Personalization and AI in Streaming Platforms

Personalization isn’t a new concept in media, but the tools and expectations behind it have evolved dramatically in recent years. Personal audience relevance is more important than ever, and streaming leaders are blending foundational machine learning with generative AI to make content discovery faster and more meaningful.

It’s both a gradual and deliberate evolution. For a long time, streaming platforms prioritized stability and scale over experimentation. AI-powered recommendations were already deeply embedded in back-end systems, yet customer-facing features lagged behind — a conscious choice. In an industry where user engagement is everything, rolling out unproven AI features — especially those prone to inaccuracy or bias — carried real risk. Several well-publicized missteps with chatbots and auto-generated content made some companies justifiably cautious.

It’s time to change the channel. A new wave of personalization is moving from theory to execution, powered by AI systems that are more capable and ambitious than ever. As generative AI matures and competitive pressure intensifies, streaming platforms are beginning to reimagine how personalization is built and delivered. And AI is no longer the monster under the bed.

Smarter recommendations through hybrid models

Attitudes are changing. According to recent projections, the global AI in media and entertainment market is expected to reach $51.37 billion by 2030. The anticipated compound annual growth rate of that market (17.5%) is a sign that these investments are long-term strategic plays, not flash-in-the-pan trends.

One of the more notable developments is how platforms are beginning to layer generative AI onto their existing personalization infrastructures. The accuracy and relevance of content recommendations can make or break a viewer’s decision to stay engaged or churn, and AI will play an increasing role in those features.

This trend also aligns with broader research around AI’s role in reshaping media infrastructure, including how intelligent systems are being embedded into workflows to streamline production, enhance distribution and improve user engagement.

Take Netflix for example. The company recently implemented a hybrid model that uses traditional machine learning to analyze user behavior at a granular level — what you watch, when you pause, what you revisit — and then applies generative models to improve content discovery. That kind of system can understand a natural language prompt like, “I want more comedies like Happy Gilmore” and respond with a curated set of titles that better reflect mood, tone and genre than keyword-based searches ever could.

AI’s expanding role in streaming media

A deeper understanding of user intent is critical as platforms try to reduce churn and increase time spent within the ecosystem. But personalization isn’t limited to recommendations.

AI is poised to transform how content is localized and made globally accessible. Subtitles, dubbing and metadata tagging are all candidates for automation — but not without oversight. We’ve already seen instances where generative models introduced typos, grammatical errors and placeholder references like “ChatGPT” in translated captions, undermining quality and user trust. These issues go beyond cosmetic to speak to a deeper challenge: how to scale AI without sacrificing fidelity.