Have you ever come home after a long day at work, opened Netflix while eating dinner, and thought: What should I watch?
You scroll. Then scroll some more.
You check the recommendations. You look at the trending section. You watch a trailer or two. You read the description of a show. Then another.
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Twenty minutes disappear. And finally, you give up.
You go back to the same comfort sitcom you have already watched 10 times.
It sounds funny. But it points to a problem that is becoming increasingly common in the streaming age.
We do not have too little entertainment. We have too much of it.
There are OTT platforms, live television channels, YouTube, short-video platforms and enormous content libraries. There are movies, series, documentaries, sports, reality shows, regional content and thousands of older titles sitting somewhere in a digital catalogue.
The paradox is simple. The more content we get, the harder it can become to decide what to watch.
And this is where artificial intelligence could change the way we discover entertainment.
The Problem Is No Longer Availability
Search engines and conventional OTT search work reasonably well when you know what you are looking for. You can type the name of a movie, an actor or perhaps a genre.
But what if you do not know? What if it is 9 pm on a Tuesday and you simply want something funny, light and easy to watch after a tiring day?
That is not really a keyword. It is a mood.
Jitin Thapar, Creator of the EPICC Reinvention “Inside-Out” Philosophy and Global Strategic Advisor, told NDTV that this is where the next big shift in entertainment discovery will happen. “We don’t have a content problem anymore. We have a relevance problem. There is more to watch than at any point in history, and yet people spend twenty minutes scrolling and switch the TV off in a worse mood than they started in,” Thapar said.
He points to Deloitte’s 2025 Digital Media Trends study, which found that 56 per cent of Gen Z and 43 per cent of millennials in the US said social media content felt more relevant to them than traditional TV shows and movies.
For Thapar, that is less about the quality of traditional entertainment and more about the way people discover it.
From Keywords To Intent
Traditional search asks you to describe the content you want. AI can potentially ask a different question: What are you trying to do right now?
“The reason search fails us is that search is index-based. You already have to know the title, the actor, the genre. AI is intent-based,” Thapar said.
That could mean telling your television: “I have had a terrible day. Give me something funny, under 30 minutes, that I don’t have to concentrate too hard on.”
Or: “Find me a thriller for tonight, but nothing too dark.”
Or perhaps: “Something my eight-year-old and I can watch together.”
“The future of discovery isn’t about helping people search a bigger library. It’s about making the library understand the person,” Thapar said.
Today, the viewer enters the catalogue. Tomorrow, the catalogue could start understanding the viewer.
Your Recommendations Could Become More Personal
Streaming platforms have already spent years trying to personalise recommendations. But AI could make that personalisation more dynamic. Instead of simply learning that someone watches comedy or crime dramas, an AI system could potentially understand more subtle patterns.
- Maybe the person watches thrillers on weekends but prefers sitcoms on workdays.
- Maybe they watch Hindi content at home but English shows while travelling.
- Maybe they prefer short episodes when they are tired and longer movies on Friday nights.
The recommendation is no longer based only on what you watched. It starts considering why you might want to watch something now.
Thapar points to examples of this broader movement. “Netflix personalises not just what you’re shown but how it’s shown, down to the artwork on a title. Spotify’s Taste Profile beta goes a step further by letting listeners tell the system directly what they’re in the mood for, alongside their history,” he said.
India’s Content Problem Is Even Bigger
This challenge becomes more complicated in India. The country does not have one entertainment audience. It has hundreds of them.
People watch content in Hindi, English, Tamil, Telugu, Bengali, Marathi, Malayalam, Kannada and several other languages. Viewing habits can change dramatically between households, age groups and cities.
And the number of viewers is enormous. Abhishek Thomas, Marketing Manager at Airtel Digital, told NDTV that India’s content discovery challenge is becoming less about availability and more about relevance. “With 664.9 million Indians now consuming online video and the CTV audience reaching 206.9 million, the scale of choice is growing rapidly,” Thomas said.
That scale makes traditional recommendations more difficult. “In a market as diverse as India-with multiple languages, genres, platforms and viewing habits- traditional recommendations can only go so far,” he added.
AI could potentially make those recommendations more contextual. It could understand the language a person wants, the type of content they prefer, the device they are using and even the situation in which they are watching.
“AI can make discovery far more contextual by understanding what a consumer wants to watch, in which language, on which screen and at that particular moment,” Thomas said.
The TV May Become More Than A Screen
The next step could be bigger than smarter recommendations. It could be about bringing fragmented entertainment into one experience.
Think about the current setup. You have one app for a particular OTT service. Another for live TV. Another for YouTube. Another for sports. Another for music.
The content may all be available through the same television, but the experience remains fragmented. Connected TVs and integrated entertainment platforms could change that.
Instead of asking which app has something to watch, viewers could simply ask the TV: “Find me a family movie.”
The system could search across available services and present relevant options. This could turn the television from a collection of apps into something closer to a personal entertainment assistant. And the personalisation could extend beyond content.
Thapar believes the connected home could eventually respond to the viewer’s intent as well. “One voice instruction, and the lighting, the sound and the screen respond together. At that point the environment matches the mood, not just the playlist,” he said.
In other words, AI may not simply decide what appears on the screen. It could help create the entire viewing environment.
There Is Another Audience That Needs Smarter Discovery
Children are another important part of this conversation. For kids, more choice does not necessarily mean a better experience. It can mean more confusion.
Sanvee Jakhwal, a kid content influencer, said the sheer number of choices can make finding something suitable difficult. “I often feel like there are just too many choices out there. With various OTT platforms, TV channels, video content libraries, the time spent in searching becomes even greater than in watching the actual content,” Jakhwal said.
AI could potentially make that experience more personalised. But there is an important difference when the viewer is a child. The recommendation cannot be based only on entertainment value. It also needs to consider age suitability and parental preferences.
Jakhwal said AI could help parents create restrictions, filter inappropriate content and establish age-appropriate viewing preferences. “Kids will be able to enjoy exploring the entertainment while parents will be able to monitor their content consumption,” Jakhwal said.
But Will AI Really Solve The Scrolling Problem?
Not necessarily. AI can make discovery easier, but it cannot eliminate the fundamental problem of choice. In fact, better technology could sometimes create even more personalised recommendation loops.
There is also a question of how much viewers want machines to know about them. The more contextual the recommendations become, the more information an entertainment system may need to understand behaviour, preferences and habits.
The technology will also have to avoid turning personalisation into a narrow bubble where viewers repeatedly see the same type of content. After all, discovery is not only about finding what we already like. Sometimes the joy of entertainment comes from finding something we did not know we wanted.
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