Imagine a world where journalists spend less time on repetitive tasks and more time on what they do best: creating original, impactful stories.

Are AI-driven editorial tools the future of journalism? Regardless of the significant impact they may have, implementing them effectively can be quite challenging — just like any change affecting work methods.

On one hand, there may be technical issues. And, on the other, there can be resistance or fear of using new technology, which might be associated with losing control over one’s content.

So why is it worth trying?

The demand for relevant content is huge, and traditional editing methods are time-consuming. Journalists now manage not only creating content but also tasks like SEO, keyword optimisation, and content linking, which can detract from storytelling. AI tools can automate these tasks, freeing journalists to focus on their core work.

Additionally, AI can provide insights into reader preferences and improve content to meet audience needs and help to grow media businesses.

In this article, I want to offer some practical tips on how to implement AI editorial tools in your newsroom workflow with success based on the experiences we’ve had at Ringier Axel Springer Polska.

How to implement AI-driven editorial tools

If you’re reading this article, you likely already have an understanding of your business needs regarding AI tools. If not, that should be your first step. Identifying pain points in your editorial workflow will assist you in selecting the appropriate AI tools.

The next step is to choose the right tools and integrate them into your system. This will probably take some time. This comes with several important considerations, like authorship and copyright issues as well as the potential risks of losing reader trust and the security of chosen tools.

However, the most important thing will be the implementation of AI tools in your existing process and helping your editorial team to use it.

Any change raises concerns for many people. According to E.M. Rogers’s Diffusion of Innovation Theory, only 16% of employees will easily and willingly adopt a change. One in three employees will be slow to follow the change, needing good justification, examples from others, training, and time to trust the new solution. The last 16% of employees will be so-called laggards, reluctant to adopt the change, finding 1,000 excuses not to change their habits.

Case study: Ringier Axel Springer Polska

Ringier Axel Springer Polska is one of those media companies that can serve as a great example of effectively implementing AI tools in editorial teams.

We use Ring Publishing’s AI Editorial Tools, which automate repetitive editorial tasks like tagging, writing bullet summaries, and creating multiple headline versions, which is useful for personalising content and tailoured recommendations. We also have AI assistants, which help with optimising content for specific needs and better SEO.

By integrating AI solutions directly into the editorial workflow, the tools provide a seamless user experience, allowing editors to work more efficiently without switching between applications.

How Ringier Axel Springer made this change

The key was understanding that implementing this change is not “business as usual.” It required redefining the entire development process for the developers and the editorial team. We wanted to make the change quickly and efficiently, without compromising on security and the daily work of the editorial office.

“Very importantly, the tools are integrated into our journalists’ work environment. Editors doesn’t have to switch to other windows or log in to external tools; everything is conveniently accessible in one place, seamlessly integrated into their daily workflow,” said Michał Fal, digital growth manager of strategy at Ringier Axel Springer Polska. “Furthermore, these tools have been developed in direct collaboration with the editors, ensuring they respond well to our needs and can be improved or adjusted based on our feedback and any new challenges that arise.”

We also didn’t want to implement tools that would not help the editorial work and would merely follow a trendy buzzword. To achieve this, it was necessary to find a common perspective in the tool development process.

What did this mean in practice?

Early involvement in the process: The editorial staff members must be genuinely present in the process from the very start. They are the ones who know whether a given idea for a tool will significantly affect the way they work, and whether it will be a positive or negative change. Additionally, it is harder not to test and not to use a tool that you participated in creating and had a say in how it should work.

Comprehensive training and ongoing support: Meet people, explain everything multiple times, and listen. What if you have great training, and you think the tool is easy? Well, you’re not the one using it. Organise training sessions that help the team familiarize themselves with the tools. Provide additional materials such as video tutorials. And, finally, stay up to date with questions and concerns.

Good measurement: Set your KPIs and measure your progress. Good analytics are necessary for knowing you are investing in what you should be investing in. Are you seeing the desired result? Often, we just make assumptions that something may or may not work. It’s important to review metrics regularly and not be afraid to redefine the project if it’s not meeting the goals.

Creating a feedback loop: Establish a continuous feedback loop to address any issues or concerns. Regularly check in with your team to gather feedback and make necessary adjustments. Consider installing a feedback mechanism within the tool. Be proactive in this as much as possible. This will ensure the tools are being used effectively and meeting your newsroom’s needs.

Having AI leaders who buy in working with editorial teams: To effectively integrate AI into editorial teams, it’s crucial to have AI leaders who can champion the technology and its benefits. These leaders should work closely with editorial teams, or even be part of these teams, to understand their needs and challenges, and to demonstrate how AI can enhance their workflows. Above all, keep in mind that implementing AI is not a finite transformation but a constant change management process. If you want to make it right, it must be ongoing and carried out with attention and care about your editorial office.

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