The way people get their news is changing fast – and AI is leading the shift. BBC News recently announced a new “Growth, Innovation and AI” department with a clear goal: to use artificial intelligence to personalize how news is delivered. 

Instead of showing everyone the same headlines, AI systems recommend stories based on each person’s interests and behavior. For PR and marketing teams, getting media coverage isn’t just about landing a great story – it’s also about making sure the right people see it.

A new way to consume news

News outlets have long used a mix of editorial judgment and basic algorithmic tools to organize content – surfacing stories based on factors like timeliness, topic, or popularity. But with the rise of AI-driven recommendation engines, that system is evolving fast. Increasingly, algorithms – not editors – decide which stories appear in front of readers based on each person’s interests, habits, and browsing behavior. If you’ve ever had YouTube or Netflix suggest something “because you watched…” you’ve already experienced how these systems work.

These engines generally fall into two categories. Content-based filtering suggests material similar to what a user has engaged with before, while collaborative filtering looks at patterns across users (“people who liked X also liked Y”). The result: someone who regularly clicks on articles about educational technology is more likely to see similar content rather than a general roundup of tech news.

This approach also means some stories won’t be widely distributed at all. They may end up in tightly defined clusters—highly visible to niche audiences but effectively invisible to the broader public. For PR professionals, this shift raises a new challenge: understanding and optimizing how each platform’s AI decides what content gets seen.

Also Read: AI’s Human Paradox: Emotion Trumps Algorithm

Looking good for the bots

To get seen, content must not only be newsworthy; it must also be algorithm-friendly. Here’s how PR pros can ensure that’s the case. 

Understand each platform’s algorithm

As major news outlets adopt AI to recommend stories to readers, PR professionals need to think beyond getting a pitch accepted – they also need to consider how that story will reach its intended audience after it’s published.

That means doing more upfront research before pitching. What kind of stories tend to get recommended on the outlet’s homepage or app? Are headlines structured in a particular way? Are stories grouped with other coverage in a content “cluster” (e.g., multiple stories around clean energy or AI ethics)? If so, your pitch may need to reflect those patterns in topic and tone.

It also means collaborating more closely with clients or spokespeople to tailor thought leadership pieces for the specific outlets you’re targeting. That might involve including internal links to related articles already published by the outlet, suggesting subheads or pull quotes, or proposing formats (like Q&As or explainers) that are more likely to appear in algorithm-driven story feeds.

In short, the pitch can’t just be good—it has to be formatted and framed in ways that help it be picked up, placed, and surfaced by the outlet’s own technology.

Remember SEO principles

SEO pros know that search engines categorize content based on factors like keywords, metadata, and tags. Based on the same principles, your news content must signal what it is, who it’s for, and how it connects to existing clusters.

That means:

• Include relevant keywords naturally in headlines and copy.
• Provide metadata, like descriptions, transcripts, tags, and alt text, to help the system classify the content. Not all publications may allow this, but you could see more of this soon. 
• Format content to match the platform’s best practices (length, visuals, tone, etc.).

Avoid writing just for the algorithm

It may sound contradictory, but writing solely for the algorithm could be just as problematic as not considering it. Over the years, various search engines have developed “penalties” for people who try to game the system. For example, stuffing in too many keywords can make your article rank worse instead of better. This is because the focus has to be on good writing, not just keywords. 

So, when it comes to recommendation systems, good content still matters above all. Optimizing for the system can be thought of as a delivery mechanism: it gets the right eyes on your content, but only great storytelling earns engagement, shares, and trust.

Also Read: AdTech 2025: AI and Data Drive Marketing’s Future

AI is the next step for media

AI may not be writing every article, but increasingly, AI is deciding who sees what. As media outlets like the BBC shift toward AI-driven distribution, PR professionals must evolve with them. Understanding how recommendation engines work will be essential to getting your message in front of the right audience.

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