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AI will impact all parts of the media value chain, whether it is the processes or providing consumers with content as per their demand.
FREMONT, CA: Media and entertainment is a massive industry in terms of the number of people, revenues, or complicated processes involved in it. Moreover, with ever-increasing data sets, it is becoming increasingly difficult to operate smoothly. Incorporation of technologies such as artificial intelligence (AI) is the need of the hour. AI will impact all parts of the media value chain, assisting content creators in channelizing their creativity, helping editors to be more efficient, and enabling consumers to enjoy the content that matches their tastes. Additionally, it will reduce manual efforts that are spent in searching for relevant content or in navigating massive amounts of data sets. AI-driven solutions are also helping the media companies to leverage AI throughout their content supply chain and foster personalized contents by automating their operations.
Unmatched Potential of AI in Media
AI technology has enormous potential in the media industry, especially as it provides a broader perspective in terms of operations while also assisting in scaling conventional techniques. Techniques such as speech-to-text transcription and image recognition are allowing metadata tagging to be the most common application of AI. The automatic metadata generated by AI algorithms is useful in driving content monetization strategies.
AI algorithms are also quick to recognize patterns and thus can be used to predict demands to adjust resources or to predict expected disruptions in the content supply chain. AI can also foster personalization by driving title recommendations and assembling contents as per the consumer’s preference.
Present Scenario and the Road Ahead
In spite of the immense potential of AI in media, only the top few companies have been able to leverage it. Media companies are using analytics tools to analyze audiences and operations. However, feeding the systems with relevant data sets is also necessary. For instance, supervised learning algorithms require humans to train the model. The process is not just complicated but also expensive for large data sets. The availability of training data sets is another challenge as an AI can only be as good as the data fed into it. Gradually, companies are realizing such limitations and investing their resources to gain more from AI.
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