You published AI friendly content, added schema, maybe bought a tracking tool, and the assistants still do not mention you. Before you conclude GEO is a scam, check these causes in order, because almost every stalled GEO effort is one of them.
AI models refresh their reading of the web on their own cycles, and changes typically take weeks to show up in answers. If it has been two weeks, nothing is wrong yet. Set a monthly re-check against the same baseline questions. The free gives you a repeatable baseline, and how to know if your GEO is working covers the metrics worth tracking.
Most stalled GEO looks like this: pages built for best-X phrases while buyers describe situations to AI in plain human language. Assistants shortlist brands from the situation, upstream of any keyword. Read what you are being taught about AI visibility is wrong, then map your buyers actual moments before writing another page.
If your site says one thing, your directories say another, and third parties barely mention you, assistants lower their confidence and recommend someone easier to verify. Fix fact consistency first, then earn mentions on the review sites and communities assistants already cite in your category. How to get recommended by AI walks through the input layer.
If you have fixed all three and cannot keep up with re-checking four assistants across every buyer moment monthly, that is not failure, that is the point where software takes over. Aethon runs the loop continuously and publishes the fixes, from $199 per month.
Stop guessing which cause is yours and test them in order. Pull the last answer where a competitor was named instead of you and read the sources the assistant cited: if none of them are pages you control, your problem is citations, not content. Next, take your most important buyer question and check whether any page on your site answers it in the first hundred words; if the answer lives in paragraph six or a PDF, that is the fix. Then search your brand name plus your category on the open web and count contradictions, old pricing, stale descriptions, dead directory listings; each one is a confidence tax. Finally, check recency: if the work shipped less than four weeks ago, the only honest diagnosis is too early. One hour, four checks, and you leave with a named problem instead of a vague worry.
Sometimes the diagnosis comes back clean and you still are not named, and then it is worth checking the shape of your category's moments. Some categories produce answers with no brand names at all, pure how-to responses, and forcing your name into them is swimming upstream; the win there is being the cited source, not the recommended vendor. Other categories have shortlists locked by brands with a decade of reviews, where the entry point is the situational long tail, the specific use cases incumbents are generic about, exactly the upstream ground covered in what you are being taught about AI visibility is wrong. Reading which game your category is playing is half of strategy, and it is one of the first things we map in the free audit.
The dangerous window is weeks four through ten: work shipped, answers stubborn, stakeholders restless. Bridge it with leading indicators that move before recommendations do: citations of your pages appearing in answer sources, brand mentions with accurate framing even without a recommendation, indexation and rich-result status on your fixed pages, and community threads gaining traction. Report those weekly alongside the unmoved headline number and the model refresh calendar, so nobody mistakes lag for failure. Internally, pre-commit the evaluation date, one full quarter from first fix, and hold both directions: no panic rewrites before it, no excuses at it. Programs die in this window from silence more than from results; a two-line weekly note with leading indicators is usually the whole difference.
Weeks, typically one to three months for meaningful movement, because assistants refresh on their own cycles. Overnight changes are not realistic and vendors promising them are overselling.
Tools that only monitor cannot fail or succeed at changing answers, they only report them. If the answers are not changing, the inputs have not changed enough: moments coverage, on-page answers, schema, citations, and consistency.
No, they compound. SEO earns the pages and authority that assistants read. GEO makes sure what they read leads to your brand being recommended. See GEO vs SEO for how they fit.
Ship in small batches, three to five fixes, then re-measure on the monthly cycle. Shipping everything at once feels productive but destroys attribution, and attribution is what tells you where the next dollar goes.
Check which moments moved. Rising mentions in moments far from purchase feel good and convert nothing; the basket needs weighting toward the questions buyers ask when money is close.
It happens more than people think: robots rules, firewalls, and bot protection can silently block AI readers. Verify your robots.txt allows the major AI crawlers and that your host's security layer is not challenging them.
Source citations: your fixed pages appearing among the documents assistants read, often weeks before the recommendation itself flips. Track cited sources, not just named brands.
No, they answer different clocks. Keep paying for the demand ads capture today; let GEO compound toward the demand ads never see. The budget conversation comes after the quarter, with data.