Applications of AI in the media & entertainment industry

 

The global media and entertainment industry is witnessing a rapid transformation in the way content is distributed. The growing ubiquity of content creation tools like high-resolution cameras, content creation software, and smartphones is allowing pretty much anyone to create, publish, and distribute written, audio, and video content.

 

This trend is further accelerated by the proliferation of the internet, which has led to the replacement of traditional media channels like cable and radio with on-demand streaming platforms like Netflix and YouTube. As a result, consumers have potentially limitless options to choose from, in terms of media consumption.

 

Thus, media companies are facing the need to raise the quantity as well as the quality of content they create to attract as many consumers as they can to drive higher value. To help them achieve this objective, media companies are adopting advanced technologies like AI.

 

The use of AI in the media and entertainment industry is helping the media companies to improve their services and enhance the customer experience. Here are a few AI use cases in media and entertainment that are transforming the industry:

 

1. Metadata tagging:

 

With countless pieces of content being created every minute, classifying these items and making them easy to search for viewers becomes a herculean task for media company employees. That's because this process requires watching videos and identifying objects, scenes, or locations in the video to classify and add tags.

 

To perform this task on a large scale, media creators and distributors like CBS interactive are using AI-based video intelligence tools to analyze the contents of videos frame by frame and identify objects to add appropriate tags.

 

Metadata tagging
AI-based video intelligence tools identify the objects and scenes in images that are specific to your business needs. Source

 

This technology is being used by content creators or media publishing, hosting, and broadcasting platforms like NFL Media to organize their media assets in a highly structured and precise manner. As a result, regardless of its volume, all the content owned by media companies becomes easily discoverable.

 

2. Content personalization:

 

Leading Music and video streaming platforms like Spotify and Netflix are successful because they offer content to people belonging to all demographics, having different tastes and preferences.

 

Content Personalization
Netflix uses AI & ML to share personalized recommendations

 

Such companies are using AI and machine learning algorithms to study individual user behavior and demographics to recommend what they may be most interested in watching or listening to next keeping them constantly engaged. As a result, these AI-based platforms are providing customers with the content that cater to their specific likings, thus offering them a highly personalized experience.

 

3. Reporting automation:

 

In addition to automating day-to-day or minute-by-minute operations, AI is also helping media companies to make strategic decisions. For instance, leading media and broadcasting companies are using machine learning and natural language generation to create channel performance reports from raw analytics data shared by BARC.

 

The weekly data that is usually received from the Broadcast Audience Research Council of India (BARC) is generally in the form of voluminous Excel sheets. Analyzing these sheets on a weekly basis to derive and implement meaningful learnings proves to be quite daunting for the analytics team.

 

Channel Performance Report
Media network companies use NLG-based reporting automation to enhance their channel performance reports.

 

By using AI-enabled data analysis and natural language generation-based reporting automation tools, business leaders can create performance reports with easy-to-understand language commentaries, providing them accurate insights to make informed data-driven decisions.

 

4. Subtitle generation:

 

International media publishing companies need to make their content fit for consumption by audiences belonging to multiple regions. To do so, they need to provide accurate multilingual subtitles for their video content. Manually writing subtitles for multiple shows and movies in dozens of languages may take hundreds or even thousands of hours for human translators.

 

Youtube Subtitle generation
Youtube's artificial intelligence allows publishers to add automatic transcription to their videos.

 

Besides, it may also be difficult to find the right human resources to translate content for certain languages. Additionally, human translation can also be prone to errors. To overcome these challenges, media companies are leveraging AI-based technologies like natural language processing and natural language generation. For example, YouTube’s AI allows its publishers to automatically generate closed captions for videos uploaded on the platform, making their content easily accessible.

 

To sum it up...

 

As competition and the need for efficiency continue to rise in the industry, the role of AI in entertainment is only expected to grow in the coming years. By exploring and experimenting with the above and other AI use cases, media and entertainment companies are maximizing their business performance by enhancing the user experience and entertainment value delivered by them with greater efficiency.

 


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WRITTEN BY

Romil Shah

Romil Shah is an AI enthusiast with considerable experience across varied technology domains, primarily Natural Language Generation (NLG), and Blockchain technology. He is passionate about technological innovation and more importantly its real-world applications. His work within the field has brought about constructive conversations and explorations around AI and its extended use in businesses.

Romil Shah is an AI enthusiast with considerable experience across varied technology domains, primarily Natural Language Generation (NLG), and Blockchain technology. He is passionate about technological innovation and more importantly its real-world applications. His work within the field has brought about constructive conversations and explorations around AI and its extended use in businesses.

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