Thanks to AI, content strategies are easier to scale than ever. But as Sam Jolly (Head of Content) explains, without humanised content featuring original insights and information, mass volume content strategies can quickly lead to declining brand visibility in search rankings and AI-generated responses.
It’s true that generative tools have made it dramatically cheaper and faster to produce content at volume. In fact, a recent Ahrefs study found that 74% of newly published web pages now contain AI-generated content. But it might not be the boon for your content strategy that you think.
The brands leaning hardest on scale are starting to discover that the AI tools promising quick hacks can actually undermine organic performance and visibility. It’s why, rather than producing the most content, your focus needs to be on producing content that algorithms and buyers can trust.
What past Google updates teach us about scaled AI content
Marketers have long produced content to further their SEO and brand awareness objectives, as well as, more recently, GEO; the growing discipline of earning mentions and citations inside AI-generated answers. While AI tools may have made it possible to supercharge those efforts, platforms are catching up.
Search engines including Google have explicitly targeted low-value, mass-produced content, and the LLMs (large language models) powering tools like ChatGPT and Perplexity are increasingly selective about which sources they pull into their answers.
It’s not the first time we’ve seen this cycle.
In the late 2000s, content farms churned out thousands of cheap articles a day, designed purely to rank for high-volume search terms. Around the same time, marketers were stuffing pages with keywords to game Google’s relevance signals and building “link wheels”, networks of low-quality sites linking to each other to fake authority. Each tactic worked, briefly, and each one was eventually corrected by a Google update.
Most substantially, Google’s ‘Helpful Content Update’ in 2023 explicitly went after pages written for search engines rather than people. Scaled, low-value pages get demoted whether a human or an LLM produced them.
This isn’t a hypothetical threat. One accountancy software start-up reported that, after publishing over 22,000 AI-generated pages, it lost 100% of its organic traffic once Google detected the pattern. Even after the pages were deleted, the penalty wasn’t lifted.
And it’s important to note that while GEO is its own animal, it’s built on SEO foundations.
GEO draws on a far wider signal environment than SEO, but LLMs still often pull information from the same indexed web search engine ranks, and they apply similar quality signals when deciding what to cite. So when a site gets flagged for thin or duplicative content, it doesn’t just risk losing Google rankings. It lowers its chances of being cited in ChatGPT, Perplexity or Google’s AI Overviews too.
One penalty, two visibility channels hit.
How expertise can drive AI search visibility
If scaled, generic content is the problem, demonstrable expertise is the answer. Raw AI output can summarise, structure and rephrase, but it can’t bring original research or a credible voice.
Buyers are increasingly alert to the difference. Forrester’s 2026 research found that more than half of B2B buyers now actively distrust AI-generated content, even as they rely on AI tools to find it.
Crucially, search engines can tell the difference too. Google’s EEAT framework , which evaluates Experience, Expertise, Authoritativeness and Trustworthiness, has been the quality filter behind its rankings for years.
As it’s a new field that’s evolving all the time, it’s up for debate as to exactly how much importance AI platforms place in these elements, but the underlying principles are the same. When deciding what to surface or cite, both Google and LLMs consider who’s speaking, what they actually know and whether they’re trustworthy. In practice, that means looking for:
- named authors with verifiable credentials
- citations from credible third parties
- original data and research
- a track record of accurate, useful content on the topic
Pages that tick those boxes get rewarded. Pages that don’t get filtered out, regardless of how well-optimised they are technically. There’s a tangible value to obviously humanised content, putting a premium on the lived experience and hard-earned knowledge that AI-generated work can’t replicate.
What this means for B2B marketers
While AI can support content production, it can’t be the source of the expertise. That has to come from the people, data and perspectives only your business can offer.
Remember, humanised content isn’t just good in and of itself. It’s crucial for protecting your brand’s visibility, whether in search rankings or, increasingly, in LLM responses.
That brand visibility matters more than ever, especially in a world where people have rapidly shifted from “Just Google it” to “Just ChatGPT it.” As GEO moves from emerging discipline to commercial priority, the brands appearing in AI-generated answers are the ones shaping buyer decisions before a human even clicks through to a website. The numbers back this up.
As we exclusively reported in our recent GEO seminar, ‘Mastering AI Visibility’, Semrush research found that an AI-referred visitor is 4.4 times more valuable than a traditional organic search visitor. Meanwhile, 6sense’s 2025 Buyer Experience Report found that 94% of B2B buyers now use generative AI somewhere in their purchasing process.
Getting in front of these audiences is crucial. Thin, mass-produced content that gets ignored by LLMs is not the way to do it.
Chase quality, not quantity, in your content strategy
There isn’t a shortcut to sustainable AI visibility, just as there’s never been a shortcut to any kind of sustainable organic visibility.
Many of the principles that earn citations in LLM answers are the same ones that have always underpinned good B2B content strategies: showing up consistently across the platforms that matter, telling a coherent story across every channel, building relationships with credible third parties and prioritising genuine expertise over volume.
The tools aren’t the issue. AI is a multiplier for good strategy and a multiplier for bad strategy. It’s all about how you use them. And the marketers who win the next phase will be the ones to use them to produce better content, not more.
