Scaling AI content generation is the number one content strategy for companies optimizing AI search visibility. The company ranked above structured data, above authoritative in-depth guides and above original research, according to Conductor’s 2026 State of AEO/GEO CMO Investment Report, which surveyed over 250 executives and digital leaders across 12 industries. Across all maturity levels surveyed, from companies venturing into AI visibility to those with enterprise-wide adoption, this was the top answer.
However, this may be where the problem begins.
Scaling AI content fails
In the report, Aleyda Solis acknowledged the strategic intent but raised concerns: “While it is 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 trend of scaling AI content “will change in 2026 as Google and other LLMs push back against low-quality content,” with an AI version of Google’s helpful content update. He also pointed out that the executives he speaks to are “somewhat skeptical about the effectiveness of mass amounts of AI content, but are afraid of falling behind if they don’t.”
Fear of missing out is not the basis for an effective content strategy.
Lily Ray, known for her in-depth analysis, said earlier this year: “Interesting, but not surprising, to see people on LinkedIn sharing their stories about 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 believe that if something is easy, it is easy for everyone and not competitive.
Pedro Dias documented that in June 2025, Google began taking manual actions specifically for large-scale content abuse, targeting sites that had published AI-generated content on a large scale. Websites across the UK, US and EU received Search Console notices about “aggressive spam techniques such as large-scale content abuse.”
Dan Taylor recently wrote extensively about the mechanics of this failure, publishing traffic graphs that illustrate what Glenn Gabe calls the “Mt. AI” effect: an initial spike as new content floods the index, followed by a drop as Google’s quality threshold rating kicks in. What Taylor identifies as the real problem is not the AI content itself, but the lack of a real content strategy behind it. “The real problem is that scaling content production, regardless of method, often presents a number of quality control issues,” he writes. The freshness boost that new URLs receive temporarily obscures these problems. Then that is not the case.
I write, read and edit a lot of content and can clearly see when AI has been used to complement writing. Some writers are good at this and have enough expertise to produce reasonable results. Others less so, where they rely on AI to compensate for their lack of knowledge or expertise. I myself can get amazing results from Claude when I submit high quality and unique research, but I have to invest a lot of guidance to get something worth publishing.
To be clear: I am not an opponent of AI use. Like Google, I focus on high quality content and copy.
It is precisely in this gap between what AI produces by default and what can actually be published that there is still an opportunity for authors who know their subject matter. Exceptional, human-driven content is not a compromise. At the moment it is the competitive advantage.
Google is consistent when it comes to AI content
Google’s position on the use of AI content and quality content is consistent.
Danny Sullivan spoke about the concept of commodity and non-commodity content at the Google Search Central event in Toronto in April 2026.
Commodity content is anything that an AI can produce from publicly available information. Non-commercial content requires that you have actually done something, know something from direct experience, or have an opinion based on real expertise. And that’s what Google sees as your competitive strength in the age of artificial intelligence.
John Mueller linked the misuse of AI content to Google’s update to its Quality Rater Guidelines, which now explicitly groups AI-generated content into 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 automatically or AI-generated, regardless of production method, with little to no effort, originality, or added value. Google’s guidelines explicitly state that AI tools alone do not determine rating, effort, originality and value.
This is all in line with the fundamentals of what Google wants to bring to the public: high-quality content that reflects first-hand experiences.
We’ve seen this before
Lily Ray ran a test by asking Perplexity about SEO news and received a confident report about the September 2025 Perspective Core Algorithm Update, a Google update that had never been seen before. The quotes provided by Perplexity pointed to AI-generated posts on SEO agency blogs. Sites that had been running a content pipeline, hallucinating an update and publishing it as news coverage. Perplexity read this, treated it as source material, and submitted it to her as fact.
There is a historical parallel here that some older SEOs will recognize.
Early digital PR/link building efforts involved pitching stories or content to lower-end publications as top journalists used them as source material, and it created implied credibility through multiple citations. Journalists then began quoting other websites’ publications, and published websites quoted and referenced them in the same citation cycle.
Another example I saw recently involved several articles [incorrectly] reported that Jeremy Clarkson and his partner Lisa Hogan (from the top Amazon UK show Clarkson’s Farm) spent time apart and ended their relationship. Clarkson had actually said that they deliberately went their separate ways during the day in order to discuss something interesting in the evening. This may be a low-stakes example, but it perfectly illustrates how quickly misinformation spreads.
Content scale is strategy and challenge
The highest maturity organizations in the Conductor report (organizations where AEO/GEO is a key digital priority) have already come to the right conclusion and 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 real research cannot be replicated by running an AI content operation and exclusivity matters.
The key finding of the Conductor report is that 94% of business organizations plan to increase AEO/GEO investments in 2026 and that AEO/GEO has become the number one marketing priority, ahead of paid media and paid search. The report also shows that generating AI-optimized content at scale is not only the most cited strategy, but also the most commonly cited challenge. Brands know what they want to do, but they don’t know how to get there.
How corporate brands can scale and win
Industries that already use programmatic content models (travel, e-commerce, large product catalog sites) have been producing content at scale for years. A hotel comparison site creating location pages, a retailer creating thousands of product descriptions, a marketplace creating structured offers are all legitimate use cases where AI can effectively accelerate something that is already happening.
However, to achieve true brand differentiation, investing in a unique voice and approach to the way they write these listings can set them apart and provide a competitive advantage.
In addition to their programmatic content, corporate brands should also find ways to produce content that is really difficult to replicate. Experience-driven, data-driven, editorially thoughtful and specific in a way only a true subject matter expert would know.
For a corporate brand to be successful in scaling content, I recommend focusing AI use on subject matter experts and editors. The power of AI is that it can turn experts into super producers and enable them to produce more. Corporate brands should invest in finding these super-producers and then use AI to exponentially scale their capabilities, not try to replace them.
AI amplifies what already exists
The most useful framework for AI in content production is as an amplifier of what you contribute to it. If you have real expertise, proprietary data, and the editorial discipline to maintain quality, AI can significantly speed up your production. It helps you produce more of what you already know how to do, faster.
But if you don’t have these things, the AI will produce more of what you don’t have more quickly. The content output has structure, length and the right vocabulary, but contains nothing that an LLM cannot generate from publicly available information. Nothing that sets you apart from every other brand trying to scale with AI in the same way.
As I said, I have been producing deep content for years and for me AI is a creative amplifier and an exciting tool that expands my knowledge. It doesn’t replace me, and it certainly can’t do what I can do on my own. Based on this, I view technical editors as the new gatekeepers of information.
For business brands looking to scale their content, they should first understand that good content isn’t about being inclusive; 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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