Search volume is no longer a reliable filter for deciding what to write about. According to Search Engine Journal (2025), it now systematically screens out the highest-intent questions buyers are actually asking, because those questions are being said to AI assistants rather than typed into a search box, where keyword tools can measure them. The content strategy has moved on; the prioritization method has not.
How Does AI Search Break the Volume Signal?
When a user prompts an AI assistant, the model does not run a single search against one keyword. Google’s developer documentation (2025) is explicit: “Both AI Overviews and AI Mode may use a ‘query fan-out’ technique, issuing multiple related searches across subtopics and data sources, to develop a response.” ChatGPT operates on the same underlying principle, rewriting the user’s original prompt into multiple search queries before retrieving content, according to OpenAI documentation cited by Search Engine Journal.
The practical result: Google’s documentation (2025) states that AI features “display a wider and more diverse set of helpful links associated with the response than with a classic web search.” Your page can be cited for a sub-question you never explicitly targeted, or left out of a topic you assumed you owned because you only addressed the headline term.
This is the core problem with using search volume to prioritize topics. It counts demand for strings people type. Prompts look nothing like that. Search Engine Journal (2025) contrasts the two patterns directly: a keyword search is a short, bare string such as “best CRM small business,” while an AI prompt is longer, conversational, and typically describes a situation, carrying constraints, a decision to make, and an implied objection. Each prompt also returns nothing in a volume column, because hardly anyone types that exact sentence.
The prompt form is also, per Search Engine Journal, “worth far more to the business than the traditional search, because the person asking provides context and is closer to acting.” The trap is that if you sort by volume, traditional search wins every time, while the actual prompt never makes it onto the list at all.
What Should Replace Volume as the Primary Filter?
Search Engine Journal (2025) identifies four shifts that do not require new tooling, they change what you are looking for in the inputs you already have.
Prioritize sub-questions, not the head term. Because a prompt fans out into multiple sub-queries, those sub-queries are the real targets. For “best CRM for small business,” that means covering pricing tiers, migration effort, integrations, contract length, and data portability. Most pages gesture at these; few genuinely answer them, according to Search Engine Journal.
Prioritize entities and concepts, not exact strings. AI synthesis matches on meaning, not phrasing. Covering a subject thoroughly, naming relevant products, standards, methods, and alternatives, and explaining how they relate, earns inclusion more reliably than repeating keyword variants. Search Engine Journal (2025) recommends “more pages built around a subject that’s covered thoroughly” over “pages built around variants of the same phrase.”
Prioritize the decision, not the definition. AI assistants deliver basic definitions instantly. People use them to decide. Comparisons, selection criteria, trade-offs, and objections are now primary targets, not sections bolted to the end of a buyer’s guide. Content that cannot serve a comparison prompt, per Search Engine Journal (2025), “won’t be much use to a model answering a comparison prompt.”
Keep volume for transactional queries. Search Engine Journal is clear that volume retains its value for short, settled queries: brand terms, product terms, local intent, and “near me” searches. For those, a classic search results page (SERP, the list of links Google returns after a search) remains the dominant surface, and volume is still the right signal. The problem is applying a transactional signal to decision-stage content.
Where to Find Demand That Keyword Tools Do Not Show
Search Engine Journal (2025) identifies sources already available to most teams:
- People Also Ask boxes and related searches reveal the sub-questions AI models are most likely to generate around a topic
- Reddit, Quora, and industry forums show how real people describe problems in context and close to a decision
- Internal sales calls and support tickets capture the exact language buyers use when they are evaluating options
- Querying an AI assistant directly with your head term and observing which sub-questions it chooses to address
Sales calls are noted by Search Engine Journal as particularly underrated, because “customers describe their problems to a salesperson in almost exactly the way they describe them to an assistant.”
How to Score Topics Without a Volume Number
For topics with no measurable search volume, Search Engine Journal (2025) proposes two proxies. First, business value: does answering this move someone closer to a purchase? As the article puts it, “a question asked by 40 people mid-decision beats one asked by 4,000 people who are idly curious.” Second, evidence the question is actually being asked, a People Also Ask entry, a recurring forum thread, or three mentions in sales calls each constitute proof of demand, even without a volume figure attached.
What This Means for AI-Search Visibility
Here is the simplest way to frame it for a business that is not deep in SEO: keyword tools show you where search traffic used to come from. They do not show you where AI-generated answers are being assembled from. Those are increasingly two different maps.
Consider a buyer who asks an AI assistant: “Which project management tool is easiest to migrate to if we’re leaving Asana?” A brand that has published a detailed, honest migration comparison earns a citation in that response. A brand that ranked well for “project management software” but never addressed the migration question does not appear, regardless of its overall SEO authority for the head term. The sub-question determined the outcome, not the volume ranking.
From a Hingewise perspective, this reveals a structural gap that most standard content audits are not designed to catch. Typical audits check technical SEO signals, keyword coverage, and traffic performance. Very few audit what we would call “decision-layer depth”: whether a page can actually serve the comparison, objection-handling, and constraint-specific prompts that AI systems are most frequently asked. A page that scores well in a standard audit can still be absent from AI-generated responses on its own topic, because it addresses the head term but not the sub-questions that fan out from it.
There is a compounding dynamic worth noting. Google’s documentation (2025) observes that “when people click from search results pages with AI Overviews, these clicks are higher quality, meaning, users are more likely to spend more time on the site.” If that pattern holds at scale, content that earns citations in AI responses will attract more engaged visitors than equivalent traffic from standard rankings. That makes the decision-layer coverage gap a progressively larger problem to leave unaddressed, not a stable one.
The metric worth adding to your tracking: not just whether your pages rank for head terms, but whether they appear as cited sources when someone asks an AI assistant the sub-questions and decision prompts your buyers are actually working through.
Before adjusting your content prioritization, check the following:
- Do your top-ranking pages answer the 8 to 10 sub-questions a buyer would need resolved before acting on the topic?
- Are your comparison pages detailed enough to serve a “vs.” or “which is better for X situation” prompt?
- Have you reviewed recent sales call transcripts or support tickets for recurring decision questions that your content does not yet address?
- Have you queried an AI assistant with your main topics and recorded which sub-questions it generates in response?
- Do your most important pages name and explain alternatives, competing standards, and trade-offs, or only cover your own approach?
- Are there high-traffic pages in your current strategy that exist primarily to capture volume, without genuinely resolving a decision?
The underlying mechanism, AI assistants fan out queries across many sub-topics and assemble answers from multiple sources, is documented in Google’s own guidance and is built into how these systems are designed to work. How content teams update their prioritization process in response is the variable worth watching.
Lam Nguyen · Hingewise
Sources:
Search Engine Journal, “Why Search Volume Is Screening Out Your Best Content Opportunities” (2025): https://www.searchenginejournal.com/why-search-volume-is-screening-out-your-best-content-opportunities/585048/
Google Search Central, “AI features in Google Search” (2025): https://developers.google.com/search/docs/appearance/ai-features