Trust-as-a-service: Media brands in the age of agentic AI

It starts with an uncomfortable question: Why do we still need a media brand? Why listen to Deutschlandfunk or read the Tagesspiegel when AI can curate everything we need to know in the morning – faster, more personalised and straight to the point? This question is keeping media organisations up at night. And they should ask it before the speed of the market answers it for them. Because markets are rarely forgiving.

For a long time, it was clear who was competing with whom: newspaper against newspaper, podcast against podcast, streaming service against broadcaster. Everyone knew the rules of the game—and who they were playing against.

But now, the rules have fundamentally changed. Large language models have placed themselves as powerful intermediaries between media providers and users. They compile news, provide recipes, recommend travel destinations, summarise data, and so on. In short: they are taking over exactly what we have used our ‘relevant set’ for – from tagesschau and rbb to RTL and Wikipedia. I used to ask myself: who do I trust on this topic? In the past, brands were the ones that shaped who we trusted. Today, that power is increasingly in the hands of language models.

Agentic AI goes even further. These AI systems not only answer questions, but also plan, act and carry out tasks independently. For many users, these systems are becoming personal companions delivering news, opinions, services and entertainment. In this process, the media brand loses the connection to its content and essentially disappears. Yet visibility has always been the fundamental baseline for brand relevance. That hasn't changed in the AI age.

Ezra Eeman (Strategy & Innovation at NPO Netherlands) sums up this dual challenge: “Put AI in your media brand—and put your media brand in AI.”

The former is already happening. Media companies worldwide are automating editorial workflows, speeding up content production and experimenting with generative tools for text, images and audio. This is an absolute necessity for operational efficiency.

The second challenge is strategically more complex: How do you get your media brand inside the AI? How do you remain visible, unique, and trustworthy?

Media companies need to understand how language models assess them. The ‘Algorithmic Brand Gap’ must be closed to ensure visibility. In other words, the gap between what a brand thinks of itself and what machines think of it. Anyone who ignores this gap will disappear from the system entirely. Which attributes do large language models (LLMs) associate with your brand? In which context does it appear, and where is it missing? How is it positioned in comparison to the competition? Where are the blind spots?
These questions may sound technical at first, but at their core they are pure brand strategy. Brand management within organisations has to master this new discipline. This often requires new roles and skills to analyse and manage the brand within LLMs.

As AI-generated content floods the web (often called content slop – an oversupply of generic, hard-to-verify content), reliable sources become more valuable than ever. This isn't just wishful thinking; it’s simple logic. When generic content is everywhere, trust becomes a rare commodity. Barry Schwartz’s 'Paradox of Choice' explains why: at a certain point, having too many options doesn’t lead to better decisions—it just leads to overload and frustration.

In a world of endless content, people are feeling overwhelmed and are looking for guidance. This is a massive opportunity for independent journalism. At diffferent, we see three ways well-managed media brands can add real value in the age of AI:

1. Reducing complexity: Clear brands are easier to find. They get mentioned, recommended, and featured more often—by humans as well as AI models.
2. Building trust: A strong brand reassures people that the news behind it is accurate and reliable.
3. Remaining distinctive: Competitors can copy your facts, but they can't copy your point of view and editorial voice.

In the past, media companies relied on big campaigns or the occasional flagship project to build their brand, usually separate from daily reporting. That was sufficient as long as the brand and its content appeared on the same platforms where users could find their way directly to the source. However, AI has changed the game. How can we develop and demonstrate a clear brand stance? Our three strategic questions will help:

Definition: What does our brand stand for – beyond the content? What makes us unique that cannot be replicated on a large scale? After all, if we cannot explain our brand in one sentence, how can we expect an AI to do it?

Access: How do we make our brand AI-friendly? Are our core values clear enough for an AI to actually understand and use them? Is our value proposition structured so AI systems can easily find and quote it (GEO as a new brand management discipline)?

Experience: Where and how do we bring our brand to life every day? Which touchpoints do we control directly; where do we reach our users without being filtered by AI? Is our unique perspective visible in every single article, podcast, and video we produce?

Those who view the brand not as a marketing message but as a guiding principle for all actions will find new opportunities for differentiation in the AI era. After all, the future of content isn't about volume; it’s about credibility.

AI platforms will eventually need verified, reliable sources to keep their own output accurate. Media brands that prove their trustworthiness every day will become the "Trust API" of the future—moving from simple content providers to the definitive source of truth.

Content sales – whether through public service broadcasting or paywalls – will increasingly become ‘Trust-as-a-Service’. People will not pay for the content itself anymore; they pay for reliable information, guidance, and the peace of mind that comes from feeling understood.