power still often moves audience numbers.
There is a human cost question here. If casting decisions become increasingly data driven, will producers prefer safer, algorithmically optimal pairings over risk-taking choices that could yield fresh work but carry commercial uncertainty? That is a tension the industry will need to negotiate. For now, AI is a decision support tool, not a decision maker. But as platforms become more integrated, the temptation to rely on their recommendations will grow.
Visual effects and de-aging: lessons from Hollywood
Visual effects is where AI shows immediate return on investment. Tasks such as rotoscoping, background cleanup, noise reduction, and even rotoscope-to-3D automation have been sped up considerably by machine learning systems. The result is that smaller teams can produce higher quality visuals at lower cost.
One very visible technology is digital de-aging. Hollywood films such as The Irishman used a blend of traditional VFX and markerless performance techniques to make older actors look younger for certain scenes. While the methods used there predate the explosion of generative AI, later iterations use neural networks and face synthesis engines to further refine the effect. This technology opens new creative possibilities, such as telling long-form stories that follow the same actor across decades, and it also introduces ethical questions about consent and legacy.
In India, deepfakes and AI-manipulated videos became a legal and reputational problem in 2025 when a Reuters investigation showed hundreds of AI-generated Bollywood-themed videos on YouTube that used lookalikes or synthetic likenesses in fabricated scenarios. Many of those videos were removed after the investigation, and the episodes sparked lawsuits from major actors seeking to protect their image and personality rights. That case illustrates how fast a technical capability can move from novelty to crisis when it collides with real people’s reputations and livelihoods.
Soundtracks: AI as collaborator and troublemaker
Music is central to Bollywood, and AI’s incursion into music production is one of the most sensitive areas. AI tools can compose melodies, suggest chord progressions, generate background textures, and even mimic vocal timbres. Platforms such as AIVA or OpenAI’s Jukebox have demonstrated the ability to produce stylistically convincing music in seconds. For composers and music directors, AI can be a creative assistant that quickly sketches ideas or generates arrangements that would have taken hours. That is a productivity gain.
The more uncomfortable use case is voice cloning. In 2025, a series of high-profile AI-generated songs that replicated the voices of veteran singers sparked intense debate in India. One widely reported example involved an AI-generated rendition that imitated the voice of the late singer Kishore Kumar, sparking debate over consent and copyright. The singer Shaan publicly called such recreations unfair and cruel. This kind of synthetic replication raises ethical and legal issues: should a company be allowed to generate songs that sound like a long-dead artist without the consent of their estate? How do we protect the moral and economic rights of performers? Those are live questions in Indian courts and public discourse.
From a practical perspective, music labels and rights holders have responded forcefully. Major Bollywood music labels including T-Series, Saregama, and Sony have sought to join copyright lawsuits against AI companies in India, alleging that copyrighted sound recordings were used to train large language models and music models without permission. The results of these cases will affect how generative music tools operate, and whether permissioned data becomes the default.
Hollywood parallels and what they teach us
It is tempting to treat Hollywood as an experiment we can learn from, and that is not entirely wrong. Hollywood invested heavily in VFX and de-aging early on, and studios experimented with data-driven greenlighting. While some techniques are being discussed in India, full adoption is still in early stages due to cultural and market differences. Bollywood produces many more films per year, and music and star culture have a different kind of centrality in India than they do in the United States. That means an AI practice that is acceptable in one context could be more explosive in another.
Hollywood’s experiences show two things. First, there is real creative value in using AI to augment workflows. Second, a technology that looks like a creative shortcut can become a legal problem when it uses copyrighted content without clearance. We have already seen multiple legal actions around image and voice synthesis globally. These cases will shape rules that Indian filmmakers will need to follow or challenge.
The legal and ethical beat: who owns creativity?
Culture and law do not move at the same pace as technology. In many jurisdictions, including India, personality rights, image rights, and the legal frameworks around AI training data are still being tested. Bollywood actors filing suit over AI-generated videos highlight a gap in explicit personality protection in Indian law. Companies and creators will need to navigate contracts, consent, and derivative works more carefully.
On the copyright front, the suits brought or proposed by music labels against AI firms in India are crucial. If courts ultimately determine that training models on copyrighted recordings without permission violates law, the economics of AI music could shift. Models may need to be trained on licensed data, or companies will face limits in how closely their output can mimic living or deceased artists. Those legal outcomes will directly affect music producers and independent creators, who may increasingly rely on licensed sample libraries and AI tools that guarantee clearance.
Ethics is not only about legality. It is about respect for legacy and creative labor. When an AI-generated song that imitates a beloved singer becomes popular, it can feel like an erasure of the original artist’s work and the people who made that performance possible. That is why many musicians and industry veterans argue for guardrails that protect artists’ moral rights and for clearer consent frameworks for using someone’s voice or likeness.
Jobs, craft, and the future of work in film
There is a real fear that AI will replace jobs, and that fear is not baseless. Tasks that were once entry level, like rotoscoping or audio clean-up, are being automated. Script readers who did grunt work to filter submissions might find preliminary filtering done by algorithms. Even composers who specialized in certain routine tasks could see parts of their workflow automated.
But history suggests a subtler pattern. New technologies often displace some tasks while creating opportunities elsewhere. VFX teams that used to spend weeks on menial tasks can now focus on more ambitious shots. Composers who embrace AI as a sketching tool can produce more material in less time and spend their energy on higher-order creative decisions. The catch is that artists and technicians will need to adapt to new tools and learn to collaborate with them. Organizations and unions will also need to negotiate fair terms, because augmentation can easily turn into exploitation if companies expect more work from fewer people for the same pay.
The audience and the experience of watching films
AI will not only change how films are made, it will change how they are consumed. Personalized trailers, dynamically mixed soundtracks, and recommendation engines that learn emotional preferences are all plausible near-term developments. Imagine a streaming platform that tailors the opening montage to the viewer’s past viewing history, or a film that offers alternate scenes depending on viewer choice. Those experiences raise questions about the public nature of culture. When a film becomes a private experience optimized for your tastes, what does that mean for shared cultural moments and watercooler conversations?
There are artistic possibilities too. Directors may create branching narratives that a human alone would find too complex to plan. Films might react to audience mood in real time. Those are exciting directions, but they will also challenge how critics, festivals, and awards systems evaluate art. New metrics of success will be necessary.
Concrete case studies and signals from India
We already referenced a few newsworthy events that are worth revisiting because they are emblematic. First, the legal push by major music labels to be part of copyright litigation against AI firms in India is a clear signal that the industry is not taking this technology lightly. This is not just a nuisance suit. It is about market structure and control over creative assets. The outcomes will create precedent for how AI firms must behave in India.
Second, the proliferation and subsequent removal of AI-generated Bollywood videos on YouTube after a Reuters investigation demonstrates how fast misuse can spread. When lookalikes and synthetic scenes show up in the wild, the harm is not merely reputational. It is commercial, psychological, and legal. Public figures and studios are right to be concerned about systems that can manufacture realistic but false content at scale.
Third, the blowback around AI-generated renditions of veteran voices, such as the Kishore Kumar imitation, shows that audiences and artists care deeply about authenticity. Popularity of an AI track does not translate into moral acceptability. This moral response matters because it will inform both market behavior and regulatory pressures.
Business models and the rise of permissioned datasets
One likely path forward is the growth of permissioned, curated datasets. If courts and regulators require explicit licensing for training models on music or performance, then companies will have to buy or license data. That will create a new economy where rights holders can be paid for the use of their content in training. It also opens a market for “synthesizable” artists who opt in to allow AI-generated content, creating a new revenue stream for estates and creators.
That model could mitigate some of the most obvious ethical problems, but it will also concentrate power in companies that control large catalogs of licensed content. Independent creators will need to weigh whether to enter such ecosystems or to use smaller, independent tools that give them more control.
Creative possibilities that feel like a promise
It is easy to focus on the risks and short-term disruptions. But there are inventive, humane applications that could broaden who gets to tell stories. For example, AI-assisted tools lower the technical barrier for independent filmmakers. A director with a strong visual idea but fewer resources can use AI-assisted VFX to realize ambitious sequences. A composer can sketch orchestral arrangements without hiring an orchestra for the first draft. Script analysis can help emerging writers see structural problems in a draft before they show it to a producer.
There are also archival uses. Imagine reconstructing a rare live performance in a way that helps preserve musical traditions when handled ethically and with the consent of communities. AI could help restore damaged film prints or remaster audio tracks in ways that are faithful to original performances. Those are uses where technology serves preservation and accessibility rather than replacement.
Regulation, industry standards, and what to watch next
The development of sensible guardrails will be decisive. Courts in India are already seeing suits about personality rights and copyright in the context of AI. The way those cases are decided will shape industry norms, and they should be watched closely by filmmakers, music directors, and platform operators.
Industry self-regulation can also play a role. Music labels, studios, and streaming services could adopt standards for how synthetic content is labeled and how consent is obtained for training data. Platforms hosting content need transparent policies and audit mechanisms that prevent the monetization of harmful or misleading synthetic material.
A practical short list of things for creators and companies to consider includes these items. First, build consent into contracts for voice and image rights, both for living artists and for estates. Second, require provenance labeling for synthetic content so audiences can tell what has been altered or generated. Third, invest in staff training so that creative teams can use AI responsibly. Fourth, explore licensing schemes for training data that compensate rights holders fairly.
A personal note on storytelling
Filmmaking at its best is a human conversation. Audiences connect to embodied performances, to imperfect gestures, to stumbles that feel honest. Technology will never fully replace that human warmth. What it can do is expand the palette available to filmmakers. The best outcomes will come from collaborations where a director’s intuition and an AI’s capacity for scale push each other to unexpected places.
Picture an old soundstage in Mumbai. On one side, a veteran composer goes through reels of cassette recordings, looking for a motif that can be modernized. On the other side, a young developer tunes a generative model to create textures that nod to the past without stealing it. They exchange ideas, argue about what is respectful and what is exploitative, and then they work together. What comes out is a song that feels both of the time and of history. That is the vision to aim for: collaboration that respects legacy, nourishes craft, and opens space for new voices.
Key takeaways
- AI is already being used across Bollywood to speed up VFX work, analyze scripts, and support casting and production decisions. Tools like Cinelytic and ScriptBook are examples of how analytics and NLP are informing film development.
- De-aging and synthetic visual techniques, seen in Hollywood films such as The Irishman, are technically feasible and artistically useful, but they raise consent and authenticity questions when applied to real people.
- AI-generated music and voice cloning are powerful, but they have provoked strong pushback in India when used to imitate beloved singers. The backlash and lawsuits from labels could reshape how AI models are trained and deployed.
- The industry will need a mix of legal clarity, consent frameworks, and technical best practices. Permissioned datasets and licensing models are likely to become important if courts require compensation for training data.
- For creatives, the best approach is to treat AI as a collaborator. Those who learn to use these tools can gain an advantage, while those who ignore them risk losing ground. But technological adaptation must be accompanied by ethical reflection and fair labor practices.
