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Artificial intelligence (AI). Just saying the words invokes visions of an apocalyptic future teeming with deadly machines like The Terminator or even software like The Matrix's Agent Smith. At least that’s the dystopia the scaremongers are peddling. If the latest hype is anything to go by, AI will not only change life on earth as we know it, it will probably take your job too.

As an editor, content marketer and millennial, it appears my head is on the chopping block. Gartner predicts that by 2018, 20 per cent of business content will be authored by machines, and many are speculating that journalists will cease to exist. Add Elon Musk comparing AI to a demon, and even I’m spooked.

But I won’t pack up my desk just yet. Here’s why.

We’re surrounded by AI

Let’s be honest: this is nothing new. Artificial intelligence, machine learning and automation have been around for quite a while, and we’ve all been targeted by Facebook’s AI-applied targeted advertising and subject to Google AdWords’ AI-powered, automated bidding for years.

Your top picks on Netflix? AI technology fuels its recommendation engine. Apple’s personal assistant, Siri? She’s machine learning to better predict, understand and answer your questions. Google? Depends on AI to rank your search results.

But the machines haven’t taken over yet. Despite it trickling into everyday life, AI is still in its infancy. Instead of conjuring images of alien robots, we should really think of the technology as a baby Bicentennial Man in nappies – waiting for us to teach it.

AI is growing up fast

To be useful for content marketing, AI needs a mammoth amount of fresh, structured data.

Its power lies in its ability to analyse large data sets to reveal patterns and trends. Feed it enough high-quality data and it will be able to predict share prices or a human's lifespan and, in some cases, even write content.

Natural language generation (NLG) is a type of AI software capable of producing coherent, readable text. NLG robo-journalists are already creating basic sports content and corporate earnings reports. But, as smart as it is, NLG isn’t truly independent – it needs very specific data sets and templates before it can write, and it can’t create anything genuinely new.

Still, that doesn’t mean we can’t use the technology. In the realm of content marketing, AI can gather, sort and make sense of oceans of data – something the industry is swimming in.

AI: Spotting trends, making predictions

Ask any marketer and they’ll tell you they’re ‘data driven’.

Sure, we’re data driven. We look at engagement metrics to tell us what’s working, and change things accordingly to make them work better and inform future decisions. But it’s generally retrospective.

A lot of what we do is still based on instinct. We still speak to real people. We still search online to understand what people are asking. We still study search volumes.

What we need is the ability to predict something before it needs to be changed. This is where the opportunity for AI is in content marketing right now.

Exciting stuff for a content marketer working in a media and data intelligence business. We’re already using our own AI to process seven million news items every day, at a rate of 234 stories per second.

With that much data, our software can make strong recommendations about what type of content we should be creating, and for whom. As it evolves (and learns), it should be able to spot trends and patterns early, informing communications strategies and helping businesses to maximise opportunity and minimise risk.

Humans and AI, living together

AI and predictive analytics will help content marketers understand who they should be talking to and what they should be saying, but it’s up to us to create the content.

AI relies on human data and intelligence to function and learn. At least for now, this is where its limitations lie.

Humans are still needed to create original work that connects with its audience at an emotional level. To completely replace a writer or content marketer, AI would need to have an opinion, think abstractly, be curious and show emotion.

So, while your inbox might be full of propaganda alluding to our impending cyberdoom, we’re not there yet.

However, we shouldn’t be naïve, as the way we work is being transformed. To stay in the game, we should spearhead the change rather than hiding in the corner.

I for one welcome working with our new robot overlords, and I urge you all to join me. As the machine said, “Come with me if you want to live.”

Disclaimer: This article was not written by a robot.

​

Paige Richardson, Isentia Strategy & Content

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If you ask ChatGPT or Gemini about your organisation today, the answer won't come straight from your website. Instead, it uses sources the model already trusts, which are often months or years old. So if your last big mention was a crisis or a controversy from 2023, that's probably still how AI describes you.

This is the tough reality for anyone working in PR and communications today. More people are getting their first—and sometimes only—impression of your organisation from an AI-generated summary, not from search results or the homepage. And these summaries often rely on outdated information.

What does freshness actually mean?

Content freshness refers to how recent the sources are that an AI model uses when it talks about you. It might seem like a minor technical point, but it's actually very important.

Search engines have always valued fresh content, and they let you update information quickly. If you change a page, Google recrawls it, and rankings can shift in days. Large language models don't work like this. As Lisa Main, Director at Main Bureau, said on Isentia's "AI as a Stakeholder" panel,  "large language models are not databases of verified facts." These models are trained on a snapshot of the internet, updated only from time to time, and they rely on sources that were already prominent when they were trained. This means a past crisis or a controversy that is already resolved can keep showing up in AI answers long after it's no longer relevant.

She shared the example of how a day and a half after a notorious terror attack, she asked ChatGPT if the area had ever experienced a tragedy of that type. It replied that it had not." The model wasn't being careless, but it just hadn't updated to include the latest news. This gap between what reality is and what AI still believes is true sums up the content freshness problem.

Dr Nici Sweaney, founder of AI Her Way, explained on the same panel why this gap matters. She calls AI "an accidental narrator" — it shapes what people believe about your organisation just by repeating the latest information it received. The system simply uses what's available and is not trying to be harmful, so it's important to make sure that information is up to date.

How does this change the way organisations show up?

For PR and communications teams, this changes what "reputation management" means. Put simply, messaging that an LLM cites will remain relevant, no matter when it dates from. Messaging that has not been factored into the LLM’s answers, meanwhile, will have no discernible impact on an increasingly vital - even central - channel, regardless of how many other metrics it might win out on. 

This leads to two important things to consider:

• First, the conditions that surround recent earned media, statements, and announcements determine whether an AI model updates its picture of the brand, or keeps running on an outdated one. Catherine Arrow of the PR Knowledge Hub made a related point on the "Inside the AI Shift" webinar: LLMs and the agents built on them are "often forbidden from going behind paywalls, from scraping particular sites," which she said creates a kind of "news vacuum." The same logic applies to the brand’s own newsroom or press page. If it isn't feeding the model something current, the model has nothing current to draw from.

• Second, owned content—like blog posts, media releases, and website pages — are strategically important because they’re something the organisation in question can control , but only if they are updated. If a page hasn't changed in eighteen months, it's much more likely to disappear from AI results, making any reputation built on it unstable. If something is published once and not updated, the brand risks letting older, less positive stories take its place.

For public sector and government communicators, the stakes are more immediate again. When a government agency's guidance changes, whether that's eligibility criteria, compliance requirements, or a service update, and the fresh version doesn't make it into what AI models are citing, people will still get fed old information, with potentially devastating real-world implications. 

The evidence is already there

This is not just in theory. It's playing out in global research and in the day-to-day data right now.

• AI is quietly replacing the front door to your content

The Reuters Institute's Digital News Report Australia 2026 confirms that many PR teams have noticed that Google organic search traffic to news sites dropped by a third worldwide between November 2024 and November 2025, and by 38% in the US, as AI Overviews and AI Mode launched. Publishers expect this traffic to nearly halve again in the next three years. Some now call this trend a move towards "Google Zero." For communications teams, this means people are increasingly less likely to  click through to your website to check if information is current. More often, they're trusting what the AI says: hence why it’s so important to monitor content freshness.

• AI models are now web-enabled and they might not actually guarantee source accuracy

One challenge is that most major chatbots are now web-enabled. For example, ChatGPT can browse the internet, Gemini uses Google Search, and Perplexity has its own live index. This makes it easy to assume that AI always knows the latest information. However, this does not mean that they are always accurate when it comes to citations. A study from Columbia's Tow Center for Digital Journalism tested eight AI search tools with 1,600 queries. They found that these tools failed to correctly identify or cite the source article more than 60% of the time. Some tools were wrong on most tests and rarely showed any uncertainty. New information has not had time to be checked or confirmed like older stories have. This is the real risk of relying on the newest updates — a story that is fast moving and poorly sourced about your organisation might end up in an AI answer before it’s even verified or fact-checked. 

• People are turning to AI chatbots specifically for what's new

The same report found that 35% of people who use AI chatbots for news do so to get the latest media updates. Dr Sora Park from the University of Canberra's News and Media Research Centre explained on the "Digital News Report Australia 2026" webinar that the main reason people use AI chatbots for news is that "AI collates stories from different news sources into a single response." People expect these tools to provide current information. If your organisation's newest content isn't included (and you have something current or novel to communicate) you miss the chance to reach audiences when they're most interested.

• Fresh content doesn’t always equate to ‘new’ content

A notable example  of creating freshness that LLMs reward and prioritise comes from updating existing pages, rather from creating brand-new content. Republishing and refreshing current material is more effective than many communications teams realise, as long as one actually updates the content, not just the date.

• Evergreen pages are the first casualties when AI overviews roll in

The DNR Australia 2026 report also notes that once someone is inside an AI chatbot conversation, they rarely leave it to check the source — only 4% of AI chatbot users say they always or often click through to the original article, compared with 19% for search and 17% for social media. The pages that used to earn traffic just by sitting there, permanent and useful, are now the ones most likely to lose visibility, because AI models favour what's recent over what's merely correct.

• One fresh statement doesn't automatically undo a stale narrative

If an executive online, especially one who has a lot of weight to what they post online, says something controversial and it quickly spreads across media articles, social media and search — it will definitely be picked up by AI as well. There is a golden window of opportunity that they need to capitalise on to clarify what they said. If they don’t, the negative story that was already built into the data AI models use, will not be affected much by the executive’s clarification statement, which wasn’t that timely anyway. As Catherine Arrow of the PR Knowledge Hub said on the "Inside the AI Shift" webinar: "public relations and media relations are not the same thing," and relying on a single release misses the point. The real lesson is not to publish faster after a crisis, but to build a strong, up-to-date presence before you need it. In our latest report, “How can leaders communicate in an age of scrutiny”, we’ve outlined exactly how comms leaders can communicate by adapting their content to audiences exposed to the “AI way” of news dissemination. 

What PR & Comms teams should actually do?

The challenge is that organisations can't make an AI model update its answers whenever they want. What they can do is track whether recent work is actually being noticed, which is what  Lumina AI View can help with.

Lumina AI View monitors which sources AI models use when talking about your organisation, how strong and recent those sources are, and how you compare to competitors. Freshness is one of five key factors in the overall score. If your freshness score drops, it's an early warning that your latest campaign or announcement hasn't reached the AI ecosystem yet, and older stories are still dominating.

What’s important to note is that the tool provides a list of source citations, paired with reputation pillars like direction, integrity, performance and innovation — giving a comms professional a fully-rounded understanding of what they need to do. It’s not just the case of knowing source citations, but also of understanding your own AI perception and performance to make informed decisions — whether that’s for a brand,a government agency, a NFP or elsewhere.

This kind of tracking is even more important because it shifts by industry and by market, so "AI visibility" doesn't mean the same monitoring job for every organisation. AI answers for healthcare might draw from the smallest, highest-trust pool of sources (mostly clinical and government), but SaaS and fintech answers lean heavily on editorial reviews and comparison sites.  Ngaire Crawford made a similar point regionally on the "AI as a Stakeholder" panel. For the APAC region specifically, she pushed back on the assumption that editorial media dominates AI citations — "there are a lot of really massive claims about the impact of editorial media... some as high as 85, 88%. That's not what we're seeing." Instead, she found "a fairly even split between (editorial media) and company content," alongside a real presence for review sites, forums, and academic sources. For a comms team, that means the freshness strategy that works for a media-heavy consumer brand might not work for a government agency whose AI visibility is really riding on review sites, .gov pages, or industry forums instead.

By tracking regularly — weekly or as a routine check— you turn the vague concern of "what is AI saying about us" into something that is super clear. You can see if recent coverage changed your list of citations, or if your owned content is still being found, or where there are gaps that need to be filled because old stories still exist and are causing problems.

The opportunity in staying current

There's a real advantage here too. If old content keeps you tied to an outdated story, fresh content is a direct way for PR and communications teams to influence how AI presents them. Publishing regularly, keeping your own pages updated, and getting recent, credible coverage is not just for human audiences. It's how PR professionals can make sure the systems shaping first impressions have the right information.

Teams that make it an ongoing habit of checking in regularly, watching for changes, and keeping fresh, credible content flowing, will have more control over how AI describes their organisation.

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If you would like to know more about our Lumina suite, please reach out here and our team will get in touch with for you a quick demo.

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