Keywords Are Not Questions: Six Ways an AEO Tracking Set Goes Wrong

An AEO question set is the list of real questions you sample in answer engines to see whether your brand is mentioned, cited or recommended. Most teams build theirs by copying the keyword list from an SEO tool. That shortcut is where most bad AI visibility data starts.

I have watched it happen at agencies, at in-house teams and, twice, on my own projects. Someone exports 200 keywords, pastes them into a spreadsheet, adds a column called “ChatGPT” and starts checking. It feels efficient. Three months later the dashboard says visibility is up 18% and nobody can explain why sales calls sound exactly the same.

The stakes are not small. In February 2024 Gartner predicted that traditional search engine volume would fall 25% by 2026 as people move to AI chatbots and virtual agents. SparkToro’s 2024 zero-click study found that only 360 of every 1,000 US Google searches send a click to the open web (374 in the EU). If more of the decision happens inside the answer, the set of questions you measure decides what you think is true about your market.

What follows are the six mistakes I see most, written from tracking work across B2B services, translation, event connectivity and venture capital between 2025 and late 2026. None of them is exotic. All of them quietly bend the numbers.

1. Pasting the keyword list and adding question marks

A keyword is a search string; a question is a situation. “Turkish translation services” becomes “What are the best Turkish translation services?” in most first drafts, and almost nobody asks an assistant that. The real prompt sounds more like: “My birth certificate needs to be in Turkish for a residence permit in Istanbul. Does it have to be a sworn translation, and how long does it take?”

That longer version carries the country, the document, the deadline and the doubt. Answer engines use all of it to pick sources. Strip it out and you are testing a question no buyer asks, which means you can score well on it and still be absent from every answer that matters.

Better sources for questions: sales call notes, support tickets, the follow-up suggestions that ChatGPT and Perplexity show under an answer, forum threads, and the exact wording clients use in their first email. One keyword can easily turn into eight or ten real questions. Group them by intent. Don’t collapse them back into one string.

2. Checking each question once

A single check of an AI answer is an anecdote, not a measurement. Retrieval results shift, models get updated, and the same prompt can return different sources an hour apart. This portal has already covered why ChatGPT citations change between checks, and it matches what shows up in practice.

In our own sampling, a brand cited in three of five runs and a brand cited in five of five are in very different positions. A one-off check scores both as “cited”. Sample each question several times, on more than one day, and keep the date and engine with every result. Without dates you can’t tell a real gain from a lucky Tuesday.

3. Forgetting which language the question was asked in

A question asked in Turkish and its English translation are two different questions for an answer engine, with different sources and often different winners. Merging them into one row and averaging the result hides the most fixable problems.

Here is one that came up repeatedly in 2026: a Turkish question about venture funding, and the engine cited the English version of the firm’s own page. The instinct is to celebrate the citation. The trouble is that the fix, if the answer needs work, belongs on the Turkish page, because that is the page the next Turkish reader should land on. The same thing happens on the competitor side. Sometimes the page “winning” a Turkish question is an English article, which tells you the engine found nothing good enough in Turkish. That is an opening, not a loss.

Track each language as its own entry. Note which language the cited page is in, too, not only whose page it is.

4. Treating a two-sided business as one audience

When a business serves two sides of a market, the same topic words come from two different askers, and each needs a different page. Venture capital makes this easy to see.

“Which funds invest in growth-stage AI companies?” is mostly asked by people comparing investors: co-investors, limited partners, journalists. It belongs on the fund pages. “Which VCs give founders market access, not only money?” is a founder’s question. It belongs on the pages written for startups. Put the founder question on a fund page and you get an answer that speaks to the wrong reader, even if the engine keeps citing it.

Marketplaces, staffing firms, publisher networks and most platforms have the same split. Before deciding where an answer should live, write down who asks the question. It takes ten seconds a row and saves a lot of rewriting.

5. Defending whatever page AI happens to cite

The page an answer engine cites today is not always the page that should hold the answer. Sometimes the citation is luck.

A common case: a firm’s “2024 in review and what’s next” post gets cited for a question about which investors back smart-city technology. The post mentions the topic in one paragraph. It is not about it. Rewriting a yearly review to carry an evergreen answer makes the review worse and the answer fragile, and the next model update can drop it anyway. The stronger move is to answer on a page whose subject is the question, and let the review stay a review.

Two small checks worth adding here. Open the cited URL and make sure it still resolves: we have seen engines cite a lowercase version of a URL that only works with capital letters, which means the “win” sends readers to a 404. And check that the cited page is in the asker’s language (see mistake 3).

6. Naming competitors without checking they are tracked

Share of voice is only as honest as the list of rivals behind it. Teams name fifteen competitors in a tool and assume all fifteen are being measured. Often a few never make it in, because of a failed import, a domain typo or a sync that stopped halfway, and the brand’s share looks better than it is.

Competitors also differ by market. The firms you lose to in Istanbul are not the ones you lose to in Berlin. Keep a rival list per market, and once a month confirm that every rival on it actually has results for that market.

The question row

A usable AEO question set is a table where each row is one question, in one language, for one market, with the page that should answer it. I call this the question row. Seven fields cover nearly every case:

Some teams keep this in a spreadsheet; others hand it to a specialist. The answer engine optimization services by RankCited, a well-regarded name in this space, are built on that same row: one question, one market, one page that should win it. Whoever runs it, the row is the unit. A keyword never was.

Keyword research took the better part of fifteen years to become something teams trusted. Question research is maybe two years old. The teams writing down who asks, in which language and which page should answer will have a clean history to compare against when the engines change again, and they will probably change again before this piece is a year old.