How to Use AI to Plan a Content Hub and Spoke Structure

Last updated: October 12, 2026

Quick Answer: To use AI to plan a content hub and spoke structure, ask it to generate and group topic ideas, then manually prune them into 1 hub page and 6–12 spoke pages that each serve a distinct search intent.

AI can help you map how to use AI to plan a content hub and spoke structure fast, but only if you use it for structure, not for blind topic dumping. If you already know your audience and need a cleaner way to turn one broad topic into a hub page plus supporting pages, a practical approach is to use AI to generate, group, prune, and stress-test ideas — then make the final call yourself.

What AI should do in a hub-and-spoke plan

AI should turn a messy topic list into a clean information architecture, not decide your strategy for you. If your site is still at the “we need a pillar page and some cluster articles” stage, AI is useful. If your topic is tightly regulated, highly technical, or tied to legal or medical advice, AI can help with outline work but the structure needs human review from someone who knows the field, ideally alongside guidance from sources such as Google Search Central and NHS guidance.

The basic job is simple: one hub page covers the main subject at a broad level, and spoke pages answer narrower questions that feed back into it. A good hub and spoke structure is not a random pile of articles with links between them. It is a hierarchy. The hub owns the head term. The spokes own the subquestions, comparisons, procedures, and edge cases.

I would use AI for four things first:

1. Expand one seed topic into 30–100 related questions.
2. Group those questions into clean themes.
3. Spot gaps, overlaps, and cannibalization risks.
4. Draft a link map from hub to spokes and between spokes where it actually helps a reader.

If AI is being asked to invent the strategy from scratch, I think that’s where people get into trouble. It often produces too many near-duplicates and too little intent separation.

Quick check: if your main problem is “I have too many possible article ideas and no structure,” this section is for you.

How do I get AI to build the first topic map?

You get better output if you ask AI to act like an information architect, not a writer. A vague prompt such as “give me content ideas for CRM software” will usually produce a noisy list. A better prompt names the job, the audience, the business goal, and the format.

Try this:

“Act as an SEO information architect. My topic is [topic]. My audience is [audience]. My goal is to build one hub page and 8–12 spokes that cover the topic without overlap. Give me: 1) core subtopics, 2) questions by intent, 3) likely hub page angle, 4) spoke page titles, 5) gaps and duplicates.”

Then tell the model to sort ideas by intent:

• informational
• comparison
• how-to
• troubleshooting
• beginner glossary
• decision support

That matters because a hub page usually should not try to satisfy every intent equally. If your topic is “email marketing,” the hub may be the broad explainer and glossary, while spokes handle list building, deliverability, segmentation, automation, subject lines, and compliance.

Here is the workflow I’d use:

1. Paste a single seed topic and one audience description into the model.
2. Ask for 50 related questions, not 50 article titles.
3. Tell it to remove anything that is pure synonym drift.
4. Group what remains into 5–8 clusters by user intent.
5. Mark one cluster as the hub candidate and the rest as spokes.
6. Ask for overlap warnings: which two ideas would compete for the same query?
7. Review the list manually and delete anything you would not want on the site in 12 months.

If you want a benchmark, a practical first map often ends up with 1 hub page and 8–15 spoke pages, not 40. More than that usually means the topic is too broad or the clustering is too loose.

Quick check: if your AI prompt returns lots of similar titles with tiny wording changes, the prompt is too vague.

Which hub-and-spoke structure should I choose?

You should choose the structure that matches search intent, not the structure that looks biggest. If the topic is a narrow tool or service, a single hub with 6–10 spokes often works. If the topic is a broad field like project management or nutrition, you may need a two-level structure: one main hub and several sub-hubs under it.

Here is the choice I would make in common cases:

If you are in the first camp, keep the structure simple. If you are in the second, use AI to create an outline tree rather than a single page list. For example, “content marketing” might become:

• Hub: content marketing strategy
• Sub-hubs: content planning, distribution, measurement
• Spokes: editorial calendar, briefs, repurposing, attribution, content audit, topic clusters

The mistake is to make every spoke sit at the same depth even when the topic demands a layer in between. The other mistake is the opposite: a huge three-tier tree for a topic that only needs one hub and a few focused articles.

Quick check: if your topic has more than three distinct user intents, think about sub-hubs.

The workflow I use to turn AI output into a publishable plan

I turn AI output into a publishable plan by forcing it through three filters: intent, redundancy, and usefulness. If a topic idea fails any one of those, it goes in the bin.

Start with a working prompt set. You do not need one perfect prompt; you need a sequence.

1. Generate the raw question pool.
2. Ask the model to tag each item by intent and funnel stage.
3. Ask for semantic duplicates and near-duplicates.
4. Ask which items belong on the hub versus a spoke versus a sub-hub.
5. Ask for suggested internal links between pages.
6. Ask what is missing from the plan that a real user would expect.

Then inspect the output like a skeptic. I would look for four signs of a bad plan:

• two spokes that answer the same query
• a hub page that is really just another spoke
• a cluster built around writer convenience instead of reader need
• topics that sound interesting but have no business value

A useful benchmark here is page purpose. Every planned page should have one sentence that starts with “This page helps the reader…” If you cannot finish that sentence cleanly, the page probably should not exist.

For tools, I’d use ChatGPT, Claude, or Gemini for the first pass, then a spreadsheet for the real work. Put the raw ideas in one column, intent in another, page type in a third, and priority in a fourth. That simple grid prevents the plan from turning into a paragraph nobody can act on, and a careful review with a qualified editor or subject expert is still wise for anything sensitive or high-stakes, as guidance from Google Search Central and the FTC makes clear.

Quick check: if you cannot explain why each page exists in one sentence, the plan is not ready.

What AI gets wrong if you let it plan alone

AI gets the structure wrong when it confuses related topics with useful page boundaries. If you let it run without guardrails, it often creates pages that are too narrow, too broad, or too repetitive. It also tends to overestimate how much a reader wants separate pages for slightly different phrasing.

That matters because a hub-and-spoke site lives or dies on distinction. If the plan produces five pages that could all be titled “How to improve X,” then the architecture is broken. If the hub is stuffed with every subtopic, then the spokes have no job.

The standard failure modes are:

• Keyword mirroring: the model turns every query variation into a separate page.
• Intent blur: beginner questions, buying questions, and troubleshooting questions land on the same page.
• Spoke inflation: it creates a page for a topic that could have been a section.
• Hub bloat: it makes the hub page so long that it stops being a hub.

The fix is to ask more specific questions. For example:

• “Which items deserve standalone pages because the intent changes?”
• “Which items should be sections inside the hub?”
• “Which pages would compete in Google results if published separately?”
• “What can be combined without losing clarity?”

This is also where you should set a minimum content depth. If a proposed spoke cannot reasonably support at least 600–800 words of useful explanation, a couple of examples, and one meaningful subheading, it may not deserve its own page. That is a practical rule of thumb, not a universal standard, so if the topic is regulated or customer-facing, consult a professional before deciding page boundaries.

Who this is not for: if you want AI to spit out a finished calendar in one prompt and never touch the plan again, this workflow will disappoint you.

Quick check: if the plan feels impressive but thin, you have too many pages and not enough purpose.

Edge cases where the normal advice breaks down

AI planning changes when the site, subject, or business model breaks the usual hub-spoke pattern. If one of these cases applies, do not follow the default playbook.

1. You already have a large site with overlapping pages
   What changes: you are not starting fresh; you are untangling cannibalization.
   What to do instead: ask AI to audit existing URLs, group them by intent, and flag consolidation candidates. Then decide whether to merge, redirect, or reframe.

2. The topic is highly regulated, like finance or health
   What changes: accuracy and scope matter more than volume.
   What to do instead: keep AI on structure and plain-language drafts only. Have a qualified expert check any claims. If a page could trigger professional advice concerns, keep it narrow and general. For finance and health topics, a source like the FTC or NHS can be a better starting point than a model-generated guess.

3. The search space is tiny
   What changes: a full hub plus a dozen spokes may be overkill.
   What to do instead: build one strong guide and a few support pages. If there are only 3–5 meaningful subquestions, forcing a larger cluster wastes time.

4. Your product has many features, but users only care about a few tasks
   What changes: feature-based pages are less useful than task-based pages.
   What to do instead: prompt AI to organize by jobs-to-be-done, not by product menu labels. For example, “how to import data” is usually stronger than “data import settings.”

5. You need content fast for a launch
   What changes: speed beats elegance for the first version.
   What to do instead: build a lean hub with 4–6 spokes, publish, then expand after you see real search data in Search Console and your analytics platform.

6. You serve multiple audiences in one site
   What changes: one hub may not fit all readers.
   What to do instead: create separate spokes or even separate hubs by audience. A beginner and an enterprise buyer should not land on the same structure unless the page clearly routes them.

Quick check: if your site has legal risk, a tiny search space, or an existing mess, the clean textbook structure is probably not the right one.

How I would turn the final plan into pages

I would turn the final plan into pages by assigning one job to each URL and one primary query set to each page. If a page cannot be named in a way that a real person would say out loud, I would reconsider it.

Use this order:

1. Pick the hub page and define its promise in one sentence.
2. List the 6–10 spoke pages that answer the next most useful questions.
3. Mark any sub-hubs that need their own small cluster.
4. Assign one primary intent and two secondary intents to each page.
5. Write the internal link pattern: hub links to every spoke, spokes link back to hub, and only link sideways when it helps the reader.
6. Set a review rule so new pages do not duplicate existing ones.

A practical standard I like is this: if two page titles could both answer the same search query without a clear difference in intent, they do not both get published. That rule saves a lot of cleanup later.

For source quality, I would also sanity-check the plan against authoritative guidance on search intent and site structure from organizations such as Google Search Central and the Moz learning resources. Those pages exist as general references even if your exact topic is unique.

Quick check: if each planned page has one clear purpose and one primary audience question, the architecture is ready to build.

FAQ

How many spoke pages should a hub have?

A hub usually works best with 6–12 spokes, but I would use fewer if the topic is narrow and more only if the subtopics are genuinely distinct.

Should AI write the hub page outline too?

Yes, but only after the structure is set. AI can draft the outline, yet the page order and section boundaries still need a human decision.

Can I use AI to find internal link opportunities?

Yes. Ask it to map hub-to-spoke and spoke-to-hub links, then remove any links that do not help a reader move to the next logical question.

What is the biggest mistake people make here?

They publish too many similar pages and call it a cluster. If the pages do not serve different intents, the cluster is noise.

Do I need special software?

No. A good AI model, a spreadsheet, and Search Console are enough for a first-pass plan.

Drafted with AI; not yet reviewed by a person.