A former client asked me why a series of new pages they had created specifically to improve their AI visibility weren’t appearing in AI Search and weren’t performing particularly well in Google either. The team had done many of the things organizations are now being encouraged to do: Analyze what appeared in AI answers, review competitors and cited sources, identify gaps, and create more comprehensive content to improve their chances of being retrieved and cited.
On a hunch, we looked at Google Search Console and found that many of these newly enhanced pages had the status “Crawled – currently not indexed.” The GEO consultant said indexing takes time, but it had already been more than a month. Based on recent articles about this error, we believed there was a different question: Did these pages actually add anything?
We used a variation of an information-gain evaluation prompt Marie Haynes shared in an earlier Search Engine Journal article to compare the new pages against the company’s existing content and sources already surfacing for the relevant questions. The results weren’t encouraging. There was relatively little information beyond what the company had already published and little differentiation from the cited sources.
The content wasn’t necessarily bad. Much of it was comprehensive and well organized. The problem was that it had largely achieved what I call me-too parity, resulting in a near-mirror replication of top-performing content, and it seems that parity was apparently not enough.
When I tried to explain the distinction between competitive content gaps and information gain, the prospect asked two very reasonable questions: If we’re not supposed to create what the successful pages already have, how do we know what to create? And if the answer isn’t in our content or something we can scrape from a competitor, where are we supposed to get it?
Those questions expose the two problems I think AI-assisted content workflows now need to solve: identifying something genuinely additive and determining where that new information will come from.
Because no matter how sophisticated the content-generation system becomes, the content still has to come from somewhere.
AI Has Made Content Parity Remarkably Efficient #
Today’s content intelligence tools can run prompts, identify citations, crawl competitors, compare topical coverage, find missing concepts, and generate content to close those gaps. That capability is useful, and this isn’t an argument against it. If every competitor explains an important product specification and you don’t, you probably have a gap worth closing.
The limitation appears when we treat that difference as information gain.
If every input into the workflow comes from information already published, the resulting content is constrained by the existing information environment. We can reorganize it, clarify it, combine multiple sources, and make it more comprehensive, but we haven’t necessarily introduced anything new. We have identified the gap between them and us. We haven’t yet identified the gap between what currently exists and what the customer and LLMs need to make a decision.
That requires a different kind of analysis and, strangely enough, may require us to use our brains again.
See also: Does AI Actually Reward Quality Content?
Move From Content Gaps To Decision Gaps #
Consider a prompt I’ve used frequently in presentations:
“What is the best all-inclusive, family-friendly, beachfront resort in Cancun?”
It is tempting to treat this as a very long keyword containing several concepts. But the customer isn’t really asking for a page containing “all-inclusive,” “family-friendly,” “beachfront,” and “Cancun.” They’re asking for a decision.
Before an AI system can recommend the “best” resort, it somehow has to evaluate what qualifies as all-inclusive, family-friendly, beachfront, and even Cancun. Some requirements may function as eligibility gates while others influence the relative strength of the properties that qualify.
That changes the content problem. Instead of asking whether our page covers each concept, we need to determine what evidence someone would require to evaluate each criterion.
I expected “all-inclusive” to be the easy one. It wasn’t. Really, how hard is it to list what is, and isn’t included? Turns out, surprisingly hard. Marketing does a great job selling the promise of “all-inclusive,” but often a much poorer job defining where “all” ends.
One high-end resort provides a reasonably useful definition in an FAQ toward the bottom of the resort overview page. Its explanation indicates that the package typically covers accommodations, dining, alcoholic and non-alcoholic beverages, sports and activities, entertainment, and children’s programs. At the same time, certain excursions and spa treatments may incur an extra charge.
Another popular resort has an entire page dedicated to its all-inclusive experience, covering dining, drinks, room service, accommodations, water sports, Wi-Fi, snacks, and entertainment. There is plenty of content, but it is surprisingly difficult to find a definitive explanation of exactly what “all-inclusive” means or whether everything described so enthusiastically on the page is actually included. For example, non-motorized water sports are included, but deeper content about its FlowRider surfing simulator explains that regular access is first-come, first-served, while private time slots are available for an additional charge.
The problem isn’t a lack of content. The “all-inclusive” page is full of content. The problem is that the content doesn’t fully resolve the criterion. The customer, or an AI system attempting to answer on the customer’s behalf, still has to assemble the definition from information distributed across the site and determine where “all-inclusive” ends and “additional charge” begins.
Once we treat “all-inclusive” as a decision criterion rather than a marketing label, additional questions appear quickly. Are all restaurants included? Which beverages? What about children’s programs, transfers, gratuities, premium activities, or reservations?
These aren’t necessarily topics requiring another collection of articles. They are pieces of evidence required to understand the original claim.
See also: AI Search Didn’t Remove Cognitive Load, It Moved It
Coverage Isn’t The Same As Qualification #
I encountered the same problem with “family-friendly.” One resort had what appeared to be excellent coverage: a dedicated kids-and-family section, distinct programs by age group, distinctive program names, and “Learn More” links.
Then I looked at the program for 14- to 17-year-olds. Under the heading “Childcare,” the description emphasized freedom, kindness, meeting new people, exploring together, and energetic staff ensuring teenagers have a great time.
It sounds appealing, but imagine you’re the parent of a 15-year-old, deciding whether this resort is a good fit for your family. You still don’t know whether this is supervised childcare or optional activities, what the hours are, what activities are offered, whether teenagers leave the property, what level of supervision exists, whether they can come, and go independently, or whether activities cost extra.
The topic is covered. The decision isn’t. A competitive content tool could correctly conclude that the resort has strong topical coverage around teen programs. Another resort could analyze it, create its own age-segmented pages, and achieve parity relatively quickly.
But the parent isn’t evaluating which resort has the best content architecture. They’re deciding which program satisfies their family’s requirements.
The gap isn’t necessarily “we need more content about teenagers.” It may be “we haven’t answered the questions necessary for a parent to evaluate our teenage program.”
This is also where query fan-out analysis becomes useful as a diagnostic tool rather than simply a content-generation engine. If “family-friendly” produces secondary questions, don’t automatically turn each question into another content brief or FAQ. Try to understand why the secondary question was necessary and what ambiguity or evidence criteria it is attempting to resolve.
The fan-out may be showing us the evidence necessary to substantiate the original claim.
A Gap Is A Diagnostic Signal, Not A Content Brief #
Once we start looking at decision criteria rather than topics, different kinds of gaps become visible. Someone else may answer an important question that we don’t, which is the traditional parity gap. No one can adequately answer it, leaving us with potential informational white space. We may already possess the answer but have it fragmented across multiple pages, systems, or departments, creating more of a knowledge-connection problem than a content problem.
The information may also be absent intentionally. Some details aren’t sufficiently useful to publish, some are commercially sensitive, and some may be more appropriate later in the customer journey. The objective isn’t to expose everything the organization knows merely because an AI system might retrieve it.
There is another gap I increasingly see in AI-generated content: The information is correct but has almost no meaningful connection to the organization that publishes it.
When working on a hreflang project, I reviewed an enterprise glossary page about machine learning. It was a comprehensive explanation of the topic, but there was almost nothing connecting machine learning to the company’s own offering, aside from a link to a product page.
This is your website. If machine learning is something your company actually does, where is your experience? What do you do differently? Where have you applied it? What have you learned? What use cases fit particularly well?
That doesn’t mean turning an informational page into a sales pitch. It means contributing the knowledge and experience that justify your organization publishing the page in the first place.
A useful validation question is: Could one of our competitors publish this content by changing the logo and a couple of links?
If so, we may have achieved topical completeness while contributing very little of ourselves.
The Content Still Has To Come From Somewhere #
This brings us back to the second, and more important, question from the team: What happens when we’ve identified information that should exist but we can’t scrape it elsewhere? We may have to do research, and dare I say, we may even have to talk to another human being.
This is unfortunately where we have ended up. AI can analyze thousands of pages, cluster customer questions, summarize reviews, perform query fan-outs, compare competitors, and identify where the existing information environment fails to resolve an important criterion. What it cannot legitimately do is manufacture an organizational fact because the content template requires one.
If nobody has documented the supervision policy for the teen program, the model doesn’t know it. If nobody has established exactly which activities are included in the package, scraping five competitors won’t provide the answer. If nobody has measured the actual walking distance from the furthest room to the beach, asking AI for a better paragraph about beachfront access doesn’t create that knowledge. The answer may be sitting in customer service tickets, call center transcripts, internal site search queries, CRM notes, product documentation, surveys, training materials, or the heads of employees who answer these questions every day.
Or perhaps nobody knows. That’s useful too, because we’ve identified something customers need to decide that the organization itself has never formally addressed. At that point, content creation stops being a writing problem, and becomes a knowledge acquisition problem.
AI has dramatically reduced the cost of transforming knowledge into content. It has not eliminated the need to acquire the knowledge in the first place.
Sometimes Customers Create The Missing Content For Us #
This may also help explain why Reddit, reviews, forums, and other community sources can be useful for decision-oriented searches. People frequently discuss exactly the details businesses leave out.
A resort describes itself as family-friendly while a frustrated parent explains what really happened when they tried to enroll a three-year-old in the kids club. A manufacturer says assembly is easy, while a customer explains that one step requires a second person and a full toolbox. A hotel says it is five minutes from the beach while a traveler explains what that means with two children, a stroller, and beach chairs.
Those observations aren’t automatically authoritative, but they add specificity to the information environment. If the business provides only the broad marketing claim while customers provide the operational details, we shouldn’t be surprised when community content becomes useful to search engines and AI systems. Absence from your website doesn’t mean absence from the web. Someone else may already be answering the question for you, accurately or otherwise.
That creates an opportunity to provide a clearer, validated first-party answer rather than simply generating more content.
From Content Generation To Validated Content #
Putting these pieces together gives us a different model for AI-assisted content development.
We can still begin with prompts, competitive research, citations, and existing content because they provide a clear view of the current information environment. But instead of moving directly from gap analysis into generation, use that analysis to understand the customer decision.
We need to better understand, for prompt variations, which decision or weighting criteria must be satisfied. What information is necessary for the criteria, and does it already exist, is fragmented, or remains unresolved?
These are your qualification gaps. Once you understand them, we need to start by identifying whether we have the knowledge and information somewhere within the organization.
A new validated content workstream may flow as follows:
Customer Decision → Decision Criteria → Existing Evidence → Evidence Gaps → Gap Qualification → Knowledge Acquisition → Validation → Content
AI can make nearly every stage of that process faster. What changes is that we stop asking it to substitute for the point where new knowledge has to enter the system.
Parity Is The Minimum Threshold #
AI has made scaling content production extraordinarily inexpensive. Even without specialized tools, almost everyone can review citations, competitor content, commonly discussed topics, and generate a reasonably comprehensive page. As those capabilities become ubiquitous, producing the page itself becomes progressively less differentiating.
The advantage moves upstream to better understand the customer’s decision, identify criteria others overlooked, uncover operational knowledge hidden within the organization, connect it to your products and experience, and validate the answer before publishing it.
Sometimes that means analyzing query fan-out. Sometimes it means listening to customer service calls, reviewing site search queries, or discovering that Reddit users are answering a question the company never addressed.
And sometimes it means doing something that suddenly feels strangely old-fashioned: talking to a subject-matter expert and learning something new. AI can help us discover the gap, organize the evidence, and transform what we learn into useful content.
But genuine information gain still requires something to be gained.
More Resources:
- [How To Build An SEO Commissioning Workflow: From Tickets To Requirements](https://www.searchenginejournal.com/how-to-build-an-seo-commissioning-workflow/566093/)
- [Why Great Content Is No Longer Enough & What Beats It In AI Search](https://www.searchenginejournal.com/why-great-content-is-no-longer-enough-what-beats-it-in-ai-search/572001/)
- [The Content Framework That Worked In 2019 Is Now Working Against You](https://www.searchenginejournal.com/the-content-framework-that-worked-in-2019-is-now-working-against-you/579051/)
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