Why AI Content Stopped Working & What To Do About It [Watch Now] via @sejournal, @hethr_campbell

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Learn to humanize AI content, hold it accountable to real outcomes, and reach the AI answer layer. Watch the webinar. That stat framed the core argument from Gabriel Dillon, Go-to-Market Lead for Personalization at Contentful: when AI makes content nearly free to produce, volume stops being a strategy. The only content that earns attention is content held accountable to a business outcome, built for a specific human, and measured against real data. In an SEJ webinar with Contentful Principal Solution Strategist John Graham, Dillon walked through why AI-assisted copy drifts toward generic output, the four questions he runs on every piece of marketing copy before it ships, and the personalization signals that work without overcomplicating your stack. The session also covered where the human belongs in an AI-assisted workflow, and how experimentation and personalization combine into an accountability loop for content performance. Your AI writing assistant acts as the ultimate yes man, and your own assumptions feed the loop. That is Dillon’s explanation for why every brand’s AI-assisted copy converges on the same output. “Our biases as we write content using the robots ends up eating the content that we produce,” he said. “We end up in this cycle of creating content that we think is good but doesn’t actually do what we think it does.” The copy that comes back either confirms what you already believed or mirrors every competitor’s blog in the tool’s training data. Both outcomes fail the reader. Dillon’s counterweight is taste, and he pushed the definition past the cliche: discernment and intuition, plus the risk-taking to make a claim no AI tool would volunteer, based on what you actually know about your market. The session mapped exactly where the human steps into the AI-assisted workflow, between AI as a research and context layer and the copy that ships. Dillon runs the same four questions on every piece of B2B marketing copy before it ships. The first is whether the copy produces the outcomes you expect. The other three cover who the content is for, how you identify those people, and how the insight scales. “If we don’t have data that proves that our content is good, then we can’t really think about the way to scale it out or make it more effective,” he said. Experimentation and personalization are two halves of the same coin in this model. How the two combine into a system, rather than a series of one-off tests, is where the recording goes deep. The full walkthrough diagrams the accountability loop and the experiment dimensions beyond variant A vs. variant B. Action item: before commissioning the next batch of AI content, run it against Dillon’s four accountability questions. The signals your stack already collects. Dillon’s diagnosis of why B2B personalization has underdelivered for years: teams tackle programs that are too ambitious, then stall on complexity. He laid out three signal tiers, starting with the simplest: new vs. returning visitors. A first-time visitor and a repeat visitor carry different intent, and serving them the same hero copy wastes the distinction. The second and third tiers use signals your ad campaigns and loyalty program generate today. Dillon called the current handling of one of them “such a missed opportunity”; the recording names which signals to use and where each one pays off. The webinar demo shows how these differentiated experiences get built and delivered inside Contentful. Watch it on demand. Detection is the wrong problem to solve, Dillon argued: whether Google can identify AI content matters less than what happens to clicks. Contentful’s clients are already reporting a crash in organic traffic as AI summaries absorb clicks. The practical response is to compete for the AI answer layer. GEO and AEO determine whether the AI summary at the top of the results page reflects your brand at all. His conclusion cut through the humans-vs-robots debate: one kind of content performs in AI summaries and on-page conversion simultaneously. What that content requires, and the tooling Contentful just shipped for it, is in the session. The recording covers how to approach GEO and AEO without splitting your content strategy in two. Expect identification of AI content to keep getting harder; Dillon called it a fight “Google won’t win.” His guidance shifts the energy away from evading detection entirely, toward a different target he argues matters more as zero-click search grows. He explains where to redirect that effort in the session. Answered by Gabriel. Get full context; watch on-demand, now. Bias enters in two places. You inject it through prompting and context, which produces “a result that you want, but maybe not the result that would be most effective.” It also lives in the training data itself. Dillon’s mitigation starts before you generate anything; he walks through the sequence in his full answer. Answered by Gabriel. Get full context; watch on-demand, now. Hold leadership accountable to the performance they expect. “Show them through data that you can create better content that drives the business outcomes that you want by creating fewer but better pieces of content.” Dillon also conceded one point to the volume argument, and that concession shapes how you make the case. Answered by Gabriel. Get full context; watch on-demand, now.

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Source: Search Engine Journal

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