Artificial Intelligence in Media and Entertainment: Personalize and Engage Smart With AI

Did you see the wonderful architectural designs and buildings created by Hungarian architect László Tóth, portrayed by Academy Award winner Adrian Brody, in this year’s film “The Brutalist”? Do you know that the accurate representation of the emergence of these revolutionary buildings to life was the result of the use of GenAI tools, rather than expensive set pieces?  

Like many others, the media and entertainment industry is embracing the rise of AI, transforming everything from news production to films and video game design. Interestingly, with more than 45.2% of the market in 2024,  artificial intellegence emerged as a leading technology because of its sophisticated abilities in pattern recognition, data analysis, and predictive content customization. Together, AI and machine learning rebuild the media landscape, simplifying content production and helping media producers take their personalization game to an entirely new level. McKinsey reports that 71% of customers want businesses to provide individualized content. According to 67% of those consumers, they become irritated when companies don’t cater to their needs. 

This shift is not solely a technological transition; it is a behavioral shift. To remain competitive in the future, every organization within the media and entertainment industry will need to embrace the adoption of AI and rethink how they engage with, create, and deliver experiences in a world where personalization is no longer optional. Want to learn more about how you can use AI to improve user experience? Continue reading, we have even more secrets worth uncovering. 

AI-Powered Personalization: Knowing Your Audience Like Never Before 

The media AI industry is projected to grow at a compound annual growth rate (CAGR) of 35.6% from 2024 to 2030, from USD 8.21 billion to USD 51.08 billion. 

This growth is primarily motivated by the growing popularity of AI-powered tools that improve the personalization of media content, deliver personalized recommendations, and improve customer experience, fostering loyalty. While this has become a widely recognized narrative, it’s time to move past the surface. What truly matters is understanding how this technology is reshaping the very fabric of entertainment as we know it. 

At the heart of AI-powered personalization lies one fundamental principle: understanding the “what” of consumer consumption, but also the “why”, “when,” and “how” consumers are interacting with content.  AI technologies can analyze everything from watch history and genre preferences down to behavioral micro-levels such as frequency of pause, spikes in volume changes, or the time of day a user typically consumes content. These multidimensional data types are analyzed through machine learning algorithms and converted into dynamic user profiles that evolve.  

We can conceptualize an example here. Take Mia, for example. When she watches content at night, she prefers romantic dramas. In the morning, she typically watches short-form thrillers. The AI algorithms have determined from her behavior over the past month that she usually clicks away from movies that are longer than ninety minutes throughout the week. They also know that she typically bypasses intros, unless it is part of a mini-series. The AI can analyze that she increases playback speed during dialogue-heavy scenes and frequently replays segments with emotional music. 

Instead of just recommending another “popular drama,” this platform is curating her nightly watch list of emotionally driven, 80–90 minute films with rich soundtracks; it automatically skips introductions, and uses her past behaviors to customize subtitle font size. The thumbnails that are shown to Mia are not generic, they are dynamically generated, using frames of emotion that match the feelings denoted in her most watched scenes. Even the trailer she sees is AI-edited to center on emotional beats instead of plot-based actions. AI offers content specifically for the time of day she is browsing.  

This is personalization in action, active beyond categories and tags. It is the orchestration of content, format, and context that takes someone who is passively viewing to a deeply personal engagement. Media platforms such as Netflix and Spotify have been doing this for years using collaborative filtering, deep learning, and reinforcement models of user viewing behavior. For instance, Netflix examines various thumbnails for the same title using A/B testing to see which ones appeal to different viewer archetypes. Spotify clusters users by their mood-based consumption lumps, then generates playlists like “Daily Drive” and “Chill Mix” that feel intuitively crafted for each listener. 

The business value is huge: hyper-personalized recommendations decrease churn rates, increase time in session, and create an experience that feels like natural discovery, allowing consumers to control their entertainment. To consumers, it’s not about finding something to watch; it’s about the feeling that media companies understand them. That emotional trust is quickly becoming the new currency of loyalty. 

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Content Creation Gets a Digital Co-Pilot 

If AI in media and entertainment can customize content experiences to individual preferences, the next step would be to change how content is created. Personalization is being moved from recommendation engines to experience designs that are shifting the role of human creators towards AI, not just as an assistant, but a creative co-pilot. The media industry has always stood on the cutting edge of innovation, and in this moment, artificial intelligence is enabling creativity at the core. AI tools are informing how media is conceived, produced, and delivered across all of these touchpoints. AI can help begin the story and writing process, enhance post-production, and stand in for visual effects. Tools like Runway, OpenAI’s Sora, and Adobe Sensei are making workflows faster and smarter, automating time-consuming processes like rotoscoping, voice dubbing, and color correction. For example, Descript allows editors to edit video and audio like a word processor, and Soundraw offers a service to generate original background tracks in minutes. 

Here’s one of many use cases. A small creative studio is working on a sci-fi short film. The team uses an AI-powered script generator that was trained on successful genre tropes and user-driven sentiment analysis, allowing them to lay out a compelling plot in a fraction of the time. Simultaneously, a generative art tool produces visual style frames based on text prompts that describe mood, lighting, and setting. The team leverages an AI tool to edit their early footage, cutting filler and identifying emotionally resonant cuts. Within a week, they are reviewing a polished concept reel, something that would have taken them a month to do manually. 

The outcomes are undeniable: 

• Decreased costs of production by automating manual rote tasks in the creative process 

• Shortened development cycles through a quick iterative process 

• Data-informed storytelling aligned with audience habits 

• Ability to scale and compete with enterprise-level production within small teams 

Smart Engagement: Real-Time Interaction and Immersive Experiences 

As content becomes more personalized and production workflows more streamlined, the next phase will be to engage with audiences in real time. Smart engagement, driven by AI, is shifting the entertainment experience from audience-induced passive consumption (i.e., viewing a film) to viewers developing interactive, immersive participatory experiences with media and entertainment, all done in a very technical way. At the heart of this shift is the existence of AI systems that process data in real time, model natural language, computer vision, and the predictive behavior of users. These systems allow applications to react to user commands, be predictive, process individual needs, and create unique experiences, whether this be through streaming services (Netflix, Hulu, etc.), games (Minecraft), virtual experiences (metaverse), etc. 

Here is a short technical breakdown. In virtual concerts or events that are live-streamed to millions of viewers, the AI sentiment engines are absorbing millions of messages and reactions, and using computer vision (if webcams are enabled) to determine audience mood. That data is then fed back into orchestration systems and can control lighting, suggest camera angles, and change the tempo of the music in real time based on how the audience is participating in the collective mood. Ultra-low-latency streaming and AI-generated improvements (such as auto-framing, voice noise reduction, and facial animation synchronizing) are made possible by real-time media processing frameworks (such as NVIDIA Maxine or Agora), which are essential for virtual presence. 

In gaming, reinforcement learning algorithms allow NPCs (non-playable characters) to develop based on player choices, which offers a gaming experience that seems naturally reactive. AI behavior models and player telemetry data are being used by companies like Ubisoft and Electronic Arts to modify difficulty, story advancement, and in-game surroundings in real-time, giving players a sense of uniqueness. 

Additionally, we’re witnessing advances in generative interaction models, such as those behind interactive brand mascots, AI-powered characters on metaverse platforms, and virtual influencers. These are not scripted bots, but rather driven by LLMs that have been refined using conversation history and domain-specific information, enabling complex, human-like communication and even memory-based continuity across sessions. 

Smart engagement provides a variety of benefits: 

• Hyper-contextual content delivery tailored to user behavior 

• Low-latency immersive environments via AI edge processing and cloud orchestration 

• Extended session times, better brand affiliation through interactive storytelling 

• Data-rich insights for creators to iterate on real-time user behavior. 

Monetization and Ad Targeting: Precision Over Interruption 

AI’s role in monetization and ad targeting takes advantage of its real-time interaction capabilities and adds a layer of intelligence that prioritizes revenue maximization without compromising user happiness. In a media environment driven by engagement metrics, retention curves, and user-level personalization, it should accompany a change in monetization strategy to move away from traditional, static placements to a real-time, predictive, and context-based approach. AI allows for audience segmentation with a granularity never reached before, benefiting from the implementation of unsupervised learning techniques (e.g., clustering algorithms such as K-means or DBSCAN) to determine clusters of users exposed to behavioral, temporal, and content interaction data. These clusters enable micro-segments that can be used for look-alike modeling, cross-device targeting, and to help brands optimize their creative for an audience’s behavior. The created cohorts are not just based on demographics, but on intentional behavior.  

On a more advanced level, companies are now implementing reinforcement learning (RL) agents that are continually testing and refining ad delivery strategies to optimize KPIs such as engagement rate, viewability, and post-view actions/conversions. Companies such as Google Ad Manager and The Trade Desk have integrated RL into their programmatic pipeline and thus can leverage adaptive bidding and a dynamic pacing approach to media plans, all based on real-time user signals. 

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Ethical Lens: Data, Bias, and Deepfakes in Ai-Powered Media 

While AI has the potential to revolutionize content personalization, generation, and monetization, it is raising important ethical questions—specifically, data privacy, algorithmic bias, and the emergence of synthetic media. The role of AI in media is often dependent on some form of personal behavioral and/or biometric data collection (which, if done without consent, can risk breaching regulations like GDPR, CCPA, or COPPA). Practicing privacy-preserving techniques like differential privacy or federated learning may help media production organisations avoid the legal and reputational harm that could come from being exposed to any of the regulations. 

Bias is another major issue. Recommendation engines, speech models and moderation systems are all subject to bias found in their training data, not only resulting in slight misrepresentation at times (or complete omission) but in the case of recommendation systems, reinforcing social inequality when deploying them to readers, consumers, or customers without applying any bias audits or fairness constraints. The most visible issue is deepfakes: AI-generated audiovisual content that convincingly reincarnates actual people, who can be used for good or harmful (to the original people’s reputations) creative output. Deepfakes raise serious ethical concerns around consent and deception as they obscure ethical boundaries for the creation of synthetic media. 

Commercial use cases of deepfakes include: 

• Hiring “deepfake actors” for scalable performance generation 

• Using a performer’s likeness as a “wrapper” over another’s performance 

• Creating virtual brand ambassadors or avatars 

Legal regulations are emerging: 

• New York (2020): Bans unauthorized use of a deceased performer’s digital replica for 40 years posthumously if it may deceive viewers. 

• Texas (2019): Prohibits deceptive political deepfakes within 30 days of an election. 

• California (2019): Similar law, but within 60 days of an election. 

• Canada passed the Online News Act in 2024, ensuring large tech platforms compensate news publishers for the content that is reposted on the social networks. This is also an attemt to limit the amount of “fake news” content and crate link between the news consumed by users and the news creators.  

Entertainment industry companies intending to use celebrity deepfakes should seek legal advice to ensure their use complies with local publicity and likeness rights laws, because incorrect use can result in serious legal consequences. In regard to the larger ethical questions, media companies are increasing their use of content provenance systems (like CAI and C2PA), disclosing synthetically-generated content, monitoring for fairness and bias regularly, and adding explainability structures like SHAP and LIME so that AI decision-making can be more transparent and accountable. 

The Future of Media With AI: Human and Machine Creativity 

The future of media is about man and machine, not man versus machine. As AI tools become more sophisticated, the creative process is less about automating tasks and more about amplifying human creativity. Film director Robert Rodriguez defined it well by saying, “Technology is just a tool. People give it meaning.” Welcome to the next evolution of our field: generative collaboration. In this mode of work, AI will assist projects with creativity and become an adaptation of human vision.  

Want to learn more about the impact of AI on the industry? Contact Avenga, your trusted AI development partner.