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Artificial Intelligence : Need of the hour and much beyond for Media & Entertainment Industry

Artificial Intelligence : Need of the hour and much beyond for Media & Entertainment Industry
March 10, 2021

In current times, artificial intelligence and machine learning is no more a choice or something that is “Good to have”. Rather, it is the “Need of the hour” for any industry’s marketing strategy and the Media & Entertainment industry is no exception to that.

According to Statista, there is a rapid growth forecast for the global AI software market in coming years, reaching up to 126 billion US dollars by 2025.

Artificial intelligence, with machine learning and deep learning technologies, promises to transform every aspect of business, devices, and their usage.


The media industry is witnessing a rapid transformation, especially during the COVID-19 Pandemic, and the only way forward to sustain against fierce competition and a huge consumption market in the best possible way is adapting to AI, machine learning, and deep learning.

Be it any area of the content value chain starting from content ingestion into the system to its distribution to the end consumers - AI, machine learning, and deep learning have their presence and benefits everywhere. Artificial intelligence, with machine learning and deep learning technologies, promises to transform every aspect of business, devices, and their usage.

Artificial Intelligence in current times, is no more a choice or “good to have” rather it is the “need of the hour” for any industry.

Let us try to understand the role of AI and its inevitability in different areas of the media and entertainment industry.

Different areas of media industry
Different areas of media industry
  • Classification for enhanced searchability- With huge content getting created every minute, classifying and organizing the content becomes the most important part in the content life cycle, as without the correct classification, it will be nothing but an ocean of unorganized data which is difficult and, in most cases, impossible to search and use. Just imagine all the data lying without any tagging or classification (which makes it of no use at all) and does not come up in any searches (for internal inventory searches as well as external searches) as they are not tagged. It is a mammoth task if we think about all this identification of metadata, classification, and tagging being done manually by identifying scenes or locations in the videos to classify and add tags.

    If the companies need to capture and sustain the market - media creators and distributors should use AI enabled video intelligence tools to analyze the content and classify for better searchability.

  • Metadata extraction and context binding (Key for consumer engagement and enhanced user experience)- Metadata extraction from images and videos, frame by frame by identifying the objects to add appropriate tags.

    Using artificial intelligence software and deep learning, all aspects of the video files of movies/series are extracted and tagged as metadata. Artificial intelligence enables the analytical services to detect activities and recognizes objects, celebrities, and inappropriate content. Based on the content-specific tagging, the suggestions are made to the consumers to watch the next content. AI algorithms and machine learning algorithms identify the end of the video in play and based on that, provide suggestions for the next videos for enhanced customer engagement.

  • Content personalization– Today, personalizing customers’ experiences is the most crucial business aspect to look for. User’s preferences, selections and predictions based on them play a major role in evolving the business model of the media companies. It is all AI and ML algorithms which is getting used in identifying the user preferences and accordingly recommends the content. The best example in this context is Netflix– it has the best in industry recommendation algorithms, which includes both the data collected based on last visits/watches of the consumer as well as 'collaborative filtering'.

    Collaborative filtering is a machine learning technique where similar users, based on their content viewing, are grouped together and their consumption pattern is analyzed to recommend relevant and highly personalized movies and TV shows to the users with similar content preferences

    Collaborative filtering
    Collaborative filtering
  • Monetization/Revenue/Marketing Strategy– Creating business plans for driving sales and revenue based on the AI advanced analytical capabilities plays a vital role in today’s times. Relying on the AI capability (that identifies the most accepted content by the users) helps immensely in building the marketing strategy for the product or service. There are many startups where the broad array of data has been compiled, and intelligence on top of that data helps the film studios in getting real time understanding of how choices like scripts and actors can create an impact on a project’s risk profile and revenue potential (Till very recently, the Hollywood studios have been dependent on only box office data and some audience surveys).
  • Real-time intelligent data streaming– Modern video compression software leverages AI for video compression automation. Automation is used to save time on tasks such as video compression. This technology enables to compress videos on the fly when uploading to the cloud. Predictive models are being used to optimize live video encoding by optimizing bitrate, resolution and frame rate. Real-time cataloging also plays a very important role with so many social platforms offering live streaming every minute. Putting analytics on top of the past viewing data helps to predict the bandwidth usage – which in turn helps the streaming platforms make the decisions on the load and cache for the regional servers to enable faster load times during peak demand.
  • Content Creation– Though artificial intelligence is still evolving and is not perfect in content creation as of now, we still have examples of movies like Zone Out where the complete direction was done by an AI algorithm. If not complete content creation, there are various areas where AI has its benefits. In content creation, it has now become possible to select best shots, best place to cut, suitable color scheme, places to cut or merge scenes. There are things that are difficult to detect by a human eye, at the same very simple to do for the AI based software. A trained AI engine is also capable to create custom ads on the fly basis the best and most watched identified scenes. There is huge work going on in this area with evolving results each day.

Is the road to AI easy?

The first thought that comes to mind when thinking about AI implementation is the cost of the implementation.

AI with defined tasks is not as expensive, rather a huge gain in long term

However, if we look at the tools, frameworks, libraries, and huge datasets that are readily available in market to be leveraged– we will find that a well-thought AI implementation with defined tasks is not as expensive, rather a huge gain in the long term. If the media companies have to sustain the competition and capture and maintain the consumer base, adapting AI is the way forward.

To sustain the competition, capture & maintain the consumer base for media companies, adapting AI is the way.