Content research for AI search looks at three things: the questions buyers actually ask at each stage of a decision, what competitors and other sources have already answered, and what nobody has covered well. It should also check the business's own pages first, since some answers exist but are buried. Good research ends in a list of questions to answer in priority order, not a list of keywords.
Keyword research asks which phrases people type and how often. That still matters, but buyers who use AI tools often ask full questions with their situation attached, and the follow-up questions shape the answer as much as the first one. Research for this kind of content has to capture the question, the situation behind it and the next thing the buyer will want to know.
This page covers research for planning content. It does not explain how any assistant chooses its sources, and it is not a measurement method; tracking what assistants say over time is covered in what a monthly AI visibility report should show.
The three inputs
Every useful research plan draws on the same three inputs, recorded in a way someone else can check later.
| Input | Where to look | What to record |
|---|---|---|
| Buyer questions | Sales and intake conversations, emails, reviews, Search Console queries | The question in the buyer's words, the situation behind it, and the stage of the decision |
| Existing answers | Competitor pages, directories, sources shown in AI answers, your own site | Who answers each question, how directly, and with what evidence |
| Gaps | The difference between the first two rows | Questions nobody answers well, and questions only your business can answer from its own knowledge |
The internal side of the first row, what your own team hears, is covered in our article on finding the buyer questions missing from your website. This page focuses on the market side.
Reading questions the way buyers ask them
Google Search Console's Performance report is one outside source. Google says it helps you see what search queries are most likely to show your site and learn which queries are bringing traffic to it. The same help page notes that even if a query appears in your list, you might not see your site in results, because results vary by time, place, device and the searcher's recent history. Treat the query list as evidence of how people phrase things, not as a picture of every answer they receive.
Follow-up questions deserve as much attention as the first one. Google's AI features documentation says AI Overviews and AI Mode may use query fan-out, issuing multiple related searches across subtopics and data sources. A reasonable reading is that the questions around a topic matter, not just the headline query, so research should map the chain a buyer follows. A keyword is not a question. "Furnace repair Dayton" tells you the topic; "Is it worth repairing a 15-year-old furnace?" tells you the decision.
Checking what has already been answered
For each priority question, read what a buyer would find today: competitor pages, directory listings, and the sources linked in AI answers when you ask the question yourself. Record the date and the platform, since answers change. Judge each existing answer on substance. Does it answer the question directly, does it state the conditions, and does it show evidence a buyer could check?
This is where research turns into a plan. Some questions are answered well by several competitors, and matching them is the minimum. Others are answered thinly or not at all. A question worth raising with your own team at this stage is, "Which of these could we answer better than anyone we compete with, and what proof would we show?" The questions where the honest answer is "we could, today" usually belong near the top of the list.
Local and segment detail
For a local business, research includes what is actually different about each place it serves: which office handles permits, the age of the housing, travel time, seasonal demand. Detail like that is what separates a useful city page from a template with the town name swapped in. Most of it comes from the business rather than the internet, so the researcher should not have to invent what your crews already know about working in each town; the job is to ask them.
National brands face the same problem in a different shape. Instead of towns, the research splits by segment, product line or buyer role, because a facilities manager and a procurement lead ask different questions about the same product. The output is the same: questions in priority order, each with a note on who in the business can answer it.
A fictional example: a research map for a physical therapy clinic
A fictional physical therapy clinic in Oregon wants to know which questions to answer first. Research across its intake notes, its Search Console queries and three competitor sites produces a short map. The questions are research findings to answer later, with the clinic's own clinicians writing or approving every answer.
| Question | Where it came from | Who answers it now | Priority |
|---|---|---|---|
| What should I bring to my first visit? | Intake calls | One competitor, briefly | High |
| How many visits will I need? | Intake calls, Search Console | Nobody directly | High |
| Do you work with my insurance? | Intake calls | Every competitor, as a list of logos | Medium |
| Can I do the exercises at home between visits? | Reviews | General articles, no local clinic | Medium |
| What should I do if my pain gets worse after a session? | Front desk notes | Nobody | High, with clinical review |
The second and fifth questions rank high because patients clearly ask them and nobody answers them. Both need a clinician's answer stated with its conditions, and the fifth needs the clinic's own approval before anything publishes. The research says what to answer; it does not supply the answer. More on this kind of work for clinics is on our medical, dental and wellness page.
How Hudson South handles this research
At Hudson South, this research is the third of four steps. It follows Authority Asset, which records the expertise the business already has, and Hudson Twenty-One, which records how the business communicates across 21 dimensions. The step is called Market Intelligence, and it researches what people in the market ask, what competitors have already answered and what nobody has covered. Local businesses get city pages with real local detail; national brands get the same work across segments, product lines or buyer roles. The output is a researched map of the questions to answer, in priority order, which AEO Creation turns into answer-first content.
In a project, the research runs as part of a build that finishes in 30 to 60 days. On a retainer, steps three and four repeat every week. The full service is on the AI search content page.
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- Performance report (Search results): Overview and basic setup. Search Console Help. Read 2026-10-17.
- AI features and your website. Google Search Central. Read 2026-10-17.