As content clutter intensifies and organic discovery of fresh titles becomes harder, consumers are increasingly turning to AI for help.
According to Gracenote, the content data business unit of Nielsen, adoption of AI-powered entertainment experiences is rising—especially among older Gen Alpha respondents (ages 13 and 14). The shift is already reshaping how this cohort discovers content.
When asked to name the best source for TV and movie recommendations, 49% of Gen Alpha chose web- and app-based AI chatbots, ahead of streaming and cable service interfaces and program guides (41%) and internet search engine results (11%).
The implication is clear: discovery is moving from browsing to prompting.
As a result, creators and platforms are experimenting with multiple entry points. Short-form clips, behind-the-scenes moments and character-led snippets are increasingly designed as discovery triggers. These fragments travel across platforms and sometimes become the reference points AI systems pick up.
The strategy is no longer just to create a show or film, but to build an ecosystem around it—maximizing the chances of being surfaced in different AI-driven contexts.
That said, questions of trust and accuracy remain.
Smarter recommendations
“AI is able to interpret content at a much deeper level. It goes beyond basic metadata to understand tone, themes, character arcs and viewing context. At the same time, it is continuously learning from user behaviour, what people watch, skip, rate or return to, which makes recommendations sharper and better over time,” said Bharath Ram, chief product officer, JioHotstar.
According to Harikrishnan Pillai, CEO and co-founder of digital marketing agency TheSmallBigIdea, AI-powered chatbots are increasingly replacing traditional search for entertainment discovery.
“What's interesting is the kind of audience driving this. There are essentially two types of entertainment viewers. One watches what's current, they don't need discovery; they need information. But the second type is looking for something more specific: the best thrillers, Oscar-winning English cinema, the finest Malayalam films, without necessarily knowing titles, platforms, or availability. They know the kind of content they want. That's where AI search becomes genuinely powerful,” Pillai pointed out.
A generational split
Charu Malhotra, co-founder and managing director, Primus Partners, a management consultancy firm, said that while among younger users—Gen Z and early Gen Alpha—AI is becoming a discovery layer, slightly older users are deploying it more functionally. They use AI to shortlist options, check reviews or get quick summaries before deciding what to watch.
With an explosion of content across OTT platforms, AI becomes a filter, agreed Siddharth Devnani, co-founder and chief operating officer of digital agency SoCheers.
Add to that the rise of voice queries in tier-two and tier-three cities, and discovery shifts from scrolling feeds to directly asking questions—fundamentally altering who and what controls attention.
“Earlier, we used to optimize for algorithms, but now we are optimising for answers, which means restructuring descriptions, press notes and the metadata to be something that AI can easily interpret. OTT platforms are investing in sharper tagging and conversational hooks. It is more like, ‘I liked x, what should I watch next?’” Devnani said.
Packaging for prompts
With shortening attention spans, especially among younger viewers, creators are already building hooks within the first few seconds of content.
Anuja Trivedi, chief strategy and marketing officer, Shemaroo Entertainment Ltd, said the same thinking now extends to how content is described and packaged. Titles, descriptions and metadata are becoming more conversational—sometimes mirroring the slang and phrasing users employ while prompting AI.
Content that is widely discussed, well-described and contextually tagged has a higher chance of surfacing when someone asks AI for recommendations, she added.
The trust gap
However, AI systems are only as reliable as the data they are trained on.
Neelesh Pednekar, co-founder and head of digital media at Social Pill, said AI may recommend outdated content, incorrect titles or mix up genres because it doesn’t always have real-time or platform-specific data. Popular shows may be disproportionately recommended, while niche or regional content remains underrepresented unless explicitly prompted.
For regional platforms, the problem is sharper.
Ujjwal Mahajan, co-founder of Chaupal, said AI models frequently hallucinate details about smaller films—wrong cast, wrong release year or incorrect platform attribution. Hindi-Bollywood and international content enjoy far larger digital footprints than Punjabi and other regional content. As a result, AI systems systematically under-represent regional titles—not due to bias, but because of limited data availability, Mahajan explained.
“Users are engaging with AI, but they are also very quick to disengage if the experience is not accurate. One clear pattern we’ve seen is that drop offs tend to increase when recommendations feel repetitive or slightly misaligned with the user’s immediate query. Even a small mismatch impacts trust,” said Sunnyraj Agarwal, founder and CEO, Chat360, an omnichannel customer engagement platform powered by Agentic AI.
“So, the focus now is shifting towards tighter data control, real time updates, and feedback loops. Because at this stage, it’s not about how intelligent the system sounds, it’s about how consistently accurate it is,” he added.