Measuring AI content generation is the first content strategy for business organizations preparing for AI search visibility. According to Conductor’s State of AEO/GEO CMO Investment Report 2026, which surveyed more than 250 executives and digital leaders across 12 industries, it ranks above structured data, above authoritative long-form guidelines, and above actual research. Across all maturity levels examined, from organizations just entering AI visibility to those being adopted by the entire enterprise, it was a high response.
However, this may be where the problem starts.
AI Content Rating Fails
Within the report, Aleyda Solis acknowledged the strategic intent but raised concerns: “While it is possible to use AI in content, personalized planning and optimized workflows are required to ensure quality, authenticity, and professionalism by combining unique product information with first-party data, which is exactly what the AI platform may mean.”
Eli Schwartz predicted that the current AI content ranking trend “will change in 2026 as Google and other LLMs push back against low-quality content” with what he described as an AI version of Google’s Helpful Content Review. He also flagged that the leaders he spoke to “are somewhat skeptical of the effectiveness of a large amount of AI content, but are afraid of being left behind if they don’t do this.”
Fear of loss is not the foundation of an effective content strategy.
Lily Ray, known for her insightful analysis, said earlier this year: “It’s interesting, but not surprising, to see people on LinkedIn sharing their stories of losing search visibility (sometimes overnight) after an aggressive AI content strategy.” He added: “Just because it’s easy doesn’t mean it’s a good idea.”
I strongly echo that if something is easy, it’s easy for everyone and it’s not competitive.
Pedro Dias wrote that in June 2025, Google began to issue manual actions specific to the abuse of moderated content, directed at sites that were publishing large amounts of content generated by AI. Sites across the UK, US, and EU have received Search Console notices citing “aggressive spamming techniques, such as mass content abuse.”
Dan Taylor recently wrote about the use of this failure in great detail, sharing traffic graphs that show what Glenn Gabe calls the “Mt. AI” effect, the first rise when new content fills the index, followed by the edge as it enters Google’s quality assessment. What Taylor sees as the absence of a real problem under any content itself is not true. “The real problem is that measuring content production, regardless of the method, often presents a raft of quality control issues,” he writes. Updates that new URLs receive mask those problems temporarily. Then it doesn’t happen.
I write, read, and edit a lot of content, and I can clearly see where AI has been used to supplement writing. Some writers can do this well and contribute enough of their expertise to get meaningful results. Others not so much, where they rely on AI to supplement their lack of knowledge or expertise. Personally, I can get amazing results from Claude when I submit quality, unique research, but I have to invest a large amount of guidance to get anything published.
To be clear, I am not an AI implementation. Like Google, I focus on good quality content and writing.
That gap between what AI automatically generates and what can actually be published is where opportunity still lies for writers who know their subject matter. Exclusive personalized content is no compromise. Currently, it is a competitive advantage.
Google Is Agnostic About AI Content
Google’s position on the use of AI content and quality content has always been consistent.
Danny Sullivan spoke at the Google Search Central event in Toronto in April 2026 about the concept of proprietary versus non-proprietary content.
Content is content generated by AI from publicly available information. Non-proprietary content requires you to actually do something, know something through direct experience, or have an opinion based on actual knowledge. And this is what Google considers your competitive strength entering the AI era.
John Mueller framed AI content abuse in the context of an update to Google’s Quality Rater Guidelines, which now explicitly includes AI-generated content in the section on content created with minimal effort or originality. Quality raters are instructed to apply the lowest rating to pages where all or nearly all content is generated automatically or by AI with little effort, originality, or added value, regardless of the method of production. Google’s guidelines make it clear that AI tools alone do not determine rate, effort, originality, and value.
All of this goes with the basics of what Google wants to deliver – quality content that reflects a personal experience.
We’ve Seen This Before
Lily Ray did a test by asking Perplexity about SEO issues and got a convincing report about the “September 2025 Perspective Core Algorithm Update,” a Google update that never happened. The citation rates provided refer to AI-generated posts on SEO blogs. The sites used the content pipeline, generated the update, and published it as reporting. Bewildered he read this and took it as the source of the story, and returned it to him as the truth.
There are historical parallels here that some old SEOs will recognize.
Classic PR/link building efforts involved planting stories or content in low-profile publications because high-profile journalists were using them as a source of information, and it generated a lot of citation credibility. Journalists then began to cite the publications of other sites, and the published sites cited and referred to them in the same citation cycle.
Another example I saw recently covers several topics [incorrectly] reports that Jeremy Clarkson and his partner Lisa Hogan (who stars in the Amazon UK show Clarkson’s Farm) are spending time apart and ending their relationship. What Clarkson said was that they deliberately went their separate ways during the day so that they would have something interesting to talk about in the evening. This may be a low example, but it shows how quickly misinformation can spread.
Content Scale Strategy and Challenge
The organizations that have grown the most in the Operator report (organizations where AEO/GEO is a digital priority) have already reached the right conclusion, and are the only group in the study that prioritizes real research based on first-party data as a content strategy. They understand that first-person data and real research cannot be replicated by implementing AI content and that uniqueness is the point.
A headline from the Taxi Report is that 94% of business organizations plan to increase AEO/GEO investment by 2026, and that AEO/GEO has become a marketing priority, over paid media and paid search. The report also points out that generating AI-optimized content at scale is not only a top-down strategy, but also a top-down challenge. Brands know what they want to do, but they don’t know how to get there.
How Business Brands Can Scale and Succeed
Industries that already operate on structured content models (travel, ecommerce, large brand catalog sites) have been producing content at scale for years. A hotel comparison site that generates location pages, a retailer that generates thousands of product descriptions, a marketplace that creates an organized listing are all legitimate use cases where AI can effectively speed up something that was already happening.
But, in order to have real product differentiation, investing in a unique voice and the way they write these lists can differentiate them and be a competitive advantage.
Alongside their programming content, corporate brands must also find ways to produce content that is truly difficult to replicate. Experience-driven, data-driven, thoughtful planning, and detailed in ways only a true subject matter expert would know.
In order for a business product to win in content moderation, my recommendation is to wrap the use of AI in experts and content editors. The power of AI is how it can turn professionals into great producers and allow them to produce more. Enterprise brands should invest in finding these great producers and use AI to enhance their strengths, not try to replace them.
AI Augments What Already Exists
The most useful framework for AI in content production is as an amplifier for whatever you bring to it. If you have the knowledge of the original subject, proprietary data, and the editorial discipline to maintain quality, AI can dramatically accelerate your output. It helps you produce more of what you’re already good at, faster.
But if you don’t have those things, AI produces more of what you don’t have, quickly. The output content has the appropriate structure, length, and vocabulary, but does not contain anything that LLM cannot do with publicly available information. Nothing separates you from every brand trying to scale with AI in the same way.
As I said earlier, I’ve produced deep content for years, and for me, AI is a creative amplifier and a fun tool that augments what I know. It doesn’t take my place, and it certainly can’t do what I can do on its own. On that basis, I see expert editors as the new gatekeepers of knowledge.
For brands, businesses that want to scale their content should start by understanding that good content doesn’t mean including everything; it’s about knowing what not to install.
The full Conductor 2026 State of AEO/GEO CMO Investment Report is available here.
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