**The architecture of newsroom software is usually quite complex to get. This is why we created a graphic overview of the tools and software commonly used for digital news publishing.**

If we were to list the most important lessons learned from  <a href="https://autentika.com/blog/nine-lessons-learned-from-working-with-newsrooms" target="_blank">our work with newsrooms</a>, we would put this one at the top: No two newsrooms are alike regarding tools, software, and the terminology they use to describe their work processes and editorial workflows.

Even when two digital media appear similar from a user's perspective, they can have very different "guts" and describe the same things differently. For example, they might use very different names for the same features, and we totally get it, as it is often a matter of culture and habit. 

Despite these differences, we have observed some common, universal characteristics – and today, we will show you how we see the typical editorial architecture. Do not think of it as the only existing truth – it's a summary of our experience that might be useful as you plan the next steps of your digital transformation.


The key parts of the newsroom’s software architecture

A CMS is the newsroom’s heart and central software for creating, editing, organising, and publishing digital content. It allows journalists and editors to write and edit articles, manage multimedia assets, and schedule publication.

<a href="https://autentika.com/blog/how-to-choose-a-text-editor-for-a-digital-first-newsroom" target="_blank">Read more: How to choose a text editor for a digital-first newsroom?</a>


1) Content Management System (CMS)

A DAM system stores, organises, and manages digital assets such as images, videos, audio files, and graphics. It provides a centralised repository for journalists to access and search for media assets quickly.


2) Digital Asset Management (DAM) System

The distribution system usually consists of several parts. 

- The first one might be a homepage management system integrated with the CMS that incorporates A/B testing tools, homepage analytics, a content recommendation engine, and layout settings. 

- The other part is the distribution network allowing for delivering content to users: push notifications, email marketing platforms for sending out newsletters, social media, podcast hosting platforms and subscription management platforms. 

These systems are interconnected with other elements of the newsroom's architecture, including user accounts, metadata management, and configuration.


3) Distribution System

User accounts are an essential component of newsroom software architecture, as they enable role-based access control and provide a personalised experience for journalists, editors, and other team members. 


4) User accounts

Configuration settings allow administrators to define workflows, permissions, and user preferences, tailoring the software to the specific needs and structure of the newsroom.


5) Configs 

Metadata includes attributes such as article headlines, authors, publication dates, categories, and tags. It enables journalists and editors to efficiently search, filter, and classify content based on specific criteria.


6) Metadata management

Additionally, a knowledge system sits atop these components, encompassing a knowledge base, contextual knowledge, and product support.


7) Knowledge system

This is obviously how we view the system – if you have any feedback or want to discuss this scheme, feel free to contact us!



Newsroom (6).png

What’s included in newsroom software architecture? [INFOGRAPHIC]

Written by <a href="https://www.linkedin.com/in/joannakocik/" target="_blank">Joanna Kocik</a>, content specialist at Autentika

**Mediahuis got it right: for this media group, editorial analytics is much more than a dashboard only a handful of people can read. Data has been democratised and is the basis for real-time decision-making and future publications. Yves Van Dooren, Data Science Business Partner Newsrooms, told Autentika how the team made it happen.**

An engineer turned media expert. <a href="https://www.linkedin.com/in/yves-van-dooren-1877182/" target="_blank">Yves Van Dooren</a> used to work as a project manager, dealing with the challenges of search engines, social media and newsletter optimisation. Since 1999, he has been working with VUM Media and then <a href="https://www.mediahuis.com/nl/" target="_blank">Mediahuis</a> , one of the leading media groups in Belgium, the Netherlands, Ireland, Germany and Luxembourg, **with more than 30 brands in its portfolio**, including De Standaard, Het Nieuwsblad, De Telegraaf, NRC and The Irish Independent.

Yves describes his role as a business partner responsible for traffic and audience measurement and coordinating various data and insight projects for the holding. Together with the Mediahuis team, **he has created a data framework for journalists and editors in the newsroom** to help them with their daily tasks. 

Now Yves has agreed to share some of his analytical kitchen. Here are 6 pillars of editorial analytics at Mediahuis, described by Yves in an interview with Autentika. 


Yves Van Dooren

Mediahuis developed **its own data tracker** as external analytics platforms did not meet the publisher’s requirements. The team wanted to draw correct conclusions and access the data in real time. 

“Equally important, by using a single tool, **we have a single version of the truth that everyone uses** – so there is no room for doubts and disagreement,” says Yves Van Dooren. As he points out, this creates more confidence in the data and gives Mediahuis complete control over data collection and processing, which is critical in light of the GDPR directive. It also helps the team develop their own data products and roll them out to the newsroom. 



1) Using an in-house data tracker

**The north star metric in the Mediahuis** newsrooms is net attention time. Every ten seconds, the system scans users' activity: whether they are still interacting with the page, scrolling, watching videos, or listening to audio.

To evaluate the quality of the content, Mediahuis uses **the median net attention time**, which is much closer to the truth than the average. 

As Yves says: "What’s also crucial attention time is a metric widely accepted by our newsrooms. This is a major advantage, especially when we compare this metric to page views and clicks."


2) Attention time as a north star metric

The pursuit of click optimisation usually leads to lower-quality journalism, while attention time is a straightforward metric widely accepted in the ‘attention economy’ we live in. 

"Also, the net attention time enables us to value other journalistic formats like video and podcasts properly,” Yves explains.

Each article produced by Mediahuis journalists has a **“business card”** with key metrics juxtaposed with the KPIs, with a real-time preview and data histogram. The team can track the total number of page views, total and average attention time, and changes in those metrics over time.

Another metric that counts is **recirculation**, the percentage of users visiting another page after reading a particular article. This allows the team to examine whether a piece of content is performing well and track which pages readers are likely to visit next. “Of course, recirculation depends on the traffic source – social media or newsletters users tend to stay on our website for a shorter time than those who use the home page as their entry page,” says Yves. 

The key metrics are also the traffic sources and their distribution over time. “With our data tracker, we can track the entire lifecycle of an article and see whether it was included in our newsletter, pushed via a notification, or shared on social media. Then we can analyse how well the article performed in each channel and how many reactions, clicks, and comments it received”, Yves adds. 


3) The story behind the story 

Yves admits that from the beginning, his team’s goal was to **give everyone in the newsroom access to all data** from the data tracker and external sources such as social media, newsletters, mobile apps, podcasts and video platforms. Still, the road to achieving that was not easy, and the team learned its lesson.  

“First, we worked with data engineers to create comprehensive dashboards with information about our content performance and some insights about the competition. We learned a lot from the dashboard experiment but decided to change tactics at some point. **The reason was that maybe 10% of people were looking at these dashboards!”** describes Yves and adds how they tackled the challenge: 

“Using the design thinking framework, we started asking journalists why they were so reluctant to use the dashboards – and we got valuable feedback. For most journalists, the dashboards were way too complicated, and they could not make much sense of them. ‘Just email us or show us the list of articles,’ they told us. ‘And make it fun!’."

4) One-stop shop newsroom analytics 

We understood that as engineers, we simply have a higher affinity for data – we work with it every day, unlike most journalists and editors. That moment was a breakthrough – we knew we had to create something new - and so we began the process of democratising data.


Interviewing the journalists and editors helped the data team see how people felt about the figures they saw. **It turned out that all those metrics and dashboards were way too advanced for them** – while the goal was to make people feel competent and knowledgeable about what they were doing. “We wanted to make the data more accessible and communicate it through the channels people already used, like TV in newsrooms, email and Slack,” explains Yves.



5) Democratising data 

Now, when you walk into our newsroom, you can see real-time data on a big TV screen: How are we doing on Facebook? How are we performing compared to the last four Tuesdays? Do we need a viral post today to drive traffic? What's the attention level today?



"Every time we show insights, we explain what people are seeing. Sometimes we even leave out the numbers and focus on explaining what the data presented means,” he adds. 

Another communication channel Mediahuis uses is email as a daily newsletter. Instead of logging into the data dashboard, **journalists receive key insights directly into their inboxes**. The data team keeps it simple and tries not to overload it with data but lists the KPIs and illustrates the findings with images. 

Mediahuis also introduced author newsletters – **each author gets a personalised email** every Monday to see how their content performs. “This made data a topic in a newsroom and sparked healthy discussions about what stories to write and how to pitch them to the front page”, admits Yves. 


As Yves describes, we can name at least four levels of data analysis:

- descriptive analytics (what happened?)
- diagnostic analytics (why did it happen?)
- predictive analytics (what will happen?)
- prescriptive analytics (how can we make it happen?)

“These levels can help us move from being simply data-informed and having insights to being data-driven and predictive analytics, which creates more value for the business. Of course, it is extremely important for any newsroom to know what is happening in real time, but what we are trying to do at Mediahuis is create suggestions and recommendations for journalists,” he says. 

An example of this initiative is a Slack bot that recommends posts that should be shared on social media. The audience team can then decide whether to accept, snooze or ignore the suggestions – and these decisions feed into the algorithm to make better suggestions in the future.

As he reveals, the data team is working on better analytics for subscription sales according to the user demand model (i.e., which articles sell more subscriptions), headline classifiers (based on headline structure) and gender bias classifiers.

Mediahuis also benefits from the Article DNA – a project supported by the Google News Initiative that defines articles based on journalistic intent and format. Article DNA allows editors to make the best possible mix of story types independent of the topics covered.

6) Invest in foresight analytics

"All this helps us make better editorial decisions, but we do not try to optimise too much. A good news site is not just optimised content. You need a mix of inspiring posts, regular updates, insights and entertainment. Otherwise, you might end up with a super-optimised site that no one reads. Journalism is still 90% news that trickles in and you write about – the key is how to use the full potential of the data at your disposal."


Newsroom (5).png

6 pillars of editorial analytics at Mediahuis – with Yves Van Dooren 

Written by  <a href="https://www.linkedin.com/in/leticiasorg/" target="_blank">Letícia Sorg</a>, a consultant, digital strategist, editor and content producer.



**Letícia Sorg shares her key insights from her experience as a consultant, digital strategist, editor and content producer at top media companies.**


For me, it all started back in 2007. I was caught up in a digital transformation process: trying to engage long-time print professionals with online journalism at a Brazilian news magazine. I was the online editor tackling the challenge of understanding the role of a weekly print product in the digital world. It is not an easy task even today, bear in mind 16 years ago. 

That was the first time I got involved in such an extensive process. It was a defining moment in my career that kept repeating itself in other newsrooms where I worked. First as an editor, then as an executive editor and, most recently, as a consultant. 

Every publication and newsroom is unique in its own way, but, in my experience, there are common obstacles to becoming a **digital newsroom**. In this article, I share **seven of them** and show how we can overcome these roadblocks from the newsroom perspective. 

I believe the journalistic perspective is particularly important for building a solid digital strategy. 

Managers and executives can create financial business plans based on KPIs such as the growth of digital subscribers, but it will come to a standstill if those goals are not translated into new practices in the newsroom. 

The digital transformation of the newsroom is crucial to guarantee the digital transformation of the business.  


Resistance to change **is a human issue rather than a journalistic problem.** It is never easy to realise that what we are used to doing is working for us but not for anybody else. 

This realisation triggers emotional responses, such as insecurity and fear of the unknown. It also raises rational counterarguments, such as lack of time to learn new ways of doing things and probable loss of quality of work. It brings social resistance, too, as it challenges how the group (in this case, the editorial team) perceives itself and its mission and how the work was traditionally organised. 

In my experience, **we could start addressing this resistance by acknowledging it exists and is  natural and expected in any change process.** After embracing the discomfort, it is crucial to figure out the values and goals that guide our actions in times of turmoil – news outlets’ mission and identity towards their audience regardless of the platform. 

1) Disempowering the resistance to change by reaffirming the values

By revisiting what the newsroom is – and what is not – we may help journalists find comfort in new routines. 

Every journalist, depending on their previous experience and current role in a newsroom, **has to find their own way in the digital world**. This process becomes easier when they discover what role this could have in their career. 

Some may discover their multimedia talent by interviewing people for podcasts. They may build a community around a particular topic of interest that could lead them to host events. Others may experiment with different text styles and even gear them towards chronicles or opinion pieces in the more flexible digital world. Everyone could find a suitable personal use of digital openness for their career, apart from the newsrooms they work for.   

**No journalist is guaranteed to embark on the digital journey at the same depth and pace**. And that is OK. We must recognise the best contributions of professionals from different generations and backgrounds to make the most of our teams. We all need patience, persistence, and strategic thinking.

<a href="https://autentika.com/blog/how-can-modern-editorial-software-be-your-journalists-superpower" target="_blank">Read also: How can modern editorial software be your journalists' superpower?</a>

2) How digitisation can change journalists’ careers on a personal level

By reaffirming our mission as journalists – and its centrality in navigating new digital waters – we are also redefining the role of technology in the newsroom: **it is the means to achieve  our goals** (albeit in different forms than before). 

**CMS will not define which story we do. Social platforms will not decide what topics we should post** – even if they can give us hints of people’s concerns. Multimedia editing tools will not decide which stories are better told as video, audio, or an interactive format.

When we learn how to deal with those technological options, we can potentiate our work and get closer to our journalistic mission. Be it to inform, update, explain, contextualise, orientate, inspire, connect, or any other user needs we want to serve.

 <a href="https://autentika.com/blog/let-s-cut-through-the-noise-which-ai-tools-in-journalism-are-really-worth-the-hype" target="_blank">Read also: Let’s cut through the noise: Which AI tools in journalism are really worth the hype?
</a>


3) Tech is only a means that you decide how to use

Recognising the technological power to deliver our message makes breaking journalists’ resistance to adopting new tools easier. **But even for those who instinctively know that technology can unleash great potential, we must ensure we provide all the information they need.**

If lifelong learning is the way for all professionals in the digital age, we ought to make room for training even in fast-paced, real-time newsrooms. **As journalists, we are masters in processing information, making it more accessible, understandable, and useful for our audiences**. Now it is time to apply that same knowledge to internal communication. Q&As, fact sheets, checklists, short videos and quizzes are possible ways to tackle the internal tech training challenge.

Breaking down the once long and theoretical training sections on SEO, multimedia, social media, CMS or AI into shorter practical tips tailored specifically for journalists in your newsroom can help reduce pain and dissolve resistance to change. 






4) Make room for training (and use your journalistic skills to do that)

Another useful training in newsrooms concerns digital analytics tools. To be honest, **good journalists have always had a good idea of what kind of content attracts massive attention from the public**, even when they only had newsstand sales data. By creating audience rankings, software such as Google Analytics, Marfeel, Chartbeat and SmartOcto raise resistance among journalists because they allegedly incentivise an unbridled pursuit of page views (which usually does not equate to quality).

However,  the main issue is not that less than 20% of the content you publish reaches a large  audience – **not every piece will be a hit, and we have known that for a long time.** It is more concerning to acknowledge that **more than 80% of our work does not virtually exist for the public** – a problem not relevant in the print world (a bundled product) provided the sales were growing. 

If looking at the biggest audience hits may create resistance, it is not easier to look at the bottom of the same rankings. It may become less unpleasant when we become aware of  two aspects: 1) we can intervene to improve the results of some content we find relevant (by improving headlines, perfecting SEO, increasing exposure etc.); 2) we can stop creating  some content that is neither editorially relevant nor enticing to the audience.  


5) Use the numbers in your favour

In the digital world, where any newsroom can publish content in real-time in any format (text, audio, video and new interactive narratives), the countereffect is that we produce more and more. Digital analytics tools can help us avoid this exhausting and often unproductive journey.


As I mentioned, **not everybody will enter the digital world at the same depth and pace.** This is not a problem per se – provided we can assure that the digital knowledge is democratised among a fair share of the newsroom. 

Commonly, the team leading the first rounds of digital transformation become the owner of the tools. They become guardians of the history of the software, its bugs, functionalities and never-ending backlogs. They are used to training others, and when they eventually leave the newsroom, they take away crucial knowledge. 

**Sometimes we realise that the problems we are experiencing at work are not due to technology problems but to lack of technological training** – this is even more common when the CMS is old and less intuitive than newer versions. Constantly sharing digital knowledge is the only path to keep the digital newsroom engine running.



6) Don’t leave all the digital tools in a few hands

One of my biggest mistakes during these digital transformation processes was **thinking that we would find a solution to the digital puzzle** – and get back to work as usual. It took me several years to understand that we would never be safe, sound and relaxed on the other side. 

The technological world continues to challenge us with innovations  (AI, anyone?) that lead to changes in audience behaviour, workflows and business models. The same has happened with the rise of the printing press, telegraphy, radio, and TV, before the internet and smartphones.


7)  Rinse and repeat

It is very easy to lose track of our mission as journalists or our identity as an outlet with the rise and hype of many different platforms and resources. 

And whenever we encounter a new roadblock that activates our resistance to change, it can be beneficial to return to the first lesson here and follow through.

Yes, it looks more like Groundhog Day. But we better get used to it.

***


Newsroom.jpg

7 lessons I have learned about digital transformation in newsrooms

**Choosing the right editor for a media company may seem like a simple task. In reality, it is a sophisticated technology where every small action, such as pressing a button, triggers many changes in the program's underlying system. We'll describe how we prepare recommendations for our clients to help you understand what to consider when choosing your next text editor.**

For years, The New York Times team had used a homegrown text editor that didn't quite meet the needs of the editorial staff. The UI left much to be desired, as users had to navigate through a long series of unintuitive and visually unappealing tabs when creating an article. In addition, the editor caused a lot of trouble on the engineering side. The NYT <a href="https://open.nytimes.com/building-a-text-editor-for-a-digital-first-newsroom-f1cb8367fc21" target="_blank">team eventually decided to develop a new editor</a> (called Oak) that provided a more intuitive and flexible code implementation and avoided many of the problems caused by the older editor.

The NYT example illustrates a common challenge newsrooms face. They use old, outdated text editors that are often anything but user-friendly. For instance, they do not allow journalists and editors to insert components of the article inline or see the detailed preview of a piece before it's published. 

If this is also the case at your organisation, and you’re searching for the right technology to build a new text editor, look at how we tackle such challenges and prepare recommendations for the right tech stack. 

 <a href="https://autentika.com/blog/behind-the-scenes-how-we-start-working-with-a-newsroom" target="_blank">Read also: How we start working with newsrooms</a>


First, let’s define typical design principles for the text editor:

1. The editor base will be used for managing blocks and should allow basic interactions, such as adding, reordering, and deleting blocks.
2. Content blocks should be independent modules/plugins with their own content, logic, and API communication.
3. Users can add content blocks independently or use predefined article templates.
4. An article should be a container of blocks with settings defining article category, publication status and date, and where and when it is published.  
5. The available blocks can be arbitrarily expanded with new solutions depending on the current need.
6. Communication with the rich text editor will be done via API.
7. Block data will be sent to the API in real-time and saved/received as a JSON object.
8. We move the styling and formatting of the content to a JSON object and abandon the HTML notation.
9. Saving blocks should result in content rendered as close to the final visual form as possible.
10. The final content should be validated before publishing, including metadata quality, SEO analysis and other criteria. 
11. The text editor must adhere to the publisher's editorial process.



Key design principles 

Basic technology stack

We recommend using Nginx, inherently designed as a proxy server. It allows easy collaboration with the process management application Node.js. It is also software that provides scalability and simultaneous handling of multiple requests and keeps hardware resource requirements low. Static content delivery is twice faster as in the case of competitor Apache2.

As a Node.js process manager, we recommend <a href="https://pm2.io/" target="_blank">PM2</a>. It enables the execution of multiple Node.js applications (e.g. microservices) and offers additional tools for monitoring the client applications.



1) Nginx + PM2

We recommend GraphQL as an API format. Schema data allows the front-end team to work without the significant involvement of back-end developers in providing data. We also considered REST or mixed-mode APIs, but we found GraphQL a more versatile, modern, and convenient solution for our client's project type.


2) API 

We propose to create an editor application based on the React library (or the Next.js framework). It is an application with dynamic content rendering that provides maximum comfort and high performance to the end user thanks to the Virtual DOM.

The basis of the interface would be reusable components with their own logic.

Extensive application capabilities and a large community characterise this environment. It is currently the most popular solution for developers to create a frontend, which is also an important aspect for HR as it allows easier acquisition of developers to support the project.


3) React/Next.js

In the era of widespread use of JS frameworks using templates designed directly in JS, a natural consequence is using a CSS-in-JS solution.

A popular library that facilitates the implementation of this approach is the styled-components library, which is often used in applications written in React.

With Styled-components, we are no longer limited to defining styles that are assigned only to a specific CSS class – instead, the styles are attached to a particular component, making it easy to reuse throughout the application. In addition, <a href="https://xstyled.dev/" target="_blank">xStyled</a> helps maintain consistency with the design system and provides the ability to create styles directly in JSX.


4) Styled components + xStyled

Rich text editor recommendation

Our review of available editor solutions finds <a href="https://lexical.dev/" target="_blank">Lexical</a> to be the preferred option for media organisations. 

**Pros:**

- Conceptually fits the premise of the tool developed for our clients' projects.
- It is framework-independent but natively supports React, a popular library for creating web applications.
- It communicates with the application via an API, allowing for more flexibility in terms of integration with other systems.
- The resulting content is in JSON format, a more structured and standardised way of representing content than HTML.
- Provides a customisable and modular interface.
- Supports real-time collaboration.
- No dependencies for easier integration with other frameworks.
- Has an open-source license.
- It is a successor to Draft.js and adapted to modern developer realities and browsers, providing a more up-to-date solution.
- It contains no dependencies, making it lightweight and easier to use with other frameworks like Solid or Svelte.

A potential downside of Lexical might be that it does not have the same level of community support as some of the more established rich text editors. It may require more development effort to fully customise and extend the editor according to our client's needs.


Lexical 

In our process, we also considered other choices but they didn't meet all initial requirements.


Alternative options

**Pros:**

- Blocks can be easily managed with drag-and-drop functionality.
- Supports inline and block-level formatting.
- Provides a clean and simple user interface.
- The editor's content can be easily exported as a JSON object, which other applications can consume.
- Supports real-time collaboration.
- Has an open-source license.

**Cons:**

- Has limited block types compared to other editors.
- End-to-end product, which may block or hinder the implementation of other solutions created for the client.
- No built-in support for working with images beyond basic insertion and alignment.
- No direct support for React.
- Requires a custom integration with an API for data communication.
- The documentation is not always up to date and may not cover all available features.
- The editor's core library is licensed under a proprietary license, which may concern some users.

[See more about Editor.js](https://editorjs.io/)


Editor.js

Quill can also be an alternative choice for newsrooms, as it offers a substantive API on top of the DOM, providing more powerful and relevant text editing capabilities. It is also highly customisable and supports new formats and content beyond traditional word processor features like bold, italics, and lists. Users can add customisations to embed slide decks, interactive checklists, and 3D models.

However, Quill has limitations that could hinder further development. The product has not been developed for three years, which could obstruct the implementation.

**Pros:**

- Provides a large variety of formatting options.
- Easy to use with a user-friendly interface.
- Supports real-time collaboration.
- Highly customisable, allowing developers to modify its features to suit their needs.
- Offers real-time collaboration features.
- Has a well-documented API.
- Has an open-source license.

**Cons:**

- No longer developed; the last update was in 2019.
- Limited block management functionality.
- Can be slow to load and render large documents.
- Requires additional plugins for more advanced functionality.
- The formatting options in Quill can sometimes be inconsistent, making it challenging to create complex designs or templates.
- API documentation can be challenging to navigate and may require trial and error to implement successfully.
- The community behind Quill is not as large as some of the other options, which may limit the availability of third-party plugins and support resources.

[See more about Quill](https://quilljs.com/)


Quill

**Pros:**

- Highly customisable and modular with various formatting options and plugins.
- Supports drag-and-drop management of blocks.
- Offers easy integration with popular web frameworks.
- Has a well-documented API.
- Has an autosave feature to prevent data loss in case of unexpected events such as power outages or crashes.
- Supports accessibility and is compliant with WCAG 2.0 and Section 508 standards.
- Offers a collaborative editing feature for real-time collaboration between users.
- Has an open-source license.

**Cons:**

- Some users have reported difficulties customising the interface to fit their needs.
- Can be resource-intensive and may slow down performance in larger projects.
- Large size can impact page loading times.
- Some plugins require paid licenses.
- The documentation can be challenging to navigate and understand for some users.

[See more about CKEditor](https://ckeditor.com/)

CKEditor

**Pros:**

- Has a modular structure, allowing easy customisation and extension.
- Supports both Vue.js and React frameworks.
- Uses a renderless component architecture, which doesn't include any pre-defined UI components, making it easy to integrate with any frontend framework or library.
- Provides real-time collaboration.
- Has an open-source license.

**Cons:**

- The learning curve for Tiptap can be steep, especially for developers unfamiliar with Vue.js or TypeScript.
- Limited community support.

[See more about Tiptap](https://tiptap.dev/)

Tiptap Editor

**Pros:**

- Highly customisable and extensible.
- Strong emphasis on data structure and schema definition.
- Uses a modular architecture, making it easy to add or remove features as needed
- Has an active community and ongoing development.
- Has an open-source license.

**Cons:**

- Steep learning curve for implementation.
- Requires more upfront configuration and setup compared to simpler editors.
- Limited documentation and resources compared to some other options.
- Some users have reported performance issues with very large documents or complex schemas.

[See more about ProseMirror](https://prosemirror.net/)

ProseMirror

**Pros:**

- Built by Facebook and used in their products, it has a strong community and support.
- Provides a rich plugin system allowing additional functionality and integration with other libraries and tools.
- Integrates well with React and other popular frameworks.

**Cons:**

- The project is in the extinction stage.
- The learning curve can be steep, especially for those new to React and its ecosystem.
- The API documentation is not as comprehensive as some other editors.
- It can be challenging to achieve real-time collaboration and synchronisation with Draft.js
- The editor's performance can suffer when working with large amounts of content.

[See more about Draft.js](https://draftjs.org/)

Draft.js

After extensive research, we concluded that the best rich text editor for large media organisations should be highly customisable and adjustable to their internal editorial processes and workflows. Among the most recommended options were Lexical and Editor.js.

Eventually, we concluded that Lexical meets most of the requirements:: customisable and modular interface, easy communication via API, and a robust support system with documentation, forums, and a responsive support team.

In our recommendation, we also consider the sustainability of the solution and who stands behind it. If you choose a solution backed by a tech giant like Meta, as is the case with Lexical, the editor is likely to be supported for the long term and attract a large user community. Finally, we look at the reported issues on Github and whether they are actively addressed.

Of course, the choice often depends on very specific needs, the size of the media company and its complexity. If you want to share your experience – we are always open to suggestions and new ideas.



Conclusion

_Watch also: Which AI tools are really worth the hype?_

Newsroom (3).png

How to choose a text editor for a digital-first newsroom? 

**Generative AI has caught the attention of those in the media. But not everyone understands why AI is so valuable and why we are seeing a growing enthusiasm for its implementation. To help clarify the picture, we asked five experts who have been working with AI for years for their key takeaways from recent developments.**

Media companies have been experimenting with using AI to support and produce journalistic features for some time. But since the launch of the latest version of ChatGPT in November 2022, journalists have fiercely debated its potential impact on the news industry. 

"Will AI replace them at work?"
"Is automation always better than human labour?"
"What are possible scenarios for the next 6-12 months regarding further development of AI?"

We have <a href="https://autentika.com/blog/why-media-need-to-learn-how-to-swim-in-the-ai-waters-2" target="_blank">already answered some of these questions in this article</a>, where we looked at media professionals' doubts about introducing AI in their newsrooms. Now, we decided to ask practitioners what they think about the future of media as it relates to AI. Here's what five of them: Charlie Beckett, Aimee Rinehart, Ezra Eeman, Sascha Devigne, and Dennis Dirdjaja told us.


 <a href="https://www.linkedin.com/in/charlie-beckett-2743234/" target="_blank">Charlie Beckett</a> is a professor in the Department of Media and Communication at LSE and founding director of Polis, the London School of Economics International Journalism Institute. Charlie has spent the last five years researching how artificial intelligence transforms journalism worldwide, enabling news media to gather, create and distribute content more efficiently.

He believes **the nature of journalism will stay the same**, despite the influence of artificial intelligence.

"The basic idea of journalism is to provide up-to-date information in a way people can access and understand. That's an exceptional skill and in high demand these days. I last worked as a journalist 17 years ago, but **I could walk into any newsroom today, and within a few days, I'd be a productive journalist again**. I'd have to learn how to shoot vertical videos and TikToks, but that's a matter of an afternoon," Charlie says.

He also points out that despite rapid technological advances, people **will always look for relationships with people rather than machines.**

Charlie Beckett: AI won't change the nature of journalism

 It's technology that allows us to connect with others.


"People subscribe to a newspaper because they identify with it and believe it shares their values. And that's a relationship with a human being behind that medium – not a relationship with an algorithm. Of course, media publishers can use technology to support that relationship. We've seen that with social media: **We love technology, but we love it because it allows us to do social and creative things,**" Charlie concludes.


 <a href="https://www.linkedin.com/in/aimeerinehart/" target="_blank">Aimee Rinehart </a>is the program manager for the Associated Press' Local News AI initiative. Before joining AP, she was deputy director of First Draft's New York Bureau, helping journalists and newsrooms identify, verify and responsibly report misinformation and disinformation in the 2018 and 2020 U.S. election cycles.

"AI has **tremendous potential to build capacity for newsrooms, especially smaller newsrooms facing staff shortages or looking to expand their coverage.** AI can significantly reduce the cost of publishing news, and fierce competition between tech giants like Meta, Google, or OpenAI will lead to price reductions in AI-powered tools. For this reason, **the next 6-12 months will be a wonderful time for people experimenting with AI**, as tests will likely be very cost-effective," says Aimee.

She also points out that AI will complement or enhance the work of journalists – but it will not replace it. 

Aimee Rinehart: The next year will be great for experimenting with AI

It’s my hope and dream that journalists will be needed more than ever to use their analytical minds and good contacts – two things ChatGPT just does not have.

"I hope that journalists will find innovative and creative ways to use technology that may be different from how they have been working but will deliver the same great, and in some cases even better, results. This will allow them to cast a wider net for their reporting," concludes Aimee.


 <a href="https://www.linkedin.com/in/ezra-eeman-8a5ba64/" target="_blank">Ezra Eeman</a> is Change Director at Mediahuis, one of the leading media groups in Belgium, Germany, the Netherlands, Luxembourg and Ireland, with over 40 national and regional news brands. Mediahuis has already worked with several AI solutions, such as text-to-speech automation for audio versions of articles and natural language mechanisms that help detect gender bias.

"AI has already arrived in the newsroom," notes Ezra, "and we now need guidelines on when and how to use it. Can journalists use it to spell check? To detect bias? Can it make suggestions for headlines? AI has many applications besides content creation, about which many editors are concerned. First, automated articles always sound generic, and second, they raise ethical questions. **I think no editor-in-chief will want to take full responsibility for the end result if the content is delivered by AI,"** Ezra adds. 

Eeman anticipates that future AI development **will bring more regulations and guidelines**. At the same time, more proprietary models are likely to emerge, with companies incorporating their data to create more specific and targeted models.

"AI applications will be stacked and interconnected, so we can soon expect to see machines talking to each other and taking over tasks for other applications," says Ezra. **"However, if someone wants to bring AI closer to the newsroom, making journalists a part of this discussion is crucial,"** Ezra notes.


Ezra Eeman: Include journalists in the discussion about AI

Newsrooms should retain control over how AI is used – so it should never be some kind of 'technical decision' or 'commercial decision'. Media should have a framework and know how and why you are using AI.

 <a href="https://www.linkedin.com/in/sascha-devigne-6ba308257/" target="_blank">Sascha Devigne</a> is editor-in-chief of STUDIO 47, a private regional television station for the western Ruhr region and the Lower Rhine, based in Duisburg, Germany. His team recently launched its AI-based product called  <a href="https://www.studio47.de/botcast.html" target="_blank">BotCast</a> – a solution that automates the production of newscasts for the daily TV news and significantly reduces the time required to do so. Several other media companies in Germany have already bought a licence to use BotCast, and more are expected to follow later this year.

"BotCast has freed our journalists from repetitive tasks, so they can now focus more on things that better fit their work. **It's been the best thing that's happened to us in the last few years, and it shows that using AI in journalism (in the right way) will be a big leap forward,"** explains Sascha.

He also emphasises that AI is one of the few chances to secure the existence of local and regional media in the future.

"The economic situation is impacting many regional and local media companies looking for cost optimisation. And since **AI and automation can be a great way to cut costs without losing quality, we shouldn't fear them.** We should embrace it and make the best use of it in our editorial work.


Sascha Devigne: We should embrace technology and use it the right way

The essence of journalism is the art of reducing facts to their maximum density, and if technology can help us do that, we should use it. 

Of course, it's best if we have it in our own hands: That's why it's vital that BotCast was developed by journalists. But the truth is that journalists have always used some kind of technology to produce content. Is the use of AI in the daily journalistic routine different from the use of other technologies and devices?" asks Sascha. 


 <a href="https://www.linkedin.com/in/dennis-dirdjaja/" target="_blank">Dennis Dirdjaja</a> is the Director of Operations at Spektrum der Wissenschaft – a German-based publisher that includes several scientific journals and online newspapers providing the latest scientific news in physics, biology, chemistry, geology, medicine, archaeology, mathematics, astronomy, and ecology. For some time, Spektrum has used AI in various ways: in social media news distribution, translation, and predicting trending topics.

"We're always looking to try new things and explore new possibilities," Dennis says. He points out that automation and AI-powered tools help media save time and money and optimise how they reach different audiences through different channels. 

"I had no doubts about using AI, and to those who are still hesitant, **I can say that generative AI is like a small child – it can only get better if we engage with it and teach it.** To do this, we have to give input, get output, and give feedback - that's the only way AI gets better. If you find that an AI isn't producing good results, don't give up because it won't evolve," Dennis explains.

He points out that we're in the driver's seat when it comes to AI tools. **"It's not something that's completely out of control. If we teach it right, it can be an excellent help to journalists,"** Dennis says. 


Dennis Dirdjaja: We need to teach ourselves to use AI effectively 

Now, more than ever, we need people who know prompt engineering. So not only do we need to teach AI, we need to teach ourselves how to teach machines and how to use our intelligence to do things that computers can't. 

We have been exploring various automation and AI mechanisms that can make journalists’ lives easier for quite some time. If you want to read more about them, 
<a href="https://autentika.com/blog/let-s-cut-through-the-noise-which-ai-tools-in-journalism-are-really-worth-the-hype" target="_blank">check out this article and see how AI works in practice. 



AI concepts worth attention

Newsroom (1).png

The future of media: 5 experts weigh in on the impact of AI

Written by  <a href="https://www.linkedin.com/in/elwira-majewska-715878146/" target="_blank">Elwira Majewska</a> and Grzegorz Wiktorski, UX Researchers at Autentika.

**Let’s admit it: media are complex organisms, and so are the solutions we develop for them. That's why we rely on proven processes and methods when collaborating with a newsroom. We'll show you how we go about the discovery phase – so you can tackle your next digital project with peace of mind.**

_Listen to the audio version of this article or read the transcript that follows below the player_

"We'll see, we'll figure it out later" are probably the worst words you can hear from a team developing a back-office system for your newsroom. You do not want white space in your plan; you want a well-defined scope of work and transparent pricing. That's why at Autentika, we have developed a process that ensures both parties' needs are met. 



We get to know your goals and challenges, and you get a concrete scenario of what will happen.


In this article, **we will share some of our most valuable insights about the discovery phase** – the initial stage of our collaboration. It is a crucial moment for both sides – and that’s because before we start working on a new CMS or editorial software, we need to **drill down to the bone of your system.** We need to learn as much as possible about the existing tools and problems users experience when interacting with them – all to deliver **a functional solution that your team will love and that will help you achieve your business goals.** 

Importantly, our role extends beyond identifying CMS weaknesses and remediating them. The discovery phase and the time we spend with your team allows us to see "the "big picture": processes around tools, workflows, habits and behaviours of users in their natural environment. All that happens in the workplace "around" your tools and software can be critical to developing optimal solutions.

For us, there's no room for "we'll-seeism". We tell you what to expect and give you a clear idea of what's coming next. Of course, in the interest of this text, we generalise and simplify certain things, as collaboration details depend on many aspects – but here is our basic framework.

**Main objective:**

To learn the basics about your media company: numbers, structure, workflows, division of responsibilities, and to define the fundamentals of our collaboration: communication channels, people involved, and tech stack. 

**Some of the questions we ask:**

- What is the organisational structure and the editorial structure?
- How are the roles in the editorial team distributed?
- How many people work in each newsroom, IT and UX /UI departments?
- What are the basic workflows in newsrooms?
- Do you work remotely or in a hybrid mode? 
- What kind of content do you produce?
- What is the daily content production volume?
- What external and internal tools does the editorial team use? 
- What is the team's general opinion about the tools?
- How do you measure the work of the editorial team?
- What innovations or major features have you introduced recently?
- Who will be involved in the project at each stage?
- Who will be the early adopters of the developed project?


**Participants:**

- C-level
- IT department
- Product owner
- Analytics department
- Newsrooms representatives
- Marketing department
- Graphics team
- Design team
- Graphics team

**Why we do it:**

As you migh conclude from the questions, at this meeting, **we lay the foundation for the entire collaboration.** It is critical for us to learn the basic facts about your organisation and understand the newsroom structure, as well as your general tech needs and expectations. We will be collaborating for quite some time, so we need to know who is who, how people work, how tasks are divided, and who the key stakeholders of each project phase are. 

We also decide on the technical setup, communication channels, and software we’ll use, and ask for necessary documentation and access to tools and systems. All this helps us plan our work and present a realistic schedule to the client.


Step 1: Initial meeting

**Main objective:**

To learn the project’s primary goal and grasp the "big picture": the business vision of the project and the motivation for change.

**Questions we ask:**

- What is the strategic business objective of the project?
- What is the motivation for change?
- What has already been done to change the existing situation?
- What challenges and obstacles has the client encountered along the way?

**Participants:**

Key business stakeholders involved in the project. 

**Why we do it:**

This meeting allows us to understand the project's essence and business perspective. We are UX designers – but **to design a good product, we need to understand the business motivation behind it.** We know that sometimes there can be discrepancies between business requirements and user needs – so it's vital to hear arguments from both sides. This allows us to produce a clear scope of work at the end of the discovery phase. 

The discovery meeting also confirms previous arrangements and agreements on details regarding the work calendar and task list.


Step 2: Strategic discovery meeting

**Main objective:**

To learn how the current editorial software and CMS work to determine the "as-is" status.

**Why we do it:**

Documentation analysis helps us shorten the overall research phase. Reviewing existing manuals and instructions can save time because later we do not have to ask our interviewees about them again. The more documentation the client has, the less time we need to spend looking for details.

However, missing documentation is not an obstacle for us – if necessary, we obtain all information directly from the users and interviewees.


Step 3: Documentation analysis 

**Main objective:**

To learn in practice how the back-office system/editorial software/CMS works.

**Why we do it:**

Having someone - usually the product owner - walk us through the system, allows us to see how it "officially" works. This saves us time because we start the survey phase already with a basic knowledge of the tools and the software. Often the surveys reveal much more about the system - shortcuts, exceptions, and workarounds that users use when working with the tool.


Step 4: System demo 

**Main objective:** 

Get an overview of the state of software and processes and learn as much as possible about existing user problems.

**Participants:**

System users: journalists and editors. The size of the group depends on the particular newsroom; it can be between 50 and 150 people.

**Why we do it:**

Surveys are essential for us to grasp, analyse and summarise the main problems of the existing system so that we can better prepare for the next phase of one-on-one interviews. The detailed and exhaustive questions give us a deep understanding of how the current tools work and how users engage with them during their onboarding process. The scope of the surveys allows us to list the most common and burning issues and better use the time needed for IDIs.


Step 5: Quantitative research: surveys

**Main objective:**

To learn more about the users' working environment, to put oneself in the role of the system user.

**Participants:**

- Journalists, editors, desk editors, chief editors, producers (selected by the client)
- People who use the existing system daily
- Employees with both long and short tenure and various levels of work experience

**Why we do it:**

IDIs help us go deeper into the topics we already know from the previous research phases and add an emotional layer. When we talk to people face to face, users are often more open and willing to share their observations and pain points. The fact that we are outsiders helps a lot, as we can notice things invisible from the inside. 


Step 6: Qualiitative research: Individual In-depth Interviews (IDI)

**Main objective:**

To learn other departments’ needs and requirements concerning the future system.

Participants:

- SEO department
- IT department
- Analytics department
- Marketing department
- E-commerce department
- Advertising department
- Video production team
- Photo team
- Design team

**Why we do it:**

Talking to people who will have contact with the future product helps us learn their needs and perspective. Then we can take them into account when designing new solutions. 


Step 7: Department meetings

**Main objective:**

To gain a more nuanced and comprehensive understanding of users' behaviour, needs and problems to complement other research methods. 

**Participants:**

Selected journalists, editors, desk editors, chief editors, producers. 

**Why we do it:**

Ethnographic research is the research part done always physically in a newsroom (on-site). We observe users in their natural work environment (newsroom), which helps us uncover needs and pain points that we may not have caught with other research methods. By more extended and immersive observation and data collection, we get the complete picture of the situation in a newsroom. 

Read more about ethnographic research <a href="https://autentika.com/blog/expert-s-voice-the-power-of-ux-ethnographic-research-in-a-newsroom" target="_blank">here.</a>


Step 8: Ethnographic research

**Main objective:**

To initiate a moderated discussion among members of a selected group of people and gather detailed information on a specific topic.

**Participants:**

Selected representatives from newsrooms/newsdesks and various departments, usually a group of 3-7 people.

**Why we do it:**

Focus group sessions help us better understand problems we have previously diagnosed during the research and delve deeper into specific topics. Those sessions take the form of structured, facilitated conversations. We proceed according to a scenario, which helps participants  focus on a selected topic, activates them, and encourages them to formulate their thoughts clearly.


Step 9: Focus groups

**Main objective:**

To report on our work, summarise current activities and results, and prepare the next steps.

**Participants:** 

Stakeholders involved in the project, our contact persons in particular departments.

**Why we do it:**

Weekly meetings give the client a good orientation on what is going on in the project, what has already been done. They also help us organise our work, plan next steps and choose new participants of planned conversations. Weeklies also give us a chance to flag any obstacles or roadblocks that appear, request additional meetings, or access to software and documentation. 


Step 10 (recurring): Weekly meetings

Each of the above steps is followed by the analysis and summary of the results. We collect all the information, user responses from surveys and IDIs, problem descriptions, and challenges people have with systems and tools.

We include our findings and conclusions in the project documentation so that the client receives a comprehensive synthesis of the entire exploration process.


Discovery phase results analysis

How long does a discovery phase last? The answer is: it depends, but to give you a rough estimate, based on our experience, it's 10-12 weeks. 

The most important result of this time spent with your newsroom is a comprehensive document that includes:

- the synthesis of the problems and challenges of the existing system, 
- tools and processes the summary of our findings and conclusions 
- the basic process modelling 
- the scope and basis of future solutions

As mentioned earlier, the main goal of the discovery phase is to understand the problems your organisation is facing and propose solutions on how to solve them. You'll get a clear idea of what needs to be done next and what the design phase will look like – so both parties know exactly what they need to do and what they need to work on.


Discovery phase timeline and outcomes

Sometimes the flawed onboarding process for new journalists leads to them not knowing CMS properly. Sometimes it's equipment or communication issues.

We want to emphasise that we always strive to develop a solution that addresses a specific problem. Editorial teams are very different – so there is never a "one size fits all" solution. That's why we need all this time and effort – to avoid the "trial and error" method and focus on what is really necessary. 

Being "outsiders" helps us a lot in this process – we're objective and unbiased in our observations, and we aren't here to tell you that you have a faulty system. We're here to develop the best possible solution for you: one that is functional for your employees and helps you achieve your business goals.

<a href="https://autentika.com/contact" target="_blank">Talk to us if you'd like to learn more -- > </a>

In the discovery phase, we spot more than problems with the software. If we observe your employees for a while, we may see more than "insiders." 

Newsroom (10).png

How do we conduct the discovery phase when working with a newsroom?

**Charlie Beckett, the Director of the Journalism AI Project at the London School of Economics and Political Science, shares with Autentika what every media professional should know about AI implementation.**

Few specialists are as experienced in AI in journalism as  <a href="https://uk.linkedin.com/in/charlie-beckett-2743234" target="_blank">Charlie Beckett</a>, a professor in the Department of Media and Communications at LSE, and the founding director of Polis, the London School of Economics International Journalism Institute. He has spent the last five years researching how artificial intelligence transforms journalism worldwide, enabling news media to gather, create, and distribute content more efficiently.

We have recently discussed generative AI which led us to the conclusion that as much as it s the new wave of technological disruption in media (after online and social), this hype – like all technological breakthroughs – also has a less pretty face. Beyond the potential to help media professionals address their challenges, it brings risks and raises questions about things we're still missing. 

Here are seven "hard to swallow" pills (sugar-coated in some cases) from our conversation with Charlie, followed by one fact that brightens up the scene.


Most news organisations do not have all the knowledge, skills, and resources to implement AI. But with some basic AI literacy, it's already possible to be inspired by and learn from others. It would be foolish to expect everyone to become an expert overnight. This goes for both people responsible for innovations in newsrooms and journalists who benefit from (or struggle with) those innovations.

As Charlie told us, we all need to learn AI – just as we learned how to use social media a few years ago. "Journalists haven't thought about AI until now. They thought they'd ask politicians questions, write them down, and publish news. Now they can work with AI, which is almost like having an assistant that you ask, 'Transcribe the audio' or 'Find me a picture that goes with my story' And those are the skills that journalists need to learn: how to prompt AI to give us the results we want," he said.

He notes that we still need more guidelines and playbooks for using AI in journalism, although several organisations have provided their insights and resources. The prediction is that news organisations must work closely with think tanks, technology companies and startups to accelerate AI development and support innovation.


1) We all need to learn 

Many newsrooms have reservations about implementing AI. Most often, we hear that media "do not see the need to invest in AI right now," that they got burned with previous solutions that got buried after a while, or that they simply are not in the right moment to spend time and resources on AI innovation because they're struggling with other challenges.

Read also: <a href="https://autentika.com/blog/why-media-need-to-learn-how-to-swim-in-the-ai-waters-2" target="_blank"> Why media need to learn how to swim in the AI waters</a>

It's true that custom off-the-shelf solutions, in particular, cost a lot of time and money to integrate, test and iterate. Innovation managers need to sift through dozens of tools and choose the right ones, and journalists need to learn how to use them.

According to Charlie Beckett, there are also cultural barriers in the workplace to adopting AI. Journalists and media professionals are afraid of "their jobs being taken over by robots" and fear they'll have more work to do and higher goals to achieve. Adopting AI is a digital transformation – and as such, it's a challenging process. Change is complex, and there is always resistance to it. It takes time and effort to realise that technology is there to solve problems – not to get in the way of anyone's work.


2) Implementation is difficult

<a href="https://blogs.lse.ac.uk/polis/2019/07/17/the-journalism-ai-global-survey-what-weve-learned-so-far/" target="_blank">JournalismAI's 2019 survey</a> of 71 news organisations in 32 countries found that only about a third have an AI strategy. Today, that percentage is likely higher, but still, many media organisations need a more coherent innovation vision. As one survey participant said, "It's not surprising that we don't have an AI strategy – we don't really have a strategy for anything."


3) You need a strategy 

Charlie Beckett: I encourage every executive to have a strategy. They should take time in their calendar and say: 'Okay, this week, despite all the pressure, I'm going to figure out if AI is just hype or if it can be meaningful to me.'

Of course, it's not that simple – you probably already have 50 people knocking on your door every day trying to sell you a new "wonderful tool". In such a situation, you can talk to someone you trust, usually in another media company that is a bit more mature and has more experience with digital innovation, or brainstorm with a trusted consulting team.

Also, think tanks like Polis offer AI  <a href="https://www.lse.ac.uk/media-and-communications/polis/JournalismAI/Starter-Pack" target="_blank">starter packs</a> that you can spend a day with to get a feel for what AI can and can't do for you. "If you haven't done it yet, you're missing out," Beckett says. "And at the beginning, you don't have to be particularly original and start from scratch. You can look at what others are doing and copy them," he adds.

What matters is what you want to use AI for.


If you're going to use it to slash costs or cut jobs, you'll probably get screwed. 

"However, if you're using AI to build new relationships with your audience, launch new products, or get your journalists to do higher quality work, then I think you have a chance of adding value in the end," Charlie concludes.

We have said this more than once, but we will repeat it: AI is not a magic wand that will solve your business problems. And that's precisely what we heard from Charlie: "The idea that you can plug something in and save money within a week is intangible," he said.

Thinking you can replace humans with AI and save staff is also a trap. As some local newsrooms say, using robots doesn't mean they don't need to hire journalists. The opposite is true – they're hiring more people to cover stories that AI can't do.

Of course, AI will likely allow humans to work faster, automate repetitive tasks, and help media manage subscriptions, newsletters, and push notifications. But the decision to implement specific tools needs to fit into your system. It means that when using AI, you need employees who understand it, who know how to use it, and who will benefit from it in their work. Also, remember that AI isn't there to solve your business model problem (no technology can). "However, it can have a big impact on efficiency. And then you have to decide what you want to do with that efficiency," Beckett says.


4) There will be no immediate return on investment

AI risks creating a new digital divide in the media world – this disparity may be more significant than anyone thought. Specific markets and organisations have always had greater resources; AI will magnify that advantage. 

"Bigger organisations will get even bigger," says Charlie Beckett. He predicts that hegemons will buy up small and mid-sized companies, and those mid-sized companies that survive will likely shrink. "On the other hand, I see a lot of specialised, hyperlocal news segmented by genre or audience interest. They, too, have some potential to grow through AI," he says. Another factor is that now English-language media have an advantage over others because AI language models work best in English.

Beckett also notes that some newsrooms had fallen out of the technology race before it began. 


5) Inequalities will persist 

There are a lot of problems in journalism, and most of them have nothing to do with AI. You might be dealing with an authoritarian government, censorship, disappearing advertisers – and those are problems that AI cannot solve.


When we asked Charlie about possible future scenarios for AI development, he gave us a little thought exercise. Now we're going to play it with you.

Go back five years. Think Facebook was still important to journalists as a source of topics and a distribution channel. TikTok didn't exist yet. No one had heard of the pandemic. Hardly anyone was working remotely.

In the last five years, we've seen a lot of things we couldn't even dream of. Five years is an aeon in technology - and it'll be the same with AI.

At the same time, Beckett points out that the core of journalism is providing information in a way that people can access and understand – and that's not going to change. "I bet I could walk into any newsroom today and be a productive journalist again in a matter of days, even though the last time I worked in that profession was 17 years ago," Charlie says. "The tools and systems are different, so I'd have to learn them, but it wouldn't take long. I could probably learn TikTok in an afternoon," Beckett adds. 


6) We don't know what's coming up next

The use of AI in journalism is not without its potential ethical problems. One issue is the possibility of biased algorithms, which can occur due to biased human decisions present in the training data, even when variables such as gender, race, or sexual orientation are removed. Faulty data sampling, where groups are over- or under-represented, can also contribute to bias. 

Another concern is the development of "filter bubbles," where personalised stories fed to users can lead to a lack of content diversity, a narrow view of the world and confirmation bias. While this is not yet a widespread problem, it could become one if media outlets focus on delivering only content that users like. Finally, AI's legal and ethical aspects, including privacy and data security, remain a grey area that newsrooms may need to address in the future. Some may even seek legal counsel with expertise in AI.

7) We'll have to address bias, filter bubbles and ethical dangers of AI

So here's the sweet pill – or a sugar cube that sweetens the whole cup. AI will not replace human journalists. We will not witness a storm of  automated stories spit out by tireless algorithms that never take sick leave and have no children to care for.

"AI will augment the work of journalists. Certain things will be done at scale (my favourite example is horoscopes), but we will still need a human judgement for more complex tasks. People will still make decisions: Is this story correct? Is this headline accurate? We will need a human in this machine loop," says Charlie Beckett.

He also points out that people love to be entertained, learn about the world, and hear what's happening. "And they subscribe to a newspaper because they somewhat identify with it. They feel they can rely on it. And they think it's entertaining and interesting and shares their values to some degree. That's a human thing. That's not an algorithm. At the end of the day, that's a human relationship.

8) We'll still need humans

People love technology, but they love it because they can be creative with it, be social with it, and have experiences with it. That is the essence of human nature.


And with that thought, we'd like to leave you. If you have any comments, we're open to discussing practical applications of AI in newsrooms, proofs of concepts, or other ideas. We're here for you.

_This text was written by a human. We tried to engage ChatGPT to help us, but it wasn't useful at all._

Read also:  <a href="https://autentika.com/blog/let-s-cut-through-the-noise-which-ai-tools-in-journalism-are-really-worth-the-hype" target="_blank">Let’s cut through the noise: Which AI tools in journalism are really worth the hype?</a>


Newsroom (9).png

7 bitter pills (and one sweet) to swallow if you want AI in your newsroom 

Interview with  <a href="https://www.linkedin.com/in/elwira-majewska-715878146/" target="_blank">Elwira Majewska</a> and Grzegorz Wiktorski, UX Designers at Autentika

**AI can help journalists in countless ways and save them a lot of time. However, it's not just generative AI that proves helpful in the day-to-day work of a newsroom. Together with experts from Autentika, we explored concrete use cases and illustrated them with short animations. Have a look!**

_Listen to the audio version of this article or read the transcript that follows below the player_

**Joanna Kocik: In recent months, we’ve heard a lot of buzz about AI-powered platforms like ChatGPT that can help content creators and journalists. We know that newsrooms can leverage AI to produce more news reports, conduct interviews, and do translations. However, AI can be helpful in many other areas, and now newsrooms can have custom-built tools that will make their work more effective. Am I right?**

**Grzegorz Wiktorski:** I'm glad you brought this up. We have been working on design concepts to test how AI solutions could be integrated into our publishing back-office projects. We have focused on existing solutions that can be used or adopted, so our concepts are practical rather than academic.

**Let’s start today with one of the basic journalistic tasks: the ideation process for news stories. How can AI help in this particular flow?**

**Elwira Majewska:** Sometimes, journalists have trouble finding topics that readers like to read and share. This is where AI tools can help, aggregating the latest news from different sources. They can also determine which content is most popular and exciting among users. AI can also indicate the article sentiment and tell us how people react to a particular article, whether they rate it positively or negatively and what emotions it triggers in readers.

**Read also:  <a href="https://autentika.com/blog/why-media-need-to-learn-how-to-swim-in-the-ai-waters-2" target="_blank">Why media need to learn how to swim in the AI waters</a>**

**Grzegorz Wiktorski:** On the other hand, there may be situations where you know what you want to write about, but you don't have strong evidence for your topic. In these cases, you can use an AI-curated data aggregator to gather supporting material from other sources. This approach can help substantiate your topic, similar to what Elwira mentioned, but in a more specific context. 

When searching for social proof for a planned topic, checking if it's hot and trending on social media is essential. If you don’t find additional material, it might indicate that a story is not attractive to readers. 

**Once we have a topic, we can think of giving it structure. It’s where AI can help with content creation –  in which ways?**

**Elwira Majewska:** One of AI's simplest and best-known applications is text writing. We can create a paragraph based on a single sentence or a brief and specify the parameters of the desired text, such as the text length or style: formal or informal.

Another option is paraphrasing. We can change the content of the text without affecting the meaning. Using different sentence structures in your text can offer several advantages. First, it allows you to make your articles more interesting and engaging for the reader. By varying your sentence structures, you can avoid the problem of background paragraphs sounding the same. Second, using different sentence structures can prevent plagiarism.

_Paraphrasing feature added to the editorial system, designed by Autentika:_

**Grzegorz Wiktorski:** We have introduced an algorithm for creating summaries in the article editor. Imagine the situation where you have to write a small abstract  placed at the beginning of the article. Writing a summary can be tedious and repetitive – so this is where an AI comes in, writing a summary for you based on your text, whether you want it as a paragraph or in bullet points for clarity.

Now for another story: Sometimes, a journalist has to write a rather boring text based on external data, right? Whether it's a reiteration of something seen on Twitter or something more heavyweight like writing a financial report based on the latest stock market data. The latter is more challenging, so we tried to solve it as part of one of our projects. The whole process was to feed the tool with financial data and let the AI analyse it, link it to the latest market and financial news on the platform, and create a report that connected all the dots. 

Sounds good?

_Summarising – an AI concept that can be integrated in the editorial software developed by Autentika:_

**Sounds great! What about supporting better SEO writing?**

**Elwira Majewska:** Writing articles optimised for Google usually looked like this: An editor entered a keyword into a search engine and analysed the articles that performed best in the search results.

Today, this work can be done for us by AI. When we enter a keyword into the software, we get suggestions for the number of words, paragraphs and headings. We also get a list of suggested keywords in the article and how often we should use them.

While writing the article, we are informed about the result of our text in terms of SEO optimisation, whether we have already reached the specified number of words, paragraphs and headings and whether we have already used the specified keywords.

_SEO writing assistant integrated with the CMS, designed by Autentika:_

**Once we write a story, we usually need to illustrate it. Today, not all newsrooms hire photo editors and illustrating an article often falls on the person who writes the text. How can photo editing tasks be automated with AI, and how can this technology improve the quality of images used in news stories?**

**Grzegorz Wiktorski:** We’ve heard the stories about journalists who spent as much time searching for images as they did writing articles. Finding a good image to go with the text isn't the whole task: licensing issues, usage restrictions, and the endless sifting through metadata and keywords in digital asset managers can drive many crazy. So why not have an image we envision for this text?


For example, I want a picture of a futuristic car with giant wheels. It doesn't matter that such an image doesn't exist. Artificial intelligence will draw it for you based on a simple description. 

Even better, this image can be customised to match the tone and style of the article, resulting in more visually appealing and engaging content that grabs the reader's attention. It's no surprise that the now-popular Midjourney does this sort of thing without issue. It'll be interesting to see how it evolves.

_Photo generating AI feature designed by Autentika:_

Now, another example related to photo editing. We all know those sci-fi visions from the TV series, where you can enlarge an image indefinitely without losing quality. It may seem irrational, but with today's AI algorithms, this is becoming a reality. Tasks such as sharpening or noise reduction of images after a strong zoom can now be performed quickly and provide high-quality results. And it all works without external tools – we never have to leave CMS or article editor. 

**Going further with the news lifecycle: we have sourced the topic, have written the news, and have generated a picture with some help from AI. Can we now use technology to detect plagiarism or check facts in the story?**

**Grzegorz Wiktorski:** Yes, indeed. Many journalists and editors have used anti-plagiarism tools to avoid plagiarism for quite some time. But in recent years, the rapid development of AI-powered tools like Grammarly and Copyscape has made the process much more efficient. 

To detect plagiarism, these AI-powered tools use advanced algorithms to scan text and compare it to an extensive database of online sources, including news articles, research papers, and other publications. The tools can quickly pinpoint potential problems and help journalists revise their content before publication.



Using these tools saves time and helps gain readers' trust, who expect accurate and reliable information.

**Elwira Majewska:** Suppose we are writing a text about politics and want to be sure who the Minister of Education was in 2000. Artificial intelligence already allows us to check if the name of the person we use is correct. If we make a mistake, it will reveal the facts to us. It will also give us the source of that information.

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