Q: How are publishers using AI in monetisation strategy?
A: The commercial landscape has shifted quickly over the last year: the third-party cookie is finally being phased out, Google’s AI Overviews and AI Mode are answering more queries without a click, referral traffic from search is trending downwards, chatbots such as ChatGPT, Gemini and Claude are becoming a genuine destination for readers, and there is a live industry debate about how, and whether, to charge the AI crawlers that harvest publisher content. Underneath all of that, publishers are rethinking who they are actually monetising.
For most of the web’s life, the answer was simple: you published content, pushed it out, and hoped it earned an ad impression after gaining traction in Google. That is no longer the whole picture. There are now two audiences moving in two directions, the human reader and the machine, and AI is being used to make both of them pay their way.
On the human side, AI is being applied to first-party data, the behaviour a publisher can see on its own pages: what people read, how long they stay and what they subscribe to. This analysis used to be manual and shallow; now you can run it across an entire archive to build audience segments that lift advertising yield, flag readers close to subscribing or at risk of lapsing, and match the right offer to the right person. Because this data is owned rather than bought, it survives the loss of the cookie and describes intent more honestly than anything you can buy in.
On the machine side, AI crawlers, answer engines and increasingly autonomous agents are consuming content on a reader’s behalf, and often citing it without sending anyone back. Publishers are starting to use AI traffic tools to see this traffic clearly, decide which content those systems may access, and charge for it through licensing deals or metered, pay-per-crawl access rather than letting their content value be stolen.
A lot has changed, but the core principle has not. Monetisation works best as a two-way relationship managed at source, which is why the tagging, permissions, pricing and measurement that once sat in a separate ad-tech stack are increasingly handled inside the content management system, alongside the content and the people who create it.
Q: What is a particularly illustrative use-case?
A: A useful example is a publisher that stopped treating its human and machine audiences as two separate problems and started running both through a single view inside its CMS.
On the reader side, they captured first-party engagement directly from their own pages rather than relying on third-party analytics. That data fed audience segments advertisers could buy against, and the same segments told the subscriptions team who was worth a hard prompt and who was better left alone. Yield went up because the inventory was better described, not because there was any more of it.
On the machine side, the same system can be used to identify AI crawler traffic, decide which sections those bots could reach, and expose it on clear terms: some free, some licensed, some metered. Content previously scraped for nothing became either a paid feed or a deliberate, measured decision to allow.
There were three main learnings. First, the value sits in the structure and the governance at the point of creation; if content is tagged, rights-flagged and priced as it is published, both audiences can be served automatically, whereas if it is not, you spend a lot of time and effort reconstructing that context downstream. Second, editorial has to be part of it, which means the tools belong next to the content, in the system journalists and editors already use, not in a separate revenue platform nobody in the newsroom ever opens.
Third, you have to measure both audiences on the same terms, because it is easy to optimise ad yield while quietly handing your whole archive to crawlers for free, and only one of those shows up in a traditional revenue report.
Q: What are your three top tips?
- Treat the bots as an audience, not a threat. AI crawlers and agents are now a real channel, so blocking everything leaves money on the table while allowing everything gives your work away; decide content by content, what is blocked, what is licensed and what is priced, and manage it where the content actually lives.
- Own your first-party data and act on it. The signals your own readers generate are more durable and more honest than anything you can buy, but they only earn their keep if your editorial and revenue teams can use them in the system they already work in, rather than waiting on a long integration.
- Keep the monetisation tools next to the content. The biggest gains tend to come from having a clever algorithm and from closing the distance between publishing a story and deciding how it earns, so that AI can help at the point of creation and monetisation becomes part of publishing rather than another department’s problem, ideally all in your CMS.
About us
Bright Sites is a leading tech company producing innovative content creation systems and newsroom tools for publishers, including our flagship product, Flow, an AI-enabled publishing platform. Also, we make Storycue, which is a news gathering tool monitoring social and search and which presents unique opportunities for your publications based on your data and style guide.
Email: brian.alford@brightsites.co.uk
Tel: 07719 019091
Website: www.brightsites.co.uk
Website: www.storycue.com
Brian and the other contributors to our AI Special will be taking part in an ‘AI Special – Q&A’ webinar on 24th November. Click here for more information and to register.
This article was included in the AI Special, published by InPublishing in October 2026. Click here to see the other articles in this special feature.
