AI in the labor market: lessons from journalism

On April 3, we met for a live session with Yvonne Pöppelbaum from tactile.news. The session was moderated by Nicole, who first welcomed everyone and briefly introduced the video conference in our AI Compass think tank.

Media change in the age of AI

Anja then gave a brief introduction to place the transformation of journalism in the wider context of digital transformation. Artificial intelligence could potentially be the defining basic innovation for future economic trends. And thus change and realign the entire global economy.

Moreover, if we look at the historical development of the digital transformation of media in the entertainment segment

and in the news section

it could well be that we have reached the end of the social media age “as we know it”.

The “age of synthetic social media” has begun – content and interactions in social media will increasingly be generated artificially by AI in the future. And that will put our democracy to the test.

The changes in the labor market will also be considerable – and continuous learning will be essential. Yvonne told us about her experiences in journalism and how to deal with them. And now we let Perplexity.ai and ChatGPT help us to summarize the session with our manual questions in the interactive change.

Change in journalism

• Journalism is undergoing a radical change due to the development of generative AI. Many jobs are being cut and content is increasingly being created automatically. Yvonne used the example of a recipe booklet from Burda, which was sold for €2.99 without the recipes being tested, to show how journalism will change as a result of AI. Such automated content without human quality control will increase – and can lead to a shitstorm. Finally, the editor-in-chief was dismissed by the publisher.

• Yvonne emphasized that trust and communities will become even more important for media in the future. One quote reads:

“There is greater trust in this network and the creation of communities is one of our key priorities.”

• It is important that journalists continue to provide trust and orientation by recognizing and classifying disinformation. This requires further training.

• She recommends that journalists “look, try, experiment”. She also shows examples of experiments with AI that her agency has carried out, e.g. creating front pages of local newspapers with AI or “talking” to paintings in museums.

• According to Yvonne, the key is to focus on user needs and make suitable offers, e.g. messenger magazines for specific communities. This requires a precise analysis of the target group using models such as the “User Needs Model”.

• AI can support journalism in many areas, e.g. in research, personalization, translation and interactive storytelling. Personalized news feeds are expected in the long term.

• Journalists should get to grips with AI, experiment and focus on user needs, e.g. through tailored messenger magazines for specific communities.

• David then briefly demonstrated in a quick Perplexity.AI run-through how to use this tool for researching and analyzing larger files.

• CONCLUSION: Experiences from journalism, which has been undergoing the digital transformation for some time, can also help other sectors to adapt to AI.

What exactly can non-journalistic media professionals learn here?

Media professionals from other sectors can learn valuable lessons from journalism to increase their visibility and raise awareness of their products or services. Here are some key strategies:

• Storytelling: Journalists know how to turn complex content into stories. Media professionals can use this technique to build emotional connections with their audience and make their messages memorable. Good stories stick and can strengthen brand loyalty.
• Understanding target groups: Journalists are experts in tailoring their content to the interests and needs of their readership. Media professionals should analyze their target groups in detail and understand which topics, formats and channels are best suited to address them effectively.
• Build credibility and trust: Journalists attach great importance to fact-checking and citing sources in order to maintain their credibility. It is also essential for media professionals to communicate in a trustworthy and transparent manner. This creates trust and strengthens the relationship with the audience.
• Adaptability and innovation: Journalism has changed dramatically as a result of digital transformation and the integration of technologies such as AI. Media professionals should also be open to new technologies, formats and platforms in order to distribute their content effectively and reach new audience segments.
• Interactivity and engagement: Journalists are increasingly using interactive elements and formats to increase reader engagement. Media professionals can use similar techniques to encourage dialog, gather feedback and build a community around their brand.
• Ethical standards and social responsibility: Journalism follows ethical guidelines that ensure the integrity of reporting. Media professionals can adopt these principles to emphasize their social responsibility and make positive contributions.
• Continuous learning and adaptation: Journalism is constantly faced with the challenge of adapting to the rapidly changing media landscape. Media professionals should also be prepared to learn continuously, follow trends and adapt their strategies accordingly.

By adopting these journalistic principles and techniques, media professionals can increase their visibility, communicate more deeply with their audience and ultimately market their products or services more successfully.

What is the main difference between content and media work in the AI age and the time before?

Before the influence of generative AI, the focus was more on:

1. Manual content creation by human authors and journalists, without the support of AI tools.
2. Emphasis on the expertise, creativity and judgment of content creators as a unique selling point.
3. Traditional SEO methods such as manual keyword research and optimization.
4. Linear, less personalized content formats for a broad audience.

Following the massive influence of generative AI, the focus is increasingly on:

1. Strategic use of AI tools to increase efficiency in research, content creation, personalization and optimization.
2. Combination of human expertise and AI support, with AI being used as a supplement rather than a replacement.
3. Use of AI for data-based content recommendations, target group analysis and automated SEO optimization.
4. Creation of customized, interactive content formats for niches and communities using AI.
5. Continuous training of content creators in AI technologies and adaptation to new possibilities.
6. Greater emphasis on trust building, transparency and ethical use of AI due to risks such as disinformation.

In summary, generative AI has shifted the focus from purely manual work to AI-supported, data-based and personalized content marketing. However, human skills remain essential for quality assurance and must be supplemented by AI expertise.

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