India’s content economy moves at a pace that production budgets simply cannot match. With over 500 million active social media users, a rapidly maturing OTT landscape, and advertisers demanding volumes of platform-specific video creative that were unimaginable five years ago, the pressure on media houses, agencies, and branded content teams is reaching structural breaking point. The response for forward-thinking teams is not to rush into adoption, but to completely rethink their production pipelines around AI video tools.
Production gap in Indian media industry can no longer be ignored
The numbers clearly tell the story. Digital advertising in India will cross Rs 60,000 billion in 2024, with video becoming an increasingly dominant share of that spend. Every rupee of video ad spend requires a creative asset. And increasingly, multiple versions of those assets are required, with different aspect ratios for different platforms, different language edits for different regional audiences, and different cuts at different stages of the funnel. One campaign brief that used to create 3-4 video assets now requires creating 30 video assets.
Traditional production infrastructure was not built with this reality in mind. Studio time, staff costs, post-production timelines, and talent fees increase rapidly as volume increases. The result is a common compromise. Short-term requests result in fewer assets and creatives being reused beyond their original lifespan, which reduces campaign performance.
AI video generation has entered this gap not as a novelty but as a true operational solution. The Kling AI video generator, accessible through Polo AI, represents a feature that media production teams and agencies are now seriously evaluating. Developed by Kuaishou Technology (one of the world’s largest short video platforms by volume), Kling brings industrial-scale video generation expertise to an interface that creators can access. Polo AI is available to media teams in India without region-specific access barriers, within a platform built around professional content workflows. For brand managers and agency producers looking to benchmark AI video tools, Kling’s particular strengths – human-smooth motion and cinematic output quality – place it on a different tier than previous generation tools.
Where AI video generation fits into the Indian production environment
The use cases that represent the strongest business case for media and advertising teams in India are those that extend core production, rather than replacing it.
Pre-visualization and creative suggestions is an immediately valuable application. Before a single shoot date is scheduled, AI-generated video can help you achieve a script or storyboard that is good enough for client approval. Approval cycles are shortened, revisions are made at the concept stage rather than after the fact, and clients reach production with aligned expectations. For agencies managing complex customer relationships across categories, this alone justifies the investment.
Social and digital content at scale This is the second major application. The production economics of creating platform-native video content (reels, shorts, vertical cuts, square formats) at the volume that performance marketing now demands is simply not possible with traditional production for most brands. AI generation fills that gap, especially for content types that have a clear outline, well-defined visual requirements, and where speed and consistency are prioritized over technical differentiation.
Regional and language versioning The Indian content market has always been an area that has demanded more than the industry can efficiently supply. Generating visual content that reflects the local context (location, aesthetic, casting sensibilities) in the volumes required for pan-India brand campaigns has traditionally meant accepting generic domestic creative that either requires large production budgets or underperforms in regional markets. AI generation makes market-specific visual content economically viable at an unprecedented scale.
B-roll and supplementary footage Applications to news, documentaries, and factual programming are also practical applications that production companies are exploring. When footage of a location, event, or scenario is unavailable or impractical to shoot, AI-generated setting shots and mood footage can fill in the gaps that would otherwise require compromising stock footage or budget reshoots.
Explainer and educational content layer
Not all video content within the media and advertising ecosystem is narrative or cinematic. A significant portion of the content produced by Indian media brands – training materials, product descriptions, investor presentations, internal communications, educational series – requires a visual language that conveys clarity and structure, rather than emotional storytelling.
This is where the distinction between different AI video tools becomes of practical importance. Cinematic AI video generation is well-suited for brand storytelling and social content use cases. Structured animated explanatory content requires a different approach. This means that progressive visual explanations of concepts, processes, and systems are the primary communication role.
Videoscribe, which is also accessible through Polo AI, is designed specifically for this content category. Its whiteboard animation format (illustrated concepts are built on screen in sync with narration) has become the standard visual language for educational and explanatory content globally, and it is widely used by Indian media brands producing content in the finance, healthcare, FMCG, and education industries. For media brands managing content production across both high-impact branded videos and structured educational and product content, having access to both Kling and Videoscribe through Pollo AI’s ecosystem means they have the right tools for each content type without having to manage separate vendor relationships.
Practical considerations for Indian media teams to evaluate AI video
The evaluation framework for AI video tools appears to be different for media houses and agencies than for individual creators. There are some important considerations at the professional level.
Consistency of output across campaigns This is non-negotiable for branded work. The ability to generate multiple assets from the same campaign brief with visual consistency (consistent color treatment, consistent character appearance, consistent aesthetic register) determines whether AI-generated content can be used as a campaign system rather than a one-off collection. This is an area where quick discipline and tool selection will determine the outcome.
Turnaround speed for revision cycles It’s more important than raw generation speed. A tool that generates quickly but requires many iterations to produce acceptable output may actually be slower than a tool that takes slightly longer to generate but has a higher first-pass acceptance rate. By testing against actual brief types rather than benchmark prompts, you can get a more accurate picture of where the tool fits into your actual workflow.
Integration with existing post-production pipelines This is a practical consideration that is often overlooked when evaluating tools. AI-generated footage must move into editing timelines, color grading workflows, and delivery pipelines. Export format flexibility and file quality specifications will determine how much additional work the integration will cause.
Intellectual property and usage rights This remains an understandably sensitive area for media legal teams in India. In India, the contractual and regulatory landscape for AI-generated content in the advertising and broadcasting context is still evolving, and production teams need to understand the terms of use of AI video platforms before leveraging them for client-facing deliverables.
Structural transformation underway
The relationship between the Indian media industry and AI video is moving along a clear adoption curve. Early experimentation, often driven by independent producers and digitally native brands with a strong appetite for new tools, is being replaced by structured evaluations by large media houses and agency networks. The question has moved from “Will this produce good results?” to “How can we responsibly incorporate this into our production infrastructure at scale?”
This change reflects a broader maturation of the tool itself. The output quality of leading AI video generators has improved to the point where the debate is no longer about whether the technology is ready, but about workflow integration, team training, and the creative and strategic framework needed to successfully use it.
For Indian media brands making this transition, the competitive implications are clear. Teams that incorporate AI video capabilities into their production infrastructure will realize cost and speed benefits over time. The amount and variety of content the market demands will continue to grow. Teams that figure out how to efficiently meet that demand without compromising the quality needed for brand relationships will be best positioned as the industry’s next phase of growth unfolds.
