Scaling AI content generation is the number one content strategy for enterprise organizations optimizing for AI search visibility. According to Conductor’s 2026 State of AEO/GEO CMO Investment Report, which surveyed over 250 executives and digital leaders across 12 industries, it ranked above structured data, above authoritative long-form guides, and above original research. Across every maturity level surveyed, from organizations venturing into AI visibility to those with enterprise-wide adoption, it was the top answer.
However, this may also be where the problem starts.
AI Content Scaling Is Failing
Inside the report, Aleyda Solis acknowledged the strategic intent but raised a concern: “Although it’s possible to leverage AI for content, a personalized editorial and optimization workflow is required to ensure quality, originality, and expertise by integrating unique brand insights and first-party data, which is exactly what AI platforms are likely to cite.”
Eli Schwartz predicted that the current AI content scaling 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 Update. He also flagged that the leaders he speaks with are “somewhat skeptical about the effectiveness of mass amounts of AI content, but are afraid of being left behind if they don’t do this.”
Fear of missing out is not a basis for an effective content strategy.
Lily Ray, who is known for her in-depth analysis, said earlier this year: “Interesting, but not surprising, to see people on LinkedIn sharing their stories of losing all search visibility (sometimes overnight) after an aggressive AI content strategy.” She 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 not competitive.
Pedro Dias documented that in June 2025, Google began issuing manual actions specifically for scaled content abuse, targeting sites that had been mass-publishing AI-generated content. Sites across the UK, US, and EU received Search Console notifications citing “aggressive spam techniques, such as large-scale content abuse.”
Dan Taylor recently wrote about the mechanics of this failure in granular detail, sharing traffic graphs that illustrate what Glenn Gabe calls the “Mt. AI” effect, an initial spike when new content floods the index, followed by a cliff edge as Google’s quality threshold assessment kicks in. What Taylor identifies as the real problem isn’t AI content itself, but the absence of any genuine content strategy underneath it. “The real problem lies in the fact that scaling content production, regardless of the method, often introduces a raft of quality control issues,” he writes. The freshness boost that new URLs receive masks those issues temporarily. Then it doesn’t.
I write, read, and edit a lot of content, and I can clearly see when AI has been used to supplement writing. Some writers can do this well and have input enough of their expertise to get reasonable results. Others not so much, where they are leaning on AI to supplement their lack of knowledge or expertise. For myself, I can get astounding results from Claude when I input quality, unique research, but I do have to invest a huge amount of guidance to get anything worth publishing.
To be clear, I’m not anti-AI usage. Like Google, I’m focused on good quality content and writing.
That gap between what AI produces by default and what’s actually publishable is precisely where the opportunity still lives for writers who know their subject. Exceptional human-guided content isn’t a compromise. Right now, it’s the competitive advantage.
Google Is Consistent About AI Content
Google’s position on the use of AI content and quality content has been consistent.
Danny Sullivan spoke at the Google Search Central event in Toronto in April 2026 about the concept of commodity versus non-commodity content.
Commodity content is everything an AI can produce from publicly available information. Non-commodity content requires you to have actually done something, know something from direct experience, or hold an opinion grounded in genuine expertise. And this is what Google considers your competitive strength going into the AI era.
John Mueller framed AI content abuse in the context of Google’s Quality Rater Guidelines update, which now explicitly groups AI-generated content in a section about content created with little effort or originality. Quality raters are instructed to apply the lowest rating to pages where all or almost all of the content is auto- or AI-generated with little to no effort, originality, or added value, regardless of production method. Google’s guidelines are explicit that AI tools alone don’t determine the rating, effort, originality, and value do.
This all aligns with the foundations of what Google wants to surface – quality content that demonstrates first-hand experience.
We Have Seen This Before
Lily Ray ran a test by asking Perplexity for SEO news and received a confident report about the “September 2025 Perspective Core Algorithm Update,” a Google update that had never happened. The citations Perplexity provided pointed to AI-generated posts on SEO agency blogs. Sites that had run a content pipeline, hallucinated an update, and published it as reporting. Perplexity read this and treated it as source material, and served it back to her as fact.
There’s a historical parallel here that some older SEOs will recognize.
Early digital PR/link building efforts involved seeding stories or content into lower-tier publications because top-tier journalists used them as source material, and it generated implied credibility of multiple citations. Journalists then began to cite what was published by other sites, and published sites cited and referenced them in the same citation cycle.
Another example I saw recently involved several articles [incorrectly] reporting that Jeremy Clarkson and his partner Lisa Hogan (from the top Amazon UK show Clarkson’s Farm) were spending time apart and ending their relationship. What Clarkson had actually said was that they deliberately go their separate ways during the day so they have something interesting to talk about in the evening. This might be a low-stakes example, but it perfectly illustrates how quickly misinformation spirals.
Content Scale Is Strategy And Challenge
The highest-maturity organizations in the Conductor report (organizations where AEO/GEO is a core digital priority) have already arrived at the right conclusion, and they are the only group in the study that prioritized original research based on first-party data as a content strategy. They understand that first-party data and genuine research cannot be replicated by running an AI content operation and exclusivity is the point.
The Conductor report’s headline finding is that 94% of enterprise organizations plan to increase AEO/GEO investment in 2026, and that AEO/GEO has become the number one marketing priority, above paid media and paid search. The report also surfaces that generating AI-optimized content at scale is not only the top stated strategy, but also the top stated challenge. Brands know what they want to do, but they don’t know how to get there.
How Enterprise Brands Can Scale And Win
Industries that already operate on programmatic content models (travel, ecommerce, large product catalog sites) have been producing content at scale for years. A hotel comparison site generating location pages, a retailer producing thousands of product descriptions, a marketplace creating structured listings are all legitimate use cases where AI can effectively accelerate something that was already happening.
But, to have real brand differentiation, investing in a unique voice and approach to how they write these listings can set them apart and be a competitive advantage.
Alongside their programmatic content, enterprise brands should also be finding ways they can produce content that is genuinely difficult to replicate. Experience-driven, data-grounded, editorially considered, and specific in ways that only a real subject matter expert would know.
For an enterprise brand to win at scaling content, my recommendation is to wrap AI usage around subject-matter experts and editors. The power of AI is how it can turn experts into super producers and allow them to produce more. Enterprise brands should invest in finding these super producers and then use AI to exponentially scale their ability, not try and replace them.
AI Amplifies What’s Already There
The most useful frame for AI in content production is as an amplifier of whatever you bring to it. If you have genuine subject matter knowledge, proprietary data, and the editorial discipline to maintain quality, AI can meaningfully 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, faster. The content output has structure, length, and the right vocabulary, but it contains nothing that an LLM can’t generate from publicly available information. Nothing that differentiates you from every other brand trying to scale with AI in the same way.
As I said earlier, I have produced in-depth content for years, and for me, AI is a creative amplifier and an exciting tool that augments what I know. It doesn’t replace me, and it certainly can’t do what I can by itself. On that basis, I see subject-expert editors as being the new information gatekeepers.
For enterprise brands who want to scale their content they should start with understanding that good content is not about including everything; it’s about knowing what not to include.
The full Conductor 2026 State of AEO/GEO CMO Investment Report is available here.
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