As Social Media Disrupted Distribution, GenAI Disrupts Production - Here's How To Win

If the internet and social media disrupted distribution for publishers, then GenAI is the equaliser, disrupting production.

Previously, the printing press disrupted production of books, and whilst the distribution (sending books by foot or sea) stayed constant the increased number of books had huge second order effects (ever heard of Catholicism?) GenAI has the potential to do the same for the publishing of news.

It’s a double edged sword, a risk and a massive opportunity, as the cost production for content trends to zero the importance of journalistic storytelling, information sourcing and brand voice increase in value.

So… How are the best media groups using GenAI? 

Before that, a quick data detour.

The internet and social media have disrupted journalism (duh), causing a 52% reduction in revenues for newspaper publishers. That’s a lot! 

The internet and social media disrupted individual publishers by aggregating, ranking and personalising news feeds for end-users.

And it’s no surprise with social media adoption in the US reaching 70% of the population that 50% of US adults saying they get news from social media channels.

Really, the hard part of media production is in “production” - creating the content. Once this has been created then distributing it via additional channels seems like a lot less work, but that does not reflect our review of media publisher’s social data.

There’s a huge range of social media presence across media firms:

CNN are doing Youtube right; this short has 204,000 views on production quality content which has been repurposed, now that’s doing double dipping right.

But the future may be slightly brighter, is GenAI the equaliser in production?

Right now the average time to write an article is 2+ hours with summaries, metadata for search traffic adding on another 20 minutes.

And that’s not considering video broadcasting and production, which can run into tens of thousands of dollars per hour. 

The competitors are clear, it’s independent journalists weaponised by Substack, Beehiiv, Youtube and Tiktok.

As mentioned - the last remaining differentiation of media production is journalistic voice and storytelling. The careful integration of GenAI into journalistic workflows can help for 3 key reasons:

1. Create more high-quality content via efficiency gains

2. Better target advertising and increase revenues 

3. Increasing engagement more effectively

Content Creation Efficiencies

At the start of a content workflow is story sourcing.

This is hard to re-create, media organisations have spent decades nurturing relationships to have differentiated access to sources. Most already use AI with social listening using GenAI to find and surface relevant events and details from social media sources.

Next comes content drafting.

It has been the obvious usecase for GenAI but there’s a balancing act between automating content creation and people-led journalism.

And to be honest, this is not new…

Washington Post x AI = Heliograf a “robot journalist” for short articles + local events 2016

Forbes x AI = Bertie an AI powered CMS in 2018

But this wave of AI is different, what happens when GenAI can do (or assist) the things that human editorial staff excel at.

Check out this quote from Wired about Reuters 2018 AI initiative Lynx Insight

“Reuters, says the aim is to divvy up editorial work into what machines do best (such as chew through data and spot patterns), and what human editorial staff excel at (such as asking questions, judging importance, understanding context).”

We expect co-pilots in media to go further to assist digital producers and journalists than ever before. This is an opportunity to scale sourcing with AI that can ask questions and judge importance by understanding larger amounts of data and context.

This is the challenge that all media companies are facing. The answers to:

How do I add GenAI into our processes whilst maintaining our editorial quality?

How do we balance between human writing, which has brand value and story-telling excellence and automation?

We believe that the best examples of integration treats AI content like an initial draft which needs a human review before pushing out.

Éduord Guihaire at AFP says it best:

“It’s important that journalists become familiar with these technologies, use them, test them out, and consider them like a suggestion box,” he explains. “But it will always be humans that supervise, check, and validate.”

The control should be in the editorial process to ensure the content created is correct (AI hallucinations are a thing) and in the right brand voice.

Content drafted by AI is notoriously hard to align with “brand voice” of the journalist or media companies. However, techniques like prompt optimisation and fine-tuning can help solve this to elicit a specific tone-of-voice.

Better ad personalisation 

Specific and personalised ads convert more. Period. And guess where most of media groups income is from? Yep, advertising.

The best of the best media groups create data pipelines, combining content tagging and user behaviour to enable dynamic advertising i.e. matching advertising to more effectively convert users. 

Ever wondered why adverts seem to know you better than you know yourself? Dynamic advertising that’s how.

Media organisations which require sign-in have the strongest metrics on customer interests, alternate privacy-compliant IP and device tracking can assist with user behavioural tracking too.

Here GenAI is being used to:

1. correctly understand or tag content so that advertising can be matched more effectively

2. dynamically create different versions of advertising (copy, images and soon videos) to share at the perfect time and content

The more recent unlock is for videos and podcasts.

GenAI can be used to tag content at scale eg: analysing video transcripts.

Language models  extract key topics and emotions and enabling better targeting of end customers which results in higher return on ad spend (ROAS) which is more attractive to partners.

Increasing engagement more effectively

Engagement, engagement, engagement. I mean other than bringing amazing stories, facts and news to the world, it’s what media businesses live on. 

We feel like brands have not been getting the most out of social media and part of that is because re-creating content for short-form channels  (Youtube, TikTok, Instagram, Facebook) feels like you’re doing the work twice!

GenAI is the “production disruptor”. It speeds up the ability to produce short-form media and re-purpose that golden, golden content across channels. This doubles or even triples the engagement from the original source.

With algorithmically driven social feeds, repurposed content CAN be viral content. Nice one BBC!

Massively increasing engagement to a channel and increasing ad revenues on social media channels can be juicy for media brands, with the GenAI cost reduction it can provide a strong ROI and teams can do more with less.

From content repurposing to AI voiceovers, this AI wave is the gift that keeps on giving.

Broadcast media companies can leverage GenAI tools like Opus Clips to re-purpose content and ElevenLabs to easily voiceover content and share across multiple languages plus automated caption generating increased engagement.

As social media continues to disrupt traditional journalism, traditional media has a potential knight in shining armour in GenAI.

By embracing technology, traditional media can create more high-quality journalistic content via efficiency gains, better target advertising to increase revenues, and enhance engagement effectively.

GenAI enables faster production, improved content personalisation, and targeted advertising, driving both engagement and financial performance. Ultimately, it's about leveraging it to regain a competitive edge.

Pav & Stephen

P.S. if you don’t know me and Stephen - We’ve started Outerop (www.outerop.com) together a GenAI development platform, Stephen was a VP at Fox and I used to help run a Series A startup.