Scaling AI content generation is the number one content strategy for businesses maximizing AI search visibility. According to Conductor’s 2026 State of AEO/GEO CMO Investment Reportwhich surveyed more than 250 digital executives and leaders across 12 industries, ranked above structured data, above authoritative long-form guides, and above original research. Regardless of the maturity level asked, from organizations venturing into AI visibility to those adopting it enterprise-wide, this is the best answer.
However, this may also be where the problem begins.
AI content scaling fails
In the report, Aleyda Solis acknowledges the strategic intent but raises a concern: “While it is possible to leverage AI for content, a personalized editorial and optimization workflow is necessary to ensure quality, originality, and expertise by integrating unique brand insights and first-party data, which AI platforms are likely to cite.”
Eli Schwartz predicted that the current trend in AI content scaling “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 Useful Content Upgrade. He also noted that the executives he speaks with are “somewhat skeptical of the effectiveness of massive amounts of AI content, but are afraid of being left behind if they don’t.”
Fear of missing out is not the basis of 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 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 agree that if something is easy, it is easy for everyone and is not competitive.
Pedro Dias documented that in June 2025, Google began issuing manual actions specifically for large-scale content abusetargeting sites that mass-published AI-generated content. Sites in the United Kingdom, United States, and European Union have received notifications from Search Console citing “aggressive spamming techniques, such as large-scale content abuse.”
Dan Taylor recently wrote about the mechanics of this failure in detail, sharing traffic graphs that illustrate what Glenn Gabe calls the effect “Mt. AI”an initial spike when new content floods the index, followed by a cliff when Google’s quality threshold rating comes into play. What Taylor identifies as the real problem is not the AI content itself, but the lack of a real content strategy underneath. “The real problem is that scaling content production, regardless of the method, often introduces a series of quality control issues,” he writes. The freshness boost new URLs receive temporarily hides these issues. So that’s not the case.
I write, read, and edit a lot of content, and I can clearly see when AI has been used to complement the writing. Some writers can do this well and contribute enough of their expertise to achieve reasonable results. Others less so, where they rely on AI to supplement their lack of knowledge or expertise. For my part, I can get amazing results from Claude when I provide unique and quality research, but I have to invest a lot of advice to get something worth publishing.
To be clear, I am not anti-AI. Like Google, I focus on good quality content and writing.
This gap between what AI produces by default and what is actually publishable is precisely where the opportunity still exists for writers who know their subject. Great human-driven content is not a compromise. Right now, it’s competitive advantage.
Google is consistent when it comes to AI content
Google’s stance on the use of AI content and quality content has been consistent.
Danny Sullivan spoke at Google Search Central event in Toronto in April 2026 on the concept of commercial or non-commercial content.
Marketable content is anything that an AI can produce from publicly available information. Non-marketable content requires that you have actually done something, know something from direct experience, or have an opinion based on true expertise. And that’s what Google sees as your competitive strength in the AI era.
John Mueller accused abuse of AI-related content in the context of Google Quality Rating Guidelines update, which now explicitly groups AI-generated content into a section about content created with little effort or originality. Quality raters should apply the lowest rating to pages where all or almost all of the content is automatically or AI-generated with little or no effort, originality, or added value, regardless of the production method. Google’s guidelines make it clear that AI tools alone do not determine rating, effort, originality, and value.
All of this corresponds to the foundations of what Google wants to display: quality content that demonstrates direct experience.
We’ve seen this before
Lily Ray took a test by asking Perplexity for SEO news and received a confident report on the “September 2025 Perspective Core Algorithm Update”, an update from Google that had never happened. The citations provided by Perplexity referred to AI-generated articles on SEO agency blogs. Sites that had managed a content pipeline, hallucinated an update and published it as a report. Perplexity read this and treated it as a source, and returned it as fact.
There is a historical parallel here that some older SEOs will recognize.
Early PR/digital link building efforts involved running stories or content in lower-tier publications because higher-level journalists used them as a source, and this generated implicit credibility from multiple quotes. Journalists then began quoting what was published by other sites, and the published sites cited and referenced them in the same citation cycle.
Another example I saw recently involved several articles (wrongly) reporting that Jeremy Clarkson and his partner Lisa Hogan (of Amazon’s top UK show Clarkson’s Farm) were spending time apart and ending their relationship. What Clarkson had actually said was that they deliberately separated during the day so that they would have something interesting to talk about in the evening. This may be a low-stakes example, but it perfectly illustrates how quickly misinformation spreads.
Content Scale is a Strategy and a Challenge
The most mature organizations in the Conductor report (organizations where AEO/GEO is a key digital priority) have already reached the right conclusion, and they are the only group in the study to have prioritized original research based on first-party data as their content strategy. They understand that first-party data and authentic research cannot be replicated by running an AI content operation and that exclusivity is the goal.
The main finding of the Conductor report is that 94% of companies plan to increase their investments in AEO/GEO in 2026, and that AEO/GEO has become the number one marketing priority, ahead of paid media and paid search. The report also reveals 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 Corporate Brands Can Evolve and Win
Industries that already operate on programmatic content models (travel, e-commerce, 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 speed up something that was already happening.
But, to have true brand differentiation, investing in a unique voice and approach to how they write these ads can set them apart and provide a competitive advantage.
Alongside their programmatic content, enterprise brands should also find ways to produce content that is truly difficult to replicate. Experience-based, data-driven, editorially thoughtful, and specific in a way that only a true subject matter expert would know.
For a business brand to successfully evolve its content, my recommendation is to wrap the use of AI around subject matter experts and editors. The power of AI lies in how it can turn experts into super producers and enable them to produce more. Enterprise brands should invest in finding these super producers, then use AI to exponentially grow 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 whatever you put into it. If you have true subject knowledge, proprietary data, and the editorial discipline to maintain quality, AI can dramatically accelerate your production. This helps you produce more of what you’re already good at, faster.
But if you don’t have these things, AI produces more of what you don’t have, faster. The content produced has structure, length and the appropriate vocabulary, but it contains nothing that an LLM cannot generate from publicly available information. Nothing that differentiates you from every other brand trying to evolve with AI in the same way.
As I said earlier, I’ve been producing in-depth content for years and for me, AI is a creative amplifier and an exciting tool that augments what I know. He doesn’t replace me and he certainly can’t do what I can single-handedly. On this basis, I see expert editors as the new gatekeepers of information.
For enterprise brands looking to evolve their content, they need to start by understanding that good content isn’t about including everything; it’s about knowing what not to include.
Full Conductor 2026 State of AEO/GEO CMO Investment Report Available available here.
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