After a couple of years of experimenting with various applications of GenAI, here’s an interesting trend we’re seeing: news media businesses creating end-to-end content solutions using GenAI across the content chain.

These are news organisations that have decided to focus on longer-term GenAI projects that can move the needle for them rather than simply seeking quick wins or flashy consumer-facing products.

They are not thinking about disconnected “point solutions” that might solve immediate problems — they are instead building systems that create capabilities for future solutions. 

“I am putting my bets on multimodal retrieval augmented generation (RAG),” said Lyn-Yi Chung, deputy chief editor at Mediacorp’s CNA Digital team and lead for its AI strategy and solutions team in the newsroom. “I don’t see video as just media but as text — and the same for audio and images. We’ve heard a lot about how content should be ‘liquid’ and easily transformed to suit a consumer’s preferences.”

A strong focus on RAG could take pressure off journalists, she said.

“Everything coming in should be meta-tagged, indexable, and searchable. It is the foundation for smarter copy testing and for true story-centric coverage. Imagine being able to quickly pull together resources from every corner across the newsroom — the digital team, television, radio and more,” she said.

The key challenge is overcoming friction between newsroom software and systems. “You can’t trigger RAG across these platforms if you don’t build out data flows and connectors,” Chung said.

“We’ve seen a rush of AI features being unveiled in newsroom software and services. I suggest vendors do a lot more listening this year and work on making their offerings more open and adaptable to AI plug-ins to sharpen their value for media companies.”

Media companies will increasingly be thinking about building a “Swiss army knife for AI features,” despite it being “a crazy ambitious project,” she said.

“It’s like we’re looking at parts of a Ferrari. It’s easy to get distracted by shiny little things. But here’s our chance to build something more powerful.”

This is very much in keeping with the advice that Uli Koppen, who runs the AI + Automation Lab at Bayerischer Rundfunk, shared with the INMA community in our last GenAI Master Class: Build “trojan unicorns,” strong AI use cases that have the solution to a bigger problem “in their belly” — and which help build data infrastructure that can be used over and over again — rather than simply building products.

Similarly, Alessandro Alviani, who leads GenAI at Süddeutsche Zeitung Digitale Medien, explains it this way: “Summaries is something we have been working on. They are now a commodity. I see summaries as a building block. What do you do with the summaries once you are able to summarise? For example, you can match summaries with audio or additional distribution formats.” 

Bauer Media, which publishes more than 200 titles in Germany, the United Kingdom, Poland, and France, is on a similar journey. After launching its five-year-long Imagine programme, which looked at the best ways to use AI tools at the company, the publisher is now embarking on its Embrace programme, aimed at getting its staff to wholeheartedly lean into AI usage.

Bauer has run a slew of experiments, looking at 71 different use cases, and the point of the programme is to bring together different use cases and scale the best ideas.

Embrace is entirely content focused, said Rob Aherne, who runs the programmes: “Embrace spans the whole content-creation process, from ideation to distribution. We could do seven AI projects. But if we’re going to put our chips on something, it is content, because that is the heart of the business.”

The programme is largely internally focused, although some components could end up powering consumer-facing products, such as text to speech projects, which Bauer has used to create podcasts out of archival content.

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