AI content is any written material generated by AI tools, like ChatGPT, Claude, or Gemini. Rather than a human writing from scratch, the AI produces content based on a prompt, which might be as simple as giving it a topic, or as detailed as a full content brief.
It's worth noting that not all AI content is the same. A fully automated article with no human involvement is very different from an AI draft that a human editor has heavily refined. For the purposes of this blog, we define AI content as any content where AI played a primary role in the writing.
The appeal of AI content is obvious: it's faster and cheaper than traditional content production. What used to take a human writer several hours can be drafted in a fraction of the time, meaning teams can produce more content and move quicker.
Today's leading LLMs, like Claude’s Opus 4.7 and OpenAI’s GPT-5.5 Pro, already produce output that's difficult to distinguish from skilled human writing.
If your competitors are using AI effectively to stretch their content budget further, opting out is a resource disadvantage. They can simply produce more content, cover more topics, and build topical authority faster with the same resources.
A common misconception is that human content is inherently better than AI content, just because it was written by a human.
It isn't. A human writer without genuine expertise in a field or topic can produce terrible content. Ultimately, content quality comes down to the knowledge and judgment behind it, not the method of production.
Google's official documentation echoes the previous point: they reward original, high-quality content however it is produced. This means quality AI content can rank well, and is not penalised simply for being AI content.
Google evaluates content across several interconnected frameworks. Understanding these is essential to understanding what content performs and how to create it, regardless of whether it's written by a human or AI.
EEAT is the framework Google's human quality raters use to evaluate content. In short: was this written by someone with real experience and expertise in the topic, and is the site it lives on authoritative and trustworthy in its space?
When it comes to AI content, experience is worth calling out specifically, because AI has no lived experience. It can describe what experience looks like based on patterns in text and what it’s been trained on, but that's fundamentally different from having it. When using AI for content, the ideas, insights, and experiences of a subject matter expert (SME) should inform and shape the final output.
Information gain refers to how much new, original value a piece of content adds beyond what already exists on the topic. Google rewards content that tells searchers something they couldn't find in the pages already ranking, whether that’s original data or a unique perspective.
This is where AI content is structurally vulnerable. LLMs are trained on existing web content, so they naturally synthesise and reorganise what's already out there rather than generate genuinely new information. And while AI can generate ideas, it can't generate ideas grounded in first-hand experience, proprietary knowledge, or insights unique to you or your business. That's what needs to come from a human.
Google pays close attention to how users interact with content to determine content quality. Clicks, hovers, scrolls and dwell time all feed into Navboost, a critical ranking system Google uses as part of its search algorithm. Low engagement signals suggest the content didn't deliver and was of poor quality.
Now we know what quality content looks like, and some of the limitations of AI content, let's look at two common mistakes when using AI content.
Many businesses mistake volume for value — creating and publishing content at scale with minimal or no human oversight. It's an easy trap: AI tools are cheap, fast, and impressive enough at first glance that it's easy to overestimate what they can do independently. But content produced this way almost always fails in the long run, for the reasons covered earlier: no lived experience, no original insight, no information gain. It might be great writing, but it isn’t great content.
And the competitive logic doesn't hold up either. If your content is generated by AI with no unique perspective or expertise behind it, what edge do you have over competitors with the same access to these AI tools? Volume without differentiation isn't a moat — it's a commodity. One that can be obtained for $20 a month and a few prompts.
Another common mistake is letting AI decide what content to create in the first place. AI can generate topic ideas, but misses the gaps, angles, and opportunities that only come from genuine industry knowledge, expertise and a real understanding of your audience, which comes from a human SME.
A good content strategy is intentional. It requires knowing what your business uniquely understands, where your audience has unmet needs, and which topics you can credibly own. That's not easy, and at this stage, AI can't do it for you.
Violating Google’s Spam Policies of scaled content abuse can result in automated or manual penalties that result in reduced visibility in search, or worse, a complete removal. It is extremely hard to recover from this, which is why using AI content the right way is so important.
If little or no human oversight is involved in AI content production, this can be highly damaging to a brand's credibility. LLMs hallucinate; they confidently produce information that sounds authoritative but is factually incorrect. Published without fact-checking, this misinformation becomes associated with your brand. In most industries, that's embarrassing. In health, finance, or legal, it can be genuinely harmful.
Beyond factual errors, there's a subtler reputational risk. Readers, especially sophisticated ones, can sense when content lacks genuine expertise. If your content reads as generic and surface-level, or if the topic or angle is off, it doesn't just fail to build thought leadership, it signals a lack of expertise. For any business that relies on being seen as knowledgeable and trustworthy in its space, that's a difficult perception to recover from.
Poor AI content doesn't just fail to perform, it's a waste of budget. The time spent briefing, generating, and publishing content that never ranks represents a real resource cost, as does the technical debt of eventually having to audit, consolidate, or remove low-quality pages from a site.
The irony is that misusing AI content in the name of efficiency ends up costing more in budget, reputation, and rankings than simply investing in quality from the start.
Used the right way, AI is an incredibly powerful tool for content. The difference between AI content that performs and AI content that doesn't almost always comes down to where in the process AI is used — and how much human expertise shapes the output at every stage. Of course, this may vary depending on the type of content you’re producing. For example, a long-form thought leadership piece would require much more expertise and human input than copy for a collection page.
The right mental model is to view AI as a highly capable assistant, not an author. Let it handle the structural heavy lifting: getting a draft on the page, organising information, suggesting headings. Treat everything it produces as raw material that a human needs to meaningfully improve. The content doesn't exist until a human expert has engaged with it, added to it, and taken responsibility for it.
Before touching AI, define what the content needs to achieve and what unique perspective your business brings to the topic. The brief should include your target audience, tone of voice, content angle, and the proprietary insights, client examples, or expert knowledge that only you can provide. A well-constructed brief produces a significantly better AI draft. A vague prompt produces generic output that takes more time to fix than it saved to generate.
This is the step most commonly skipped and the one that matters most. Every piece of AI-assisted content should be reviewed and enriched by someone with genuine expertise in the topic. Not just to fact-check, but to add the lived experience, original perspective, and nuanced insight that AI cannot generate. This is what creates information gain, satisfies EEAT, and produces content that is genuinely difficult to replicate.
And don't stop at the content itself. Demonstrate EEAT through authorship too — a named expert with a byline & biography displaying expertise, authority and credibility goes a long way to building the trust signals Google looks for.
LLMs hallucinate. Every claim in an AI draft needs to be verified against a reliable source before publication. A single significant error can undermine the credibility of the entire piece, as well as the brand behind it.
Google doesn't require AI disclosure, and there's no ranking benefit to doing so. The question is less about Google and more about your audience. If disclosing AI content is important to them, then being transparent about this can reflect positively on your brand.
AI content isn't going away, and the businesses that know how to use it well will have a genuine advantage over those that don't. But those that treat it as a shortcut will pay for it, in rankings, in reputation, and in wasted budget.
The framework is straightforward: AI for efficiency, human experts for quality. The balance between the two will shift depending on what content you're producing, and getting that balance right is what maximises results.