{"id":1764,"date":"2019-06-18T02:11:58","date_gmt":"2019-06-18T02:11:58","guid":{"rendered":"http:\/\/kusuaks7\/?p=1369"},"modified":"2023-06-29T15:06:32","modified_gmt":"2023-06-29T15:06:32","slug":"how-artificial-intelligence-is-transforming-the-media-industry","status":"publish","type":"post","link":"https:\/\/www.experfy.com\/blog\/ai-ml\/how-artificial-intelligence-is-transforming-the-media-industry\/","title":{"rendered":"How artificial intelligence is transforming the media industry"},"content":{"rendered":"

Artificial intelligence offers huge promise for media companies \u2013 yet success to date has been reserved for the pioneering few. Here we discover what needs to be done to overcome challenges and ensure the benefits are felt right across the industry.<\/strong><\/span><\/p>\n

<\/h3>\n

Artificial intelligence (AI) promises to transform the media and entertainment business \u2013 impacting everything from content creation to the consumer experience.<\/p>\n

\u201cAI will influence all parts of the media value chain, helping content creators to be more creative, helping content editors to be more productive, and helping content consumers to find the content that matches their interests and current situation,\u201d explains Rainer Kellerhals, Microsoft\u2019s Media and Entertainment industry lead for the EMEA region. \u201cIt will assist human creativity and human curiosity by taking a lot of the leg work out of finding relevant content, navigating large amounts of content, and re-formatting and re-purposing content.\u201d<\/p>\n

Lorenzo Zanni, IABM\u2019s lead research analyst, agrees. \u201cMedia companies can leverage AI throughout their content supply chains to automate operations, drive decision-making and personalise the consumer experience,\u201d he says, pointing towards automatic metadata tagging and extraction as an excellent example of one of the most effective use cases of AI today. \u201cThrough techniques such as image recognition and speech-to-text transcription, metadata tagging is the most widespread application of AI so far. The metadata automatically created by the AI algorithms can then be used to drive content monetisation strategies.\u201d<\/p>\n

And this is just the start. Zanni says that media companies can also use AI to strengthen their predictive capabilities. \u201cFor example, AI tools can be used to predict demand to adjust resources (in on-demand cloud models) or to predict possible disruptions in the content supply chain (such as a content supplier failing to meet a deadline). These use cases could bring sizable savings to media companies.\u201d<\/p>\n

When it comes to distribution, AI can personalise the consumer experience, driving title recommendations and curating content based on consumer preferences. \u201cThis is consistent with the transition from a \u2018one-to-many\u2019 to a \u2018one-to-one\u2019 model,\u201d Zanni notes.<\/p>\n

Despite this potential, Zanni believes that true success to date has only been achieved by the pioneering few. \u201cAlthough media companies are already using analytics tools to analyse operations and audiences, they are just starting to harness the power of more sophisticated tools such as deep learning algorithms,\u201d he says.<\/p>\n

There are a number of reasons for this, most of which revolve around data. In supervised learning algorithms, datasets need to be labelled by humans to train the model, making the process expensive and cumbersome for large datasets.<\/p>\n

\u201cThe availability of training data is a particular challenge,\u201d explains Kellerhals. \u201cMany AI methods use some sort of machine learning, and in most cases, AI can only be as good as the data which is used to train it.\u201d<\/p>\n

\u201cDeep learning algorithms produce the most accurate results only when they are fed with millions of observations,\u201d Zanni adds. \u201cTherefore, media companies need to manage different types of data in a unified manner to power effective AI-driven decision-making. This data includes audience data, operational data and content data (metadata).\u201d<\/p>\n

To succeed, media companies need to deploy technologies and implement strategies to gather data at scale. Zanni says that a \u201cdata-first\u201d approach is necessary \u2013 something that heavy users such as Netflix and some of the niche over-the-top players are adopting. \u201cMost of these companies have moved data processing workflows to the cloud, as this allowed them to scale up resources if needed by the size of the information analysed,\u201d he explains. \u201cThis leads not only to what all media companies are looking for at the moment \u2013 a better return on investment \u2013 but also to a greater responsiveness to market changes. What\u2019s more, as they move to the cloud and establish direct to consumer connections, they\u2019ll be able to gather more data on operations and audiences.\u201d<\/p>\n

This is where the Microsoft Azure cloud comes into its own. It offers powerful machine learning, real-time analytics, cognitive and bot capabilities through open APIs, which enables companies operating across the media and entertainment industry to transform their content and audience data into a competitive advantage.<\/p>\n

\u201cAzure Video Indexer, for example, builds upon media AI technologies to make it easier to extract metadata from video, including timecoded transcripts, faces, speakers, objects, actions, brands, keywords and sentiments,\u201d explains Kellerhals. \u201cOur audience insights function, meanwhile, uses the Microsoft Azure Data Platform to capture data about user interactions with online media, building user profiles (also of anonymous users) that in turn are used to power recommendation engines, personalisation, ad targeting and inform content investments.\u201d<\/p>\n

With these types of solutions, the potential for media and entertainment companies is huge. And, according to Guy Finley, executive director at the Media and Entertainment Services Alliance (MESA), it\u2019s an opportune time to make use of this potential. \u201cAs an industry we are beginning to build a direct relationship with the consumer for the first time and we\u2019re already working to automate and digitise existing workflows and processes,\u201d he explains. \u201cFor example, when contracts become smart, and we migrate from what was once called a \u2018\u00adone-button transcode\u2019 to an AI-enabled \u2018\u00adno-button\u2019 transcode, we will begin to see how fluid this entire process can become. Redundancy will be further reduced in our supply chains by smarter, data-driven systems and AI will ultimately drive the globalisation of our enterprises, enabling production and distribution to meet a growing, but still localised, demand for our content.\u201d<\/p>\n

Possibly most significantly, AI will be at the forefront of creativity \u2013 the force that ultimately drives the media business. \u201cArtists equipped with an AI-enabled feedback loop based on real-time, consumption metrics will up their creative batting average, which will thus increase production and commercial ROI,\u201d Finley concludes.<\/p>\n

This article was originally published on The Record<\/a>.<\/p>\n","protected":false},"excerpt":{"rendered":"

Possibly most significantly, AI will be at the forefront of creativity – the force that ultimately drives the media business. Artists equipped with an AI-enabled feedback loop based on real-time, consumption metrics will up their creative batting average, which will thus increase production and commercial ROI. AI will influence all parts of the media value chain, helping content creators to be more creative, helping content editors to be more productive, and helping content consumers to find the content that matches their interests and current situation.<\/p>\n","protected":false},"author":576,"featured_media":3042,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"content-type":"","footnotes":""},"categories":[183],"tags":[97],"ppma_author":[3267],"class_list":["post-1764","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-ai-ml","tag-artificial-intelligence"],"authors":[{"term_id":3267,"user_id":576,"is_guest":0,"slug":"lindsay-james","display_name":"Lindsay James","avatar_url":"https:\/\/secure.gravatar.com\/avatar\/?s=96&d=mm&r=g","user_url":"","last_name":"James","first_name":"Lindsay","job_title":"","description":"Lindsay James is Head of Editorial at Tudor Rose. She worked in publishing and advertising for almost twenty years. She spends much of her time interviewing CEOs, writing features for a wide variety of magazines."}],"_links":{"self":[{"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/posts\/1764","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/users\/576"}],"replies":[{"embeddable":true,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/comments?post=1764"}],"version-history":[{"count":2,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/posts\/1764\/revisions"}],"predecessor-version":[{"id":28980,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/posts\/1764\/revisions\/28980"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/media\/3042"}],"wp:attachment":[{"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/media?parent=1764"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/categories?post=1764"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/tags?post=1764"},{"taxonomy":"author","embeddable":true,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/ppma_author?post=1764"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}