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How Indian Media Brands Are Using AI Video Tools to Scale Content？
India’s content economy is running 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 platform-specific video creative at volumes that would have been unthinkable five years ago, the pressure on media houses, agencies, and brand content teams has reached a structural breaking point. The response from forward-thinking teams isn’t to hire faster — it’s to rethink the production pipeline entirely, with AI video tools at the center of that rethink.
The Production Gap India’s Media Industry Can No Longer Ignore
The numbers tell the story clearly. Indian digital advertising crossed Rs 60,000 crore in 2024, with video commanding an increasingly dominant share of that spend. Each rupee of video ad spend requires creative assets — and increasingly, it requires multiple versions of those assets: different aspect ratios for different platforms, different language edits for regional audiences, different cuts for different stages of the funnel. A single campaign brief that once produced three or four video assets now needs to produce thirty.
Traditional production infrastructure was not built for this reality. Studio time, crew costs, post-production timelines, and talent fees compound quickly when volume scales. The result is a familiar compromise: fewer assets than the brief demands, creative that gets repurposed beyond its natural lifespan, and campaign performance that suffers for it.
AI video generation has entered this gap not as a novelty but as a genuine operational solution. The Kling AI video generator accessible through Pollo AI represents the kind of capability that media production teams and agencies are now evaluating seriously. Developed by Kuaishou Technology — one of the world’s largest short-video platforms by volume — Kling brings industrial-scale video generation expertise to a creator-accessible interface. Pollo AI makes it available to Indian media teams without region-specific access barriers, within a platform built around professional content workflows. For brand managers and agency producers benchmarking AI video tools, Kling’s particular strength in fluid human motion and cinematic output quality puts it in a different tier from earlier-generation tools.
Where AI Video Generation Fits the Indian Production Context
The use cases that make the strongest business case for Indian media and advertising teams are not the ones that replace core production — they are the ones that extend it.
Pre-visualisation and pitch creative is the most immediately valuable application. Before a single shooting day is scheduled, AI-generated video can bring a script or storyboard to life well enough for client approval. The approval cycle shortens, revisions happen at the concept stage rather than in post, and clients arrive at the production phase with aligned expectations. For agencies managing complex client relationships across categories, this alone justifies the investment.
Social and digital content at scale 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 are simply not viable with traditional production for most brands. AI generation closes that gap, particularly for content types where the brief is clear, the visual requirements are well-defined, and the priority is speed and consistency rather than craft differentiation.
Regional and linguistic versioning is an area where India’s content market has always demanded more than the industry could supply efficiently. Generating visual content that reflects regional contexts — locations, aesthetics, casting sensibilities — at the volume that a pan-India brand campaign requires has historically meant either a large production budget or an acceptance of generic national creative that underperforms in regional markets. AI generation makes market-specific visual content economically viable at a scale it never was before.
B-roll and supplementary footage for news, documentary, and factual programming is another practical application that production houses are exploring. When footage of a location, an event, or a scenario is unavailable or impractical to shoot, AI-generated establishing shots and atmospheric footage can fill gaps that would otherwise require stock footage compromises or reshoot budgets.
The Explainer and Educational Content Layer
Not all video content in the media and advertising ecosystem is narrative or cinematic. A significant portion of the content Indian media brands produce — training materials, product explainers, investor presentations, internal communications, educational series — calls for a visual language that communicates clarity and structure rather than emotional storytelling.
This is where the distinction between different AI video tools matters practically. Cinematic AI video generation serves the brand storytelling and social content use cases well. Structured, animated explainer content requires a different approach — one where the progressive visual explanation of a concept, a process, or a system is the primary communication job.
Videoscribe, also accessible through Pollo AI, is designed specifically for this content category. Its whiteboard animation format — where illustrated concepts build on screen in sync with narration — has become the standard visual language for educational and explainer content globally, and Indian media brands producing content in the finance, healthcare, FMCG, and education verticals use this format extensively. For a media brand managing content production across both high-impact brand video and structured educational or product content, having both Kling and Videoscribe accessible through Pollo AI’s ecosystem means the right tool is available for each content type without managing separate vendor relationships.
Practical Considerations for Indian Media Teams Evaluating AI Video
The evaluation framework for AI video tools looks different for a media house or agency than it does for an individual creator. A few considerations that matter at the professional level:
Output consistency across a campaign is a non-negotiable for brand 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 is usable as a campaign system rather than a collection of one-offs. This is an area where prompt discipline and tool selection together determine the outcome.
Turnaround speed relative to revision cycles matters more than raw generation speed. A tool that generates quickly but requires many iterations to produce an approvable output may be slower in practice than a tool with slightly longer generation times but higher first-pass approval rates. Testing against actual brief types — rather than benchmark prompts — gives a more accurate picture of where a tool fits in a real workflow.
Integration with existing post-production pipelines is a practical consideration that often gets overlooked in tool evaluation. AI-generated footage needs to move into editing timelines, color grading workflows, and delivery pipelines. Export format flexibility and file quality specifications determine how much additional work the integration creates.
Intellectual property and usage rights remain an area where Indian media legal teams are rightly cautious. The contractual and regulatory landscape around AI-generated content in advertising and broadcast contexts is still developing in India, and production teams should understand the usage terms of any AI video platform before committing to it for client-facing deliverables.
The Structural Shift Underway
The Indian media industry’s relationship with AI video is moving through a recognizable adoption curve. Early experimentation — often driven by individual producers or digital-native brands with a higher appetite for new tools — is giving way to structured evaluation by larger media houses and agency networks. The question has shifted from “can this produce good output” to “how do we build this into our production infrastructure responsibly and at scale.”
That shift reflects a broader maturation of the tools themselves. The output quality of leading AI video generators has improved to the point where the conversation is no longer about whether the technology is ready — it’s about workflow integration, team training, and the creative and strategic frameworks needed to use it well.
For Indian media brands navigating this transition, the competitive implication is straightforward: teams that build AI video capability into their production infrastructure now will have a cost and speed advantage that compounds over time. The volume and variety of content the market demands is not going to decrease. The teams that figure out how to meet that demand efficiently — without compromising on the quality that brand relationships require — are the ones that will be best positioned as the industry’s next phase of growth unfolds.
