Your content brief is built for a dead internet. Here's the new one.
The content brief is broken. It’s optimizing for an internet search experience that no longer exists… Here's the fix.
*I've enabled commenting on this document. Things are changing fast, and building a modern content brief may take collective insight to get right. Content marketing is in a weird place right now. Every day, there are new reports, articles, podcast clips, LinkedIn posts, newsletters, studies, and data points all telling us some version of the same thing: search is changing. The way Google and LLMs surface results (and the signals they collect) is volatile. Search itself is fragmenting across social media, LLMs, forums, and traditional engines. Traffic patterns are changing, and so are consumer behavior and trust. New insight, strategy ideas, and data are coming to us in fragments for us to piece together. And despite all the evidence that discovery, search behavior, and content consumption are evolving, many brands still cling to strategies built for an older version of the internet. I see it often in the briefs that come across my desk. Most briefs and content workflows still operate as if search works the way it did five years ago. Usually, these briefs include a version of this:
I suspect sticking to this relic of a content brief (and an overinvestment in scaled AI content) is one reason we’re seeing gobs of TOFU content that lacks the 3Vs that are essential to good content marketing: VoiceAn abundance of online content is now a snooze to read and a snooze to write. It is completely interchangeable with competitor content. There is no perspective, no personality, no tension, no editorial identity. It could absolutely be generated by AI. It is more noise in an already overcrowded internet. It’s forgettable. ValueIt does very little to genuinely help the customer. It rarely offers original insight, layered thinking, or meaningful expertise. In many cases, it speaks below the audience’s actual level of sophistication. The result is throwaway content that exists primarily because a keyword tool suggested it should exist. VisibilityMaybe it ranks for a while. Maybe it captures some search traffic before the SERP changes again, or an AI Overview absorbs the click entirely. But increasingly, this kind of content struggles everywhere else where visibility now matters. It’s absent from:
In other words, it is technically “optimized” while being strategically invisible. Continuing to use this old content marketing strategy and related brief is why search traffic and brand visibility look like this (actual footage): So, what’s the fix? Well, to start out, it’s doing the hard work. Step one starts with understanding what’s changing. Step two is testing new strategies. And step three is helping team members outside of strategy roles — writers, editors, and contributors — adapt through education and updated briefs. What’s changing in the world of search & content1. People are searching on search engines & AI platformsAn important corrective in the research right now comes from the 2026 State of Search report from Datos, built on large-scale clickstream data from real users across the US, EU, and UK. One finding: Google recovered to 94.3% desktop search share in the US by March 2026. Zero-click searches (the metric that's become shorthand for "the open web is dying") actually fell from 24.5% in December 2025 to 22.4% in March 2026. Organic click share rose to 44.9%. AI tools, for all the attention they receive, still account for less than 2% of total desktop web visits. Some experts, like the CEO of AirOps, Alex Halliday, offer different projections. He asserts that by 2027, ChatGPT will surpass Google in search traffic. This matters because content strategy gets influenced by overreaction in both directions. If your team concludes that Google no longer matters and shifts investment away from organic search (note: organic search looks a lot different now than it used to), you're making a decision the actual data doesn't support. If you conclude that AI search is irrelevant because its share is still small, you're ignoring where the structural signals are pointing. The right search optimization frame is a dual-track model. One track protects and expands visibility in traditional search (however volatile it may be). The other prepares content for AI-mediated environments where the rules of visibility differ. Both are real. Neither is optional. 2. Information agents are searching the webAt Google I/O 2026, Google announced what it called the biggest change to search since the search box debuted 25 years ago. The new experience introduces agentic capabilities, which are "information agents" that work in the background 24/7, synthesizing findings on a user's behalf. This means search won’t look like it has in the past, with 10 blue links to click after a keyword search. Many searchers will tell an agent what to monitor and receive synthesized updates with links they can explore if they choose. As TechCrunch put it: "searching the web will increasingly be performed by AI agents rather than humans." What does this mean for content? The click is no longer guaranteed. Your content may be read, summarized, and acted on without a single visit to your site. The agent becomes the intermediary between your content and your audience, which means content that exists only to rank for a keyword, with nothing distinctive to say once it gets there, has almost no reason to exist at all. Note: I’ve seen about 100 versions of what Google will look like since Google I/O 2026. I’m going with the version I believe is most probable/accurate for now. 3. We have to create for agents and humansThe Economist's VP of Generative AI, Josh Muncke, told Digiday their publication is preparing for "a world with two versions of the web,” one that’s optimized for rich human reading experiences, another where agents want clear structure, questions and answers, and ideally plain text. The Economist is already building parallel versions of its marketing content: one designed for humans, one stripped back and Q&A-structured for agents. The logic: AI intermediaries are increasingly acting on a user's behalf before that user ever arrives at a homepage or types into a search bar. But even in a world shaped by agents and AI discovery, humans are still the audience that matters. Visibility means very little if the actual person on the other side feels nothing, learns nothing, trusts nothing, or remembers nothing. An LLM can surface your content. It cannot force someone to care about it. That’s part of the danger of AI-powered scale. It’s now incredibly easy to plug into a scaling tool and flood the internet with structurally optimized content built for discoverability. And yes, some of it may earn visibility. Some of it may even get surfaced by agents. But visibility alone has never been the point of content marketing. The “Value” part of the 3V framework matters more than ever now. If content doesn’t offer real insight, perspective, usefulness, originality, or a reason for a human being to stay engaged, then scale simply produces more forgettable content faster. And this type of scale gets annoying fast. Google talks about this often. In its guidance around helpful content and quality evaluation, Google repeatedly draws a line between commodity content — interchangeable, generic information created primarily to capture traffic — and content that demonstrates originality, experience, perspective, and genuine value to readers. Maybe that’s the real test now: Would a real person still care about this if visibility were guaranteed? 4. People use different search tools for different purposesHere's a behavioral finding that most content strategy conversations miss entirely. Nielsen Norman Group research published in February 2026 found that users choose AI tools to explore and synthesize information, but they still rely on traditional search when accuracy and trust are critical. A reader who finds your content through an AI summary is in exploration mode. They're orienting, comparing, building a mental model. A reader who clicks through from a traditional search result is in verification or buy mode. They're checking a specific claim, evaluating a source, deciding whether to trust you. In my own experience as a searcher, AI mode/LLM search is enough for quick answers, but I don’t remember (or care) who/where the content came from. When I want a deep answer, I look beyond AI visibility. I’ll search until I find content that stands out, and I’ll typically continue my search across platforms looking for expert insights. This search pattern is how I learned about Lily Ray and Aleyda Solis, and why I subscribe to their newsletters and social accounts. This behavioral shift matters in a zero-click environment and should inform your brief. Rand Fishkin and Amanda Natividad have argued that modern content increasingly has to deliver value before a click ever happens. AI summaries, search snippets, Reddit threads, LinkedIn posts, TikTok explainers, and social search results are all becoming discovery layers through which users can get enough information to move on without ever visiting a website. The content itself—across every discovery surface—has to be helpful/interesting enough that people remember the source (e.g., your name or your company). It should stand out enough that they later search for your brand directly, subscribe to your newsletter, look up your framework, follow your perspective, or intentionally seek out more of your work. In a fragmented search environment, memorability and trust matter as much as (or more than) rankings—and that should fundamentally shape the modern content brief. 5. AI platforms look beyond Google — and LinkedIn matters more than most marketers realizeResearch synthesized by AEO strategist, Kaleigh Moore, drawing on data from Profound and Semrush, gives us a clearer view of where AI systems surface information for professional and B2B-style queries. LinkedIn now appears heavily in AI-generated answers for professional queries. Profound data, cited by Moore, found LinkedIn appeared in 14.3% of ChatGPT Search responses and 13.5% of Google AI Mode responses, putting it ahead of Wikipedia in that dataset. The content getting cited also wasn’t necessarily viral. Moore notes that the average cited LinkedIn post had only 15–25 reactions. Patterns associated with citation include original content, substantive long-form posts or articles, consistent publishing from named experts, and clear, citable claims. Semrush found that roughly 95% of cited LinkedIn content was original rather than reshared, and Moore has highlighted research showing LinkedIn articles in the 500–2,000-word range account for a large share of AI citations (but, again, emphasizes that it’s helpfulness—not word count range—that matters). The big takeaway is that AI systems increasingly evaluate distributed expertise signals across the open web—not just what ranks in Google. That idea applies to more than LinkedIn, too. Krista Doyle’s analysis shows that platforms like Reddit are becoming increasingly important because AI systems learn from real community discussion, not just brand-owned content. The visibility that emerges there is rarely driven by aggressive optimization. It comes from consistent participation, demonstrated expertise, usefulness, and genuine trust built inside communities over time. In other words, AI systems increasingly reward signals that look less like traditional SEO manipulation and more like authentic reputation. Google is reinforcing this direction, too. In its guidance around helpful content and AI search, Google warns against inauthentic mentions and low-value tactics created primarily to manipulate visibility. At the same time, the systems surfacing content are becoming far more sophisticated. According to Mike King, many AI search systems now operate through agentic retrieval. Instead of retrieving one set of results and generating an answer, they may break a query into multiple sub-queries, retrieve information repeatedly, compare passages against competing sources, and filter results through reranking and reflection stages before generating a final answer. That raises the bar for content. A strong page isn’t enough if the individual passages are vague, stale, generic, or hard to extract. Content now has to compete at the passage, claim, expertise, and trust level—not just the page level. 6. Freshness is a citation signalIt’s worth mentioning that freshness plays a huge role in what content appears in search results. Seer Interactive analyzed 5,000+ URLs across ChatGPT, Perplexity, and AI Overviews and found that 65% of AI bot hits targeted content published within the past year, 79% from the last two years, and 89% from the last three years. The old evergreen content model of "write it once, optimize it once, let it rank forever" is incompatible with how AI retrieval systems (and search engines) evaluate sources. A brief that doesn't account for a content refresh cycle is producing content with a built-in expiration date for AI visibility. What this means before we get to the briefI’m not sure anyone has formally studied the relationship between brief quality and content performance in this new environment. I haven’t found published research connecting brief structure to AI citability, cross-platform visibility, or the kind of depth that earns clicks after an AI summary. That gap is part of what this piece is trying to close. What the research does make clear is the shape of what content needs to do now. It needs to be found—across Google, across AI systems, across the platforms people use to validate what they've already read. It needs to say something specific enough to be citable and deep enough to be worth clicking. It needs a voice distinctive enough that an AI summary can't fully replace it. And it needs to be fresh enough that retrieval systems still consider it current. The brief is where all those requirements either get built in from the start or aren't built in at all. So let's build a better one.
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