Digital Innovation Digital Publishing
6 mins read

How AI can support editorial teams, by taking over mundane tasks

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AI has gradually made its way into the media industry, where it’s beginning to make a significant impact. Bit by bit, it’s revolutionising content development, user experience, video workflows, SEO, digital marketing, and lots more.

Some of the big players in the media industry, such as the BBC or the New York Times, recognised this some time ago. These publishers have already begun harnessing the power of AI. For the most part, they are using AI for automating various mundane tasks in content development and publishing. 

Nevertheless, the hype that still surrounds AI prevents many publishers from understanding the areas where it can simply and cost-effectively solve many of the challenges they are currently facing

Here are a few pointers into what AI means for publishers and how they can benefit from it. 

We’ll cover:

  1. What does AI mean?
  2. Editorial work and AI
  3. AI in content discovery
  4. AI in content creation
  5. AI in content publishing

What does AI mean?

Since there is a lot of buzz around the term, let’s get on the same page about what we mean by AI in the context of this piece.

AI 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. 

Machine Learning 

When it comes to Artificial intelligence there are two buzzwords most people are using nowadays: machine learning and deep learning.

Machine-learning algorithms use statistics to find patterns in massive amounts of data. And data, here, can mean a variety of things—numbers, words, images, clicks. If it can be digitally stored, it can be fed into a machine-learning algorithm. 

Video streaming platforms, for instance, use this technology to recommend new videos to users. Machine learning involves a lot of calculations and code, and it can only be of use when there is enough data available to process.

Deep Learning

Deep Learning is a subfield of Machine Learning. 

Whereas in machine learning a programmer has to intervene in order to make adjustments, in deep learning the algorithms can self-determine whether their prognosis is right or wrong. This technique basically learns by experience. 

Deep learning can be seen in driverless cars where algorithms can study their environment over time and make decisions based on experience. Some deep-learning models specialise in street signs, while others are trained to recognise pedestrians.

While that might all sounds super exciting, why should editorial teams be interested in this kind of technology? 

Artificial Intelligence impacts the media industry significantly. An Accenture report indicates that 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.

The impact of AI on Industry Growth (Publishing & Media)

Editorial work and AI

The impact of AI ranges from content creation, user experience, SEO, and digital marketing. It has the potential to enable content editors and creators to be way more productive, creative, and efficient. 

Nowadays, editors have a multitude of other tasks, in addition to their main jobs of research and writing. 

AI can take over exactly those mundane actions that people tend to find kind of annoying anyways. For example, keyword research, performance optimisation, and distribution. 

This allows editors and content creators to refocus on their core competencies

“I want an editor to be creating an idea – 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.”

Jon Watkins, Media Consultant

In the next sections, we’ll discuss how Content Intelligence can help editorial teams, based on real use cases.

AI in Content Discovery

To find the right topics to cover is one of the biggest challenges for editorial teams. However, it’s not a 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. 

This makes it an essential complement for any content strategist, as AI can deliver the information they need to make informed decisions out of noisy, unstructured data.

The unifying thread through all of this is the fact that AI can deliver highly relevant insights automatically, at scale, and in a manner you can easily share. 

Without this kind of technology, publishers could only achieve a similar result with the support of hundreds of analysts and an unlimited budget.

AI helps editorial teams with aspects like:

  • Headline insights
  • Seasonal topic recommendations
  • Finding hot topics related to your content domain
  • Image recognition and visual search
  • Audience targeting and segmentation 

AI in Content Creation

Artificial intelligence has the potential to assist editors in the process of creating content, too. Here are a couple of examples to demonstrate what that means.

Automated text tagging

When creating an article, digital journalists usually have to either rely on the automated tagging available in the CMS or add tags manually.

However, there are smarter alternatives such as Editor, a self-learning interface for text editing by The New York Times. The Editor automatically tags text and creates annotation based on information gathered through a set of neural networks.

Screenshot of Editor, a self-learning interface for text editing by The New York Times

Content translation

Most 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. 

Despite the fact that automated translation software like Google Translate and Deepl have been out there for years, the style of the language output rarely meets high journalistic standards.

Nevertheless,, a multilingual policy news website, has been experimenting with automated content translation since its inception.

Screenshot of, a multilingual policy news website

Only two years ago they started using an AI-powered technology by the company Tilde to 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 aligns it with the official style guide.

Additional areas for AI in Content Creation

Other areas AI helps with content development are:

  • Adding trending keywords
  • Finding synonyms
  • Sentiment analysis
  • Grammar check
  • Image recognition
  • Automated reporting
  • Reformatting of articles
  • Content moderation 

AI in Content Publishing

Traditionally the process of content management has been a serious issue for editors. Artificial Intelligence can be used to automate the publishing process too. 

It 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. Artificial Intelligence can do these tasks millions of times faster and better than any human being. 

Furthermore, SEO plays an increasingly important role in content distribution. Without, for example, the correct setting of metadata, 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. 

The chart below shows the average amount of time spent on the crucial but sometimes repetitive task of keyword research based on the size of a particular site.

Chart: Time spent on keyword research

To conclude

AI 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 ride it out. 

It’s important to simply get started with using AI, at least in a preliminary manner. Based on further experience publishers can then decide if and how to continue using AI-driven technologies. It can, and should, be a step by step approach.

Regarding the fear that AI could replace humans… humans will never be substituted by software in publishing and media, since creativity and art are fundamental and irreplaceable aspects of creating valuable content. 

Editorial teams should not be spending time building hyperlinks, auto linking products, and uploading stories. That’s what AI can do. When human content creators are supported by AI, editorial jobs become more rewarding.

“AI should be an extension of your team. It should not be your team.”

Hanifa Dungarwalla, Group Digital Marketing Manager at Bauer Media

by Esra Celebi
Head of Marketing, SPRYLAB

AboutSpryLab was founded in 2007 by Stephan Heck and Benjamin Kolb. The company now employs more than 50 people and supports publishers in creating and distributing unique content. Clients include major media houses such as Axel Springer, Ringier, TI Media, News Corp, Bauer Media, Hubert Burda and Aller Media. Find out more here.