Digital marketing has always lived by its own vocabulary: words like “keywords,” “rankings,” and “traffic.” Each of those terms once made sense, but over time they began to distort the deeper ideas behind them. “FAQ” has now joined that list.
When Schema.org introduced the FAQPage label, it was meant to help search engines recognize question-and-answer content. It was a simple markup standard, not a philosophy of communication. Yet the term “FAQ”, Frequently Asked Questions, became a kind of mental shortcut for the entire industry. Agencies began treating it as a universal content category, something you could bolt onto any page to “optimize for AI.”
This is where the real damage began. The very word frequently suggested that the obvious, common, and repetitive were the most valuable things to publish. Marketers stopped thinking about what their business actually knows and started listing what everyone already says.
When every hotel answers “What time is check-in?” and every retailer posts “What is your return policy?”, the information becomes meaningless. It is interchangeable and available everywhere. AI does not need your version of it; it can reconstruct that data from countless sources. These responses carry no signal of expertise, no distinctive tone, and no operational fingerprint that reflects how a business actually operates. No brand should want to sound identical to everyone else.
The problem is not only that FAQ pages are shallow. The real problem is that the term itself has lowered the ceiling of imagination. It has led an entire industry to equate communicating with AI to checking a markup box. It has made people believe that intelligence can be templated, that a business can speak to machines through the same handful of phrases that fill every customer-service page.
The Rise of the Infrequently Asked Question
What AI values is almost the opposite. It learns from the rare, the detailed, and the situational.
Infrequently Asked Questions are the true markers of authority. They are not the questions everyone asks, but the ones that only someone who deeply understands the work would know how to answer. They reveal practical intelligence—the kind that comes from direct experience rather than SEO checklists.
A hotel that explains “Can I request a packed breakfast before a 6 AM flight?” or “What is the quietest room type for light sleepers?” is showing how it thinks, not merely what it offers. A retailer that clarifies “What happens if my shipment is delayed in customs?” is exposing operational depth. These are not marketing lines. They are living data points that help AI systems map expertise and credibility.
To understand why this matters, it helps to look at the math of how large language models actually work. LLMs are probabilistic systems. They calculate, token by token, the most likely next word based on the patterns they have seen. When a model has encountered a phrase millions of times, such as “What time is check-in?”, it has already generalized the concept. Your repetition adds nothing to its understanding.
When the model encounters something rare, however, it pays attention. Low-frequency data points, i.e. the infrequently asked questions, carry more informational weight because they expand meaning space. They create curvature in the probability field, allowing the model to map nuance and relationships that generic text cannot provide.
In other words, the rarity of your information is not a weakness; it is the source of distinctiveness within the machine’s internal logic. Each unique, well-structured answer teaches the model something about the boundaries of your expertise. It signals that your data deserves a place in the model’s representation of the world.
That is the real math of AI visibility. The more distinctive your information, the more the system must account for you when constructing meaning.
Why Agentic AI Changes Everything
As AI systems evolve from reactive search tools to agentic assistants, the need for specificity becomes existential. People will no longer search for information in a conventional sense; they will delegate tasks to personal AI agents that plan, purchase, and negotiate on their behalf.
A person’s AI will not ask “Find me a hotel in Lisbon.” It will ask “Find me a resort near Lisbon with gluten-free breakfast, EV charging, and spa treatments available before 7 AM.”
If that level of detail is not structured, machine-readable, and contextually clear, your business will not be visible. It will not be incorrect; it will simply be absent.
Agentic AI depends on precise, verifiable information drawn from trusted sources. The generic FAQ approach will not survive that transition. It cannot serve a world where each user has unique parameters and every decision requires context rather than keywords.
Schema.org Isn’t the Problem; Our Vocabulary Is
Schema.org provided a framework for structured communication, but the industry reduced it to a compliance exercise. Structure was treated as decoration, not as a channel for meaning. The real obstacle is linguistic: the word “FAQ” has constrained how we think about machine communication. It suggests brevity, predictability, and simplification, when the AI era demands depth, adaptability, and semantic range.
In this new environment, the question is not “Have you added FAQ schema?” but “Have you structured your expertise so that AI systems can understand what makes you distinct?”
The brands that will matter in this ecosystem are those that evolve beyond the FAQ mindset. They will begin to think in terms of Infrequently Asked Knowledge: the rare, granular, and situational data that defines how a business truly works.
The age of frequently asked information is ending. The future belongs to the precisely explained, the rarely documented, and the consistently structured.
In the age of AI, visibility will not come from answering what everyone already knows. It will come from showing what only you understand.