Artificial Intelligence is a common buzzword in use these days across almost all industries. There are ongoing conversations about how AI can revolutionize sectors such as the automotive, healthcare, and manufacturing, name them. Everyone participating in these side chats about AI agrees on how AI can grow economies, bring innovative ideas to life, make industrial operations more efficient, and so on. The bottom line: AI has a lot to offer to any sphere, and the media industry is no exception to this revolutionary tech of our time.
AI and its impact on media
Digital media is shaping the media industry in more significant ways than we could imagine. All market players in the media industry, from radio, and TV, to films, have noted the importance of having digital strategies and how these strategies can become key revenue generation sources. However, the digital transformation that these media players are hoping for is not easy and requires an innovative approach in order to keep a competitive edge over everyone else—and this is where AI steps in.
In fact, before we knew it, media players had some form of AI-enabled solution in their business models. Take Netflix, for example, which uses computer vision algorithms as part of its recommendation engine that helps viewers rate a movie even before they watch it. The same case applies the 20th Century Fox and IBM, who used AI to come up with a trailer that viewers would consider frightening and suspenseful just from the click of the play button. The latter example was made possible after IBM and 20th Century Fox used machine learning techniques to analyze multiple trailers from horror and thriller movies to come up with one trailer that would truly keep their audiences engaged. The partnership was a success, thanks to AI.
Here are some of the major applications of AI in media, areas in which AI has impacted the media industry for the better:
News automation with AI
There is no better way to increase viewership while at the same time reducing costs than automating news production using AI. With automated journalism, broadcasters can research, compose, and distribute relevant content through AI.
To circle back on video streaming media services, Netflix is the biggest company so far benefiting from AI, saving $1 billion in content spending. With over 130 million subscribers on different payment plans, Netflix is using machine learning automation algorithms to analyze what their viewers want to watch the most and thereafter recommend movies or TV shows based on that data.
When it comes to the publishing media industry, Reuters and Bloomberg News are among the first players to lay off editors and reporters after adopting AI automation technology. AI automation, for example, accounts for one-third of the content published by Bloomberg News. Through automation, Bloomberg News is able to examine available financial reports and create news stories that are newsworthy and relevant to the financial industry. To add, AI fact-checks this to provide top-level accuracy in the long run.
AI in social media
AI has revolutionized how social media functions. Facial recognition made it possible to recognize people in photos and videos and generate suggestions about who to ‘tag’ them. Facebook, one of the social media giants, had over a billion of these digital representations of faces stored in its database for that purpose. Facebook also used the same feature to bring up ads for products users were looking at. The same can be said for LinkedIn’s Recruiter, which uses AI search and recommendation algorithms, also called ‘matching engines,’ to make job recommendations for job seekers based on their skills and qualifications. Through the Recruiter, hiring managers can find qualified candidates through the engine’s accurate recommendation from a ‘talent pool’ optimized by the algorithms.
Metadata tagging works just like text annotation, involving the tagging of labels to content or various elements of its content. Picture it this way: there are a million pieces of content being created each minute, and human power cannot possibly classify these items to make it easy for viewers to consume them. If it were to be done manually by employees from media companies, they would have to spend hours watching videos and other media pieces to identify and classify objects, locations, and scenes—then add tags. The process would take forever. With AI, content creators can analyze any form of media content, identify objects, and label appropriate tags to them and that makes it easier for media companies to make their content discoverable by millions of people as soon as it is created.
Although the world is becoming more interconnected, multilingualism is still a reality when it comes to shared content. Movie producers, for example, always want to make their content consumable to a global audience, regardless of their native language. To make that possible, these video producers must provide accurate multilingual subtitles for their target region. The manual creation of the subtitles for a dozen of languages would take hours for human translators. In addition, human translation is not error-proof and thus may end up making incorrect subtitles that would mislead their audience. This explains why YouTube came up with its AI-powered automatic subtitle creation software to solve this problem for content creators.
The future of AI in media
The world is estimated to spend $118.6 billion by 2025 on AI, and another $1,860.9 million by 2025 to be spent by the media and entertainment (M&E) industry (a rise from $329 million in 2019). It is not enough to say AI is gaining traction in the media industry but rather becoming mainstream when it comes to the integration of AI in media workflows. Media companies have experienced increased efficiency in understanding their audiences and predicting their choices before making appropriate recommendations, etc. —all that is thanks to AI. Indeed, AI is a game-changer in this industry and is already disrupting existing marketing strategies, streamlining media processes through automation, and ultimately driving revenue.
The rising competition in the media industry calls for the creation of a competitive edge that will put media companies ahead of the game. AI’s role in increasing efficiency and driving revenue is certainly expected to shape the industry in the coming years as more AI algorithms are being implemented. The media industry use cases above demonstrate the eagerness of these media companies to explore and experiment with what AI can do to maximize business performance through enhancing consumer experience while at the same time delivering value.