Login is restricted to DCN Publisher Members. If you are a DCN Member and don't have an account, register here.

Digital Content Next logo


InContext / An inside look at the business of digital content

How media companies can stand out in an AI age

Everyone in the media industry has thoughts about Google introducing AI to search. Here’s what you can do to protect your business in this shifting landscape

June 26, 2024 | By Doyle Irvin, Senior Product Marketing Manager – WordPress VIP@DoyleIrvin1Connect on

The introduction of AI-generated search results is just the next step in a long line of the platforms moving more of the audience interactions behind their walled gardens. This is an accelerating trend that’s not going to reverse. Google began answering common questions itself in 2012, Meta increased its deprioritization of news in 2023, and now some analysts are predicting that AI search will drop traffic to media sites by 40% in the next couple years.

It’s a dire prediction. Panic is understandable. The uncertainty is doubled by the sheer pace of AI developments and the fracturing of the attention economy.

However, it is important to know that this is another situation in which it is critical to focus on the fundamentals. Media companies need to develop direct relationships with audiences, double down on quality content, and use new technology to remove any inefficiencies in their publishing operation. Yes, the industry has already done this for decades. However, there are new approaches in 2024 that can allow publishers to improve experiences to attract direct audiences. 

All-in on direct relationships

When there’s breaking news, is the first thought in your audience’s mind opening your app, or typing in your URL? Or are they going to take the first answer they can get – likely from someone else’s channel?

Some media companies view direct relationships as a “nice to have” or as a secondary objective. If that’s the case, it’s time to make them a priority. 

Whether direct relationships are already the top priority or not, now’s a good time to take a step back to re-evaluate the website’s content experience and the business model that supports it. Does it emphasize—above all else—providing an audience experience that encourages readers to create a direct relationship with your business? 

When the cost to produce content is zero, quality stands out

This brings us to the avenue that drives direct relationships: your website, and your app. Particularly as search declines as a traffic source, these become the primary interaction space with audiences. We’ll follow up next month with frameworks for your product team to use to make your website and apps more engaging to further build your direct audience traffic. 

It’s no longer about competing for attention on a third-party platform—for example through a sheer quantity of content about every possible keyword. It’s about making the owned platform compelling. Quality over quantity has never been more important. 

Incorporating AI into editorial workflow

As the cost to create content is increasingly commoditized via the large language models (LLMs), the internet will fill up with generic noise—even more so than it already is.

Content that’s actually of genuinely high quality will rise in appreciation, both by readers themselves and the search engines that deliver traffic to them. Google is already punishing low quality content. So are audiences. The teams using LLMs to generate entire articles, whole-cloth, are being downgraded by Google (and this approach is not likely to drive readers to you directly either). 

But AI does have its uses. One big challenge in generating quality content is time. Ideally, technology gives time back to journalists. They’ll have extra time to dig into their research. They may gain another hour to interview more sources and find that killer quote. Editors have more time to really make the copy pop. The editorial team has more time for collaborating on the perfect news package. The list goes on. 

AI is perfect for automating all the non-critical busywork that consumes so much time: generating titles, tags, excerpts, backlinks, A/B tests, and more. This frees up researchers, writers, and creatives to do the work that audiences value most, and deliver the content that drives readers to return to websites and download apps. 

This approach has been emerging for a while now. For example, ChatGPT is great at creating suggestions for titles, excerpts, tags, and so on. However, there’s a new approach that’s really accelerating results: Retrieval Augmented Generation (RAG).

RAG is the difference maker when it comes to quality

The base-model LLMs are trained on the whole internet, rather than specific businesses. RAG brings an organization’s own data to AI generation. Journalists using ChatGPT to get generations will get “ok” results that they then need to spend time fixing. With RAG, they can focus the results to make sure they’re fine-tuned to your particular style. That’s important for branding, and also saves creatives time to use for other things. 

The next level not only uses content data, but also performance data to optimize RAG setups. This way, AI is not just generating headline suggestions or excerpts that match a particular voice, it’s also basing them on what has historically generated the most results. 

In other words, instead of giving a newsroom ChatGPT subscriptions and saying “have at it,” media companies can use middleware that intelligently prompts LLMs using their own historical content and performance data.

Do this right and journalists, editors, and content optimizers can effortlessly generate suggestions for titles, tags, links, and more. These generations will be rooted in brand and identity, instead of being generic noise. This means the team doesn’t need to spend time doing all that manually, and can focus on content quality. 

Using RAG to leverage the back catalog

Media companies have thousands upon thousands of articles published going back years. Some of them are still relevant. But the reality is that leveraging the back catalog effectively has been a difficult undertaking. 

Humans can’t possibly remember the entirety of everything an organization has ever published. But machines can. 

A machine plugged into the CMS can use Natural Language Processing (NLP) to understand the content currently being worked—what is it about? Then it can check the back catalog for every single other article on the topic. It can also rank each of those historical articles by which generated the most attention and which floundered. Then it can help staff insert the most high-performing links into current pieces.

Similarly, imagine the same process, just in reverse. By automating the updating of historical evergreen content with fresh links, new articles can immediately jump-start with built-in traffic. 

Removing silos between creation and analysis

While Google traffic might be declining, it will nonetheless remain important in this new world. And in this period of uncertainty, media organizations need to convert as much as possible of the traffic from this channel while it is still operating. 

We call this “Leaving the platforms behind.” Media companies should focus on getting as much of the traffic from search and other channels into first-party data collection funnels as possible. This way, they can build enough moat to continue floating if any or all of these traffic channels completely disappear. 

Most teams today have dedicated SEO analysts who are essentially gatekeepers between SEO insights and content production. The SEO analysts aren’t going anywhere any time soon. But the new table stakes are that every journalist needs to be able to self-serve keyword insights.

It is important to use analytics tools that bring search console data directly to the approachable/easy article analytics page that the editorial team already knows how to use. Ideally, analytics tools should connect keywords and other platform traffic to conversions, so everyone on your team can understand their impact on leaving the platforms behind. 

Done well, you’ll create a feedback loop that evolves and improves your content in a way that resonates with readers and machines.

Quality is all that matters

This is not the first “all hands on deck,” moment for the media industry. That being said, what we’re seeing is that the barometer of success is a truly aligned strategy and execution that brings product, business development, and editorial teams together to pursue creating first party relationships with audiences. The organizations that have little brand identity, and pursue traffic instead of subscriptions, are suffering—and will likely continue to do so.

Liked this article?

Subscribe to the InContext newsletter to get insights like this delivered to your inbox every week.