AI in OTT uses artificial intelligence and machine learning to personalize content recommendations, improve search, understand viewing behavior, and optimize advertising. These capabilities help streaming platforms connect viewers with relevant content while supporting engagement and revenue goals.
The opportunity for AI is reflected in Deloitte’s 2026 Digital Media Trends report, which found that 22% of fans surveyed said they would use streaming services more if they offered more personalized recommendations powered by generative AI. This highlights the potential for AI to make streaming experiences more relevant and encourage deeper engagement.
This article explores how artificial intelligence in OTT works, its key applications, implementation considerations, and emerging trends shaping AI in OTT streaming.
AI in an OTT platform analyzes viewing activity and content information to personalize recommendations, improve content discovery, and support advertising decisions.
The process begins with collecting data such as watch history, searches, watchlist activity, viewing duration, and content metadata, including genre, cast, language, and themes. Machine learning models analyze these signals to identify viewing patterns, rank relevant titles, personalize homepages, and predict changes in viewer interests. Similar insights can support content management and ad selection.
Most production recommenders work in 2 stages:
Features are served from a feature store, which keeps training data and live data consistent. Batch pipelines refresh long-term preferences overnight. Streaming pipelines (Kafka/Flink) update session signals within seconds.
Example: When a Netflix member watches several crime thrillers and searches for detective dramas, the platform can use these interactions, along with title information and the viewing patterns of similar members, to rank relevant shows on the member's homepage. Another viewer with different interests may see a different selection. This demonstrates how AI helps OTT platforms personalize content discovery instead of displaying identical recommendations to everyone.
AI-powered personalization helps OTT platforms tailor the viewing experience to individual preferences. From recommending relevant movies and shows to adapting content rows and improving subscriber retention, AI helps viewers find content they are more likely to enjoy.
Below are some key ways OTT platforms use AI to personalize the viewing experience:
Personalized recommendations help viewers find movies and shows that match their interests instead of relying only on trending or popular titles. By combining viewing history, ratings, and title details such as genre, cast, and language, AI can estimate which titles a viewer is more likely to enjoy.
Under the hood: New titles have no viewing history. This is the cold-start problem, and it is usually handled with content-based embeddings built from synopsis, cast, genre and trailer data. These place a new title near similar ones in vector space from day one. Collaborative signals take over as viewing data accumulates.
Understanding how recommendation engines work can help OTT teams choose suitable approaches for matching viewers with relevant titles.
Real-world example: Netflix
Netflix uses personalized recommendations to help members find content that matches their tastes. Its recommendation system considers viewing activity, title information and feedback, as well as the preferences of other members with similar viewing habits. These signals help Netflix rank titles and decide which recommendations to display.
Netflix also takes a global approach to recommendations. The platform identifies groups of viewers with similar preferences across countries. This can help someone discover a show or movie from another market that matches their interests, even if that title is not among the most popular in their own country. For example, a viewer who enjoys a particular style of anime may receive relevant suggestions based on the tastes of anime fans around the world.
What to measure: How often viewers watch recommended titles and how long they watch them.
A viewer’s preferences are not always clear from their watch history alone. Someone might start a show but stop watching after a few minutes, while another viewer may finish several episodes of the same series. AI can analyze signals such as viewing duration, searches, saved titles, and ratings to identify these patterns and build a better understanding of individual interests.
Under the hood: Most signals are implicit, so they need careful weighting:
Raw watch time is usually normalised by runtime (completion ratio). Otherwise, long films would be overvalued compared with short episodes.
Real-world example: Roku
Roku uses viewing behavior and viewer feedback to make content recommendations more relevant. Its personalized home screen considers what viewers watch, save, and rate when suggesting movies and shows. This feedback gives the platform additional information about viewers' interests, helping recommendations adapt as viewing habits change. Roku also uses content tags and metadata to help its machine-learning systems match titles with relevant audiences.
What to measure: Viewer engagement with recommendations and changes in viewing activity after feedback.
AI can use recent viewing activity alongside longer-term interests to keep recommendations relevant as viewers return for new sessions. Cross-device features, such as synchronized playback progress, complement this experience by letting viewers resume programmes on supported devices, although playback continuity itself does not necessarily rely on AI.
Under the hood: Session-based sequence models (transformer- or RNN-based) predict the next title from the order of recent views. Their output is blended with long-term profile scores. Contextual features such as device type, time of day and session length shape the result.
A short weekday mobile session might favour episodes, while a weekend TV session might favour films. Shared household accounts also need profile disambiguation so one person's viewing does not distort another's recommendations.
Real-world example: BBC iPlayer
BBC iPlayer offers personalized recommendations to signed-in viewers based on their viewing activity. It also lets viewers resume programmes from where they left off when using supported devices. These features illustrate two related parts of the viewing experience: using viewing history to suggest relevant content and maintaining playback progress across sessions.
Practical consideration: Keep recommendations relevant across sessions and synchronize viewing progress across supported devices.
The homepage is often the first place viewers look when deciding what to watch. AI can help arrange this space with relevant content rows, themed collections, and suggestions based on individual interests. Rather than requiring viewers to search through an entire catalogue, these personalized layouts bring suitable titles closer to their attention and make browsing easier.
Under the hood: Homepage personalisation is a page-level optimisation problem, not just ranking titles. The system decides which rows to show, what order they appear in, and how to remove duplicate titles across rows. Contextual bandits balance exploitation (showing proven rows) with exploration (testing new rows or artwork).
When generative AI writes row titles, as with interest-based collections, outputs must be grounded in the actual catalogue. This stops the model from describing titles that don't exist or aren't licensed in that region.
Real-world example: Prime Video
Prime Video has explored using generative AI to make content discovery more personalized through its AI Topics feature. As described in Amazon's announcement of Prime Video's AI Topics feature, the feature groups titles into interest-based collections, such as mind-bending science fiction and fantasy adventures. Viewers can explore these collections and refine their choices through related topics.
Amazon introduced AI Topics as a beta experience, so it should not be treated as a feature available to every Prime Video subscriber. The example shows how AI can help OTT platforms organize titles around specific interests, giving viewers another way to discover content beyond individual recommendations.
What to measure: Clicks on personalized collections and the number of titles watched after viewers explore them.
AI can flag subscribers who may be at risk of cancelling by identifying changes such as fewer viewing sessions, declining watch time, or reduced interaction. These signals help retention teams test relevant content suggestions or reminders, though they do not guarantee a subscriber will cancel.
Under the hood: Churn models typically use gradient boosting or survival analysis, which predicts when churn is likely, not just whether. Common features:
More advanced teams use uplift modelling. It targets subscribers whose behaviour an intervention can actually change, rather than everyone flagged as high-risk, which avoids spending incentives on users who would have stayed anyway. Explainability tools like SHAP show retention teams why a user was flagged.
Practical scenario: An OTT subscriber becoming less active
Consider a subscriber who previously watched several times a week but has recently stopped watching regularly. An AI-based churn prediction model could flag this change, along with other relevant account signals, for further assessment. Based on the subscriber’s interests and communication preferences, the platform could test a personalized content recommendation or send a relevant reminder to encourage them to return.
The goal is not simply to predict cancellations but to identify suitable ways to retain subscribers without sending unnecessary notifications or irrelevant offers.
What to measure: Churn rate, renewal rate, and reactivation rate, compared with a control group to determine whether the retention effort made a difference.
Here are 4 practical ways AI can improve search, organize video libraries, and help viewers discover content they might otherwise miss:
Viewers may know what they feel like watching without knowing a title, actor, or genre. AI-enabled search for streaming platforms can interpret everyday requests and match them with content based on meaning, not just exact keywords.
For example, a viewer might type “a light-hearted mystery series with a clever detective and little violence.” With semantic search in OTT platforms, the system can compare the request with plot summaries, themes, character details, and other available metadata to find suitable titles.
Natural language search can help viewers look for content based on:
Under the hood: Production systems usually run hybrid search:
An LLM can parse the query into structured filters, such as genre=mystery, tone=light, maturity rating ≤ 13+. A cross-encoder reranker then refines the top results. All of this must fit within a tight latency budget (typically a few hundred milliseconds), so heavy LLM calls are cached or limited to complex queries.
When an OTT platform manages thousands of videos, manually describing every scene, topic, and spoken phrase can take considerable time. AI-based content categorization can help teams build richer metadata by identifying information in video and audio files. Detecting objects, scenes and on-screen text relies on computer vision, and spoken content relies on speech recognition.
Depending on the technology used, this can include:
Dailymotion attracts over 300 million monthly unique visitors and records more than 3.5 billion video views each month across 25 languages. To manage its video library, Daffodil Software developed a CMS with workflows for reviewing videos, assigning tags, and managing publication. AI can build on this structured approach by suggesting tags and extracting video information, helping teams improve content organization and search.
Read Full Success Story Here: How Daffodil helped Dailymotion build a scalable video CMS
Someone searching for a documentary about ocean conservation might also be interested in films about coral reef restoration, plastic pollution, or endangered marine life. Content discovery algorithms can connect these related topics using metadata and semantic matching, even when titles use different keywords.
AI can support this process by helping OTT platforms:
A viewer in Spain searches in Spanish for a psychological thriller set in a remote hotel, but the film's original description is in English. With accurate translated metadata and multilingual search, an OTT platform can match the query with the relevant title.
Artificial Intelligence can improve content discovery by helping platforms:
The results depend on translation quality, language coverage, and accurate metadata. Human review can help catch cultural references and expressions that AI may misunderstand.
Advertising helps OTT platforms generate revenue, but showing the same ads to every viewer can limit campaign effectiveness. AI in OTT advertising can help platforms choose relevant audiences, improve ad placement, and make better use of available ad inventory.
It can also help advertisers improve campaign performance without overwhelming viewers with repetitive ads.
Not every viewer responds to the same advertisement. AI can help group audiences using permitted signals such as viewing patterns, content interests, and engagement with different programmes. Advertisers can use these segments to plan campaigns for audiences more likely to be interested in their products.
For example, a sports streaming service could create an audience segment of viewers who regularly watch football. A sportswear brand could then target eligible viewers with ads for training gear or football boots.
AI-driven ad targeting can help OTT platforms:
Audience segmentation should respect viewer consent, privacy choices, and applicable targeting restrictions.
Contextual targeting matches an ad to the programme or subject being watched. Personalized ads use permitted audience information to make the selection more specific. Both approaches can support targeted advertising in OTT, but they rely on different signals.
Consider a documentary about professional cycling. A contextual ad for cycling equipment could fit the programme, while eligible audience data might help determine which viewers should see a particular offer. AI can help identify programme topics, assess ad relevance, and select suitable campaigns.
After identifying eligible audiences and campaigns, an OTT platform still needs to decide which ad to show and where to place it. AI can support ad placement optimization by assessing campaign priorities, available inventory, content context, and predicted performance.
For example, during a live football match, several advertisers may compete for the same commercial break. The ad decisioning system can evaluate eligible campaigns and select an ad based on bid value, campaign rules, and placement requirements. AI may help estimate which option is likely to perform better.
Dynamic ad insertion in OTT allows platforms to insert ads into live or on-demand streams instead of showing the same advertisement to every viewer. Programmatic advertising automates parts of the process of buying and selling ad inventory.
These technologies work together, but they perform different jobs:
For example, viewers watching the same live tennis tournament might receive different ads during an available break, depending on eligible campaigns and audience rules. The insertion system handles delivery, while the ad decisioning process selects the ad.
Reliable delivery also depends on ad timing, playback compatibility, and integration between the streaming player and advertising systems. The IAB Tech Lab CTV programmatic standards cover key areas such as ad delivery, targeting, measurement, and brand suitability.
AI for video advertising can help advertisers identify which campaigns, placements, and creative variations are more likely to meet their goals. Instead of waiting until a campaign ends, teams can use performance data to make adjustments while it is running.
For instance, an advertiser promoting a new streaming series might discover that one trailer generates more completed views than another. AI can help identify patterns across placements and eligible audiences, informing decisions about where to allocate the remaining budget.
Campaign teams can use these insights to:
More ad impressions can create additional revenue opportunities, but frequent interruptions and repetitive ads may frustrate viewers. Advertising revenue optimization for OTT platforms needs to account for both campaign performance and the viewing experience.
For example, if viewers regularly abandon a programme after several interruptions, the platform could test fewer ad breaks or stronger frequency caps. Comparing the results can show whether the change improves viewing continuity while maintaining acceptable revenue.
Privacy and ad suitability remain essential. Platforms must follow applicable rules for data use and targeting, including restrictions on sensitive categories outlined in Google's personalized advertising policies.
For OTT businesses building these capabilities, the challenge is connecting AI models with existing ad servers, streaming infrastructure, campaign data, and reporting systems.
Implementing AI in an OTT platform requires coordination between content systems, viewer data, streaming infrastructure, and business workflows. A recommendation model, for example, may perform well in testing but fail to deliver value if it cannot access updated content metadata or respond quickly enough to support real-time viewing.
A structured implementation approach helps businesses address these dependencies, control development effort, and introduce AI features without disrupting the existing viewing experience.
The following steps outline how OTT businesses can plan and implement AI solutions:
Start by identifying where AI can address an existing business challenge, such as low content discovery, declining subscriber retention, or inefficient ad targeting. Prioritize use cases based on business value, data availability, implementation effort, and expected results.
AI consulting services can help businesses assess potential use cases and define an implementation roadmap.
AI features rely on relevant, accurate data. Depending on the use case, this may include viewing history, search queries, content metadata, subscription activity, or ad performance. Data from different systems may need to be cleaned, organized, and connected before it can support reliable predictions.
Select an approach that fits the use case, whether it involves adapting an existing model, using an AI service, or developing a custom solution. The selected capability must integrate with relevant OTT components, such as the content management system, recommendation engine, search functionality, or ad server.
Daffodil Software’s AI integration services can help connect AI capabilities with existing applications and business systems.
Test the feature with a limited audience before rolling it out across the platform. Evaluate its accuracy, response time, reliability, and business impact. For example, a recommendation system should be assessed on whether it helps viewers find and watch relevant content, not just whether they click a suggested title.
The next phase of AI in OTT will focus on smarter content discovery, more efficient streaming operations, and greater personalization.
AI in OTT helps streaming platforms improve content discovery, personalize viewing experiences, and optimize advertising. From personalized recommendations to automated content management and ad delivery, these capabilities can strengthen viewer engagement and create new revenue opportunities.
Successful implementation depends on reliable data, integration with existing systems, and continuous performance monitoring. OTT businesses should prioritize AI use cases that address specific challenges and deliver measurable value.
For businesses planning to build or upgrade their streaming platforms, Daffodil Software’s OTT App Development services support the development of streaming applications tailored to business needs. With the right technical foundation, OTT providers can introduce AI capabilities that meet changing viewer expectations and support long-term growth.