AI has crawled its way into the media industry, where it is beginning to make a huge impact. It is step by step revolutionising content development, user experience, video workflows, SEO, digital marketing, and lots more.\r\n\r\nSome of the big players in the media industry, such as\u00a0BBC\u00a0or the\u00a0New York Times recognised this already a little while ago. Those big players are already harnessing the power of AI to some extent. For the most part, AI is used by them in content development and publishing, which is saving a huge amount of cost.\r\n\r\nI believe that the hype that still surrounds AI prevents many publishers from identifying the areas where it can simply and cost-effectively\u00a0solve many of the challenges they are currently facing.\r\n\r\nThat\u2019s why in this article I want to explain what AI means for publishers and how they can benefit from it.\r\n\r\nI\u2019ll cover:\r\n\r\n \tWhat does AI mean?\r\n \tEditorial work and AI\r\n \tAI in content discovery\r\n \tAI in content creation\r\n \tAI in content publishing\r\n\r\nWhat does AI mean?\r\nSince there is a lot of buzz going on about it, I want to make sure that we are on the same page and you fully understand what AI actually means.\r\n\r\nAI or Artificial Intelligence is a subset of computer science. It is concerned with building smart machines and systems that are capable of performing a task that would usually require human intelligence.\r\nMachine Learning\r\nWhen it comes to Artificial intelligence there are two buzzwords everyone is talking about:\u00a0machine learning\u00a0and\u00a0deep learning.\r\n\r\nIn other words, Machine-learning algorithms\u00a0use statistics to find patterns in massive amounts of data. And data, here, encompasses a lot of things\u2014numbers, words, images, clicks. If it can be digitally stored, it can be fed into a machine-learning algorithm.\r\n\r\nVideo streaming platforms, for instance, use this technology to\u00a0recommend new videos to users. Machine learning requires lots of mathematics and code to perform as requested. Most of the time this procedure doesn\u2019t work simply because there is not enough data available.\r\nDeep Learning\r\nIt becomes really interesting when computers learn new tricks. In this case, we are talking about deep learning. Deep Learning is a subfield of Machine Learning.\r\n\r\nWhereas in machine learning a programmer has to intervene in order to make adjustments, in deep learning algorithms themselves determine whether their prognosis is right or wrong. This technique basically learns by experience.\r\n\r\nDeep learning can be seen in driverless cars\u00a0where they can study their environment over time and make decisions based on their experience. Some deep-learning models specialise in streets signs while others are trained to recognise pedestrians.\r\n\r\nThat all sounds super exciting, but why should editorial teams be interested in this kind of technology?\r\n\r\nArtificial Intelligence impacts the media industry significantly.\u00a0Based on an Accenture report, the information and communication sector is the biggest beneficiary of AI. Despite this, only a few media organisations have realised the potential AI offers to the sector.\r\n\r\n\r\n\r\n\r\n\r\n\r\nEditorial work and AI\r\nThe impact of AI ranges from content creation, user experience, SEO, and digital marketing. In sum, it has the potential to enable your content editors and creators to be way more productive, creative, and efficient.\r\n\r\nNowadays, editors have a\u00a0multitude of other tasks in addition to their main tasks\u00a0of research and writing.\r\n\r\nAI can take over exactly those mundane actions\u00a0that your editors probably tend to find kind of annoying anyways. For example, keyword research, performance optimisation and distribution.\r\n\r\nThis will allow content creators\u00a0to refocus on their core competencies.\r\n\r\n\u201cI want an editor to be creating an idea \u2013 developing it through images, or developing it through words, or developing events, or video or other alternatives, and I want them to be doing that in its purest form.\u201d\r\n\r\nJon Watkins, Media Consultant\r\n\r\nIn the next sections, I\u2019ll show how Content Intelligence can help editorial teams based on real use cases.\r\nAI in Content Discovery\r\nTo find the right topics to write about is one of the biggest challenges for editorial teams. However, it\u2019s not a big challenge for Artificial Intelligence. It can, for instance, process and interpret patterns in data at a scale that is just impossible for people to replicate.\r\n\r\nThis makes it an essential complement to any content strategist, as AI can deliver the information you need to\u00a0make informed decisions\u00a0out of noisy, unstructured data.\r\n\r\nThe unifying thread through all of this is the fact that AI can deliver highly relevant insights automatically, at a huge scale, and in a manner, you can easily share with other departments in your organisation.\r\n\r\nWithout this kind of technology, you could only achieve a similar result with the support of\u00a0hundreds of analysts and an unlimited budget.\r\n\r\nAbove all, AI helps editorial teams with aspects like:\r\n\r\n \tHeadline insights\r\n \tSeasonal topic recommendations\r\n \tFinding hot topics related to your content domain\r\n \tImage recognition and visual search\r\n \tAudience targeting and segmentation\r\n\r\nAI in Content Creation\r\nArtificial intelligence has the potential to assist your editors in the process of creating content, too. Let me give you two examples to demonstrate to you what I mean by that.\r\nAutomated text tagging\r\nWhen creating an article, digital journalists usually have to either rely on the automated tagging available in CMS or add tags manually.\r\n\r\nHowever, there are smarter alternatives such as\u00a0Editor, a self-learning interface for text editing by The New York Times. This\u00a0Editor\u00a0automatically tags text and creates annotation based on information gathered through a set of neural networks.\r\n\r\n\r\n\r\n\r\n\r\nContent translation\r\nMost international news outlets strive to win a broader audience across countries and languages. This is where translation and adaptation of the content becomes a challenge.\r\n\r\nDespite the fact that automated translation software like\u00a0Google Translate\u00a0and\u00a0Deepl\u00a0have been out there for years, the style of the language rarely meets high journalistic standards.\r\n\r\nNevertheless, there is\u00a0EurActiv.com, a multilingual policy news website, that has been experimenting with automated content translation since its inception.\r\n\r\n\r\n\r\n\r\n\r\nOnly two years ago they started using an AI-powered technology by the company\u00a0Tilde\u00a0to streamline their processes. The system analyses tens of thousands of uploaded stories and their human-made translations to learn the language the site uses and\u00a0aligns it with the official style guide.\r\nAdditional areas for AI in Content Creation\r\nOther areas AI helps within\u00a0content creation are:\r\n\r\n \tAdding trending keywords\r\n \tFinding synonyms\r\n \tSentiment analysis\r\n \tGrammar check\r\n \tImage recognition\r\n \tAutomated reporting\r\n \tReformatting of articles\r\n \tContent moderation\r\n\r\nAI in Content Publishing\r\nTraditionally content management has been a serious issue for editors. Artificial Intelligence can be used to automate your publishing process, too.\r\n\r\nIt can reduce routine workload through automation and optimisation of linking between articles. It can also be used to optimise affiliate linking by analysing content like images, audio, video, and text. The interesting part is that Artificial Intelligence can do these tasks millions of times faster and better than any human being.\r\n\r\nFurthermore,\u00a0SEO has been playing an increasingly important role for years. Without, for example, the correct setting of metadata, your content has less chance of being found online. These SEO challenges can hardly be mastered alone. Anything that is not creative in nature can be done by AI.\r\n\r\nThe chart below shows the average amount of time spent on the crucial but sometimes repetitive\u00a0task of keyword research based on the size of a particular site.\r\n\r\n\r\n\r\n\r\nConclusion\r\nAI offers editorial teams many possibilities to work more efficiently. Due to the complexity of the topic and the fear that comes with new technologies, publishers are very often overwhelmed and tend to\u00a0ride it out.\r\n\r\nI think that it\u2019s important to simply get started with using AI and see for yourself. Every company is different. Based on your experience you can then decide if and how you want to continue using AI-driven technologies. It should be a step by step approach.\r\n\r\nRegarding the fear that AI could replace humans: human beings are still very much necessary. I believe that humans will never be substituted by software in publishing and media since creativity and art are a fundamental and irreplaceable part of creating valuable content.\r\n\r\nEditorial teams should not be spending time building hyperlinks, auto-linking products, uploading stories. That\u2019s what AI can do. Human writers will only be supported by AI, editorial jobs will become simpler and the average quality of our articles likely even better.\r\n\r\n\u201cAI should be an extension of your team. It should not be your team.\u201d\r\n\r\nHanifa Dungarwalla, Group Digital Marketing Manager at Bauer Media\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\nThis article was first published on\u00a0purplepublish.com with some minor edits.