What are the dangers of publishing too much AI content on my site?

Highlights

• Google's March 2024 core update introduced "scaled content abuse" as a spam policy, resulting in 45% less low-quality content in search results
• AI-generated content that lacks human review risks factual hallucinations, which can lead to legal exposure and lost credibility
• Publishing dozens of similar AI-drafted pages causes keyword cannibalization, where your own content competes against itself in search
• Search engines now evaluate E-E-A-T signals (Experience, Expertise, Authoritativeness, Trustworthiness) that raw AI output struggles to demonstrate
• A composable CMS with structured content and workflow governance gives teams a human-in-the-loop layer before anything reaches production

Introduction: More content is not always better content

It's tempting to use generative AI to publish as much as possible, as fast as possible. But volume without quality control is the quickest way to ruin your search results and tarnish your brand.

This was confirmed by Google’s March 2024 core update, which added a “scaled content abuse” spam policy aimed at websites that are producing high volumes of low-quality content to game the system — regardless of whether that content is automated, authored by a human, or a combination of the two. Following its release, Google says it saw a 45% reduction in the amount of low-quality content showing up in search results. Translation: Just because you’re creating more pages doesn’t mean you’ll get more traffic.

If you’re an in-house team with multiple brands, markets, and distribution points in the mix, the potential damage of one AI-sourced piece containing incorrect information or sounding too generic is that much greater: it can ripple across your whole ecosystem. Below, we walk through the most common risks and how to avoid them.

The marketer's guide: Frequently asked questions on AI content risks

Does Google penalize websites for publishing high volumes of AI content?

Google does not penalize content solely for being AI-generated. It penalizes content that exists primarily to manipulate search rankings and provides little value to readers. Google's spam policies define "scaled content abuse" as publishing many pages with no original insight, regardless of how they were created.

In practice, this means a site that publishes hundreds of AI-drafted pages without editing, fact-checking or adding a unique perspective is at risk of site-wide demotion. Google's systems now evaluate content at the page level and the site level, so a cluster of thin articles can drag down the performance of your best-performing pages too.

The safest approach is to treat AI as a drafting tool, not a publishing pipeline. Every piece should pass through human review before it goes live.

Learn how Contentstack's automation workflows support content governance →

How does too much AI content affect brand authority and customer trust?

It makes your brand sound like everyone else. Most large language models draw from the same training data, so unedited AI output tends to produce similar phrasing, similar structure and similar conclusions across competing brands. Readers notice this, even if they can't pinpoint why your content feels off.

For B2B audiences especially, trust is built through specificity: original research, named experts, real customer examples and points of view that reflect actual experience. When your content reads like a summary of what every other company already published, it signals that no one at your organization actually has deep expertise on the topic.

Contentstack's Brand Kit helps solve this by enforcing your voice, tone and style guidelines across AI-generated content, so output reflects your brand's identity rather than a model's defaults.

What are the risks of hallucinations and factual errors in bulk AI publishing?

AI models fabricate information. They present made-up statistics, cite non-existent studies and attribute quotes to the wrong people. These "hallucinations" happen because language models predict likely word sequences rather than verify facts against a database.

If you’re publishing at scale without checking your facts, the likelihood that a mistake will find its way to your reader increases with each piece of content you produce. In a B2B organization, the stakes are very real: A misstated compliance statement, an incorrect product spec or a made-up statistic can translate to customer dissatisfaction, liability and a loss of trust that will be difficult to regain for years to come.

The fix is straightforward. Build a human review step into your content workflow. Use AI to generate drafts, then have a subject matter expert verify every claim, statistic and attribution before publishing. A headless CMS with structured workflows makes this easier to enforce across teams.

Can high volumes of AI content cause keyword cannibalization?

Yes. When you publish many AI-drafted articles targeting similar topics, search engines struggle to determine which page should rank for a given query. Your own pages end up competing against each other, and none of them rank as well as a single, comprehensive piece would.

This problem, sometimes called "content decay," happens because AI tools tend to produce variations on the same structure and talking points. You end up with five articles that each cover 60% of a topic instead of one article that covers 100% of it. The result is diluted authority across all five.

A better approach is to audit your existing content before publishing anything new. Identify gaps, consolidate overlapping pages and use AI to improve what you already have rather than creating more of the same.

How do search engines tell the difference between quality AI-assisted content and spam?

Search engines look for E-E-A-T signals: Experience, Expertise, Authoritativeness and Trustworthiness. These are hard to mimic with straight AI-generated content. Original research, unique case studies, named authors with real credentials and cited first-party data are how you separate the wheat from the chaff.

Google's quality rater guidelines describe "lowest quality" content as pages that show no evidence of human effort or expertise. Content that lacks these signals gets buried, especially now that AI-generated search overviews pull from the most authoritative sources first.

To give your AI-assisted content these signals, pair it with real data from your own platforms. A Real-time CDP can surface audience insights and engagement data that make your content specific to your customers rather than generic to your industry.

Contentstack brings governance for AI-driven content

Contentstack's composable architecture treats content as structured, modular data rather than unstructured blobs of text. That structure is what makes governance possible at scale.

With a headless CMS, your team can set up automated workflows that require human review before any AI-generated content reaches production. Brand Kit enforces voice and style consistency. And because content is separated from presentation, the same reviewed, approved content can be delivered across web, mobile, email and any other channel without being recreated (and re-risked) each time.

The goal is not to avoid AI. It's to use AI in a way that improves your content without introducing the risks that come from publishing without oversight.

For more on how enterprise teams are adapting their strategies for AI-driven search, see the Enterprise AI Search Playbook.

Next steps

Take inventory of your existing content. If two pages offer the same information, consider merging them, or if they contain nothing unique, consider eliminating them. Also, introduce fact-checking processes that happen before publication, to ensure AI mistakes don’t make it onto your website.

AI is most effective if it enables your team to do what they already do well, faster. Don’t rely on it for your content if it means eliminating the work that sets it apart.

Start a free trial of Contentstack to see how structured content and workflow governance support a smarter AI strategy.