AI for SEO: How to Use AI at Every Step in 2026

Updated October 28, 2026, by the Rillow team. We make Rillow, an SEO data server for AI agents, and it comes up in this guide where it fits. There are no affiliate links here.

AI for SEO means using AI models and agents, such as ChatGPT, Claude, Gemini, Claude Code or Codex, to do the work of SEO: keyword research, reading what ranks, briefs, writing, technical audits, fixes and reports. AI is good at reading, sorting, writing and editing code. It is bad at numbers it doesn't have, because no model knows today's search volumes, rankings or your Search Console data unless you connect a source for them. This guide goes task by task, with what AI does well, the data each task needs, a prompt to paste and the mistake to watch for. The last part covers the other meaning of AI SEO: getting your pages cited in AI answers.

What AI can and can't do for SEO

AI handles any SEO task that comes down to reading or writing text. Give it a list of 300 keywords and it groups them by intent in a minute. Give it the top 10 pages for a search and it tells you what all of them cover. Give it your site's code and it finds the pages with missing titles and fixes them.

AI can't see anything that lives in a database it isn't connected to. ChatGPT, Claude and Gemini have no keyword volumes, no difficulty scores, no live Google rankings, no backlink counts and no access to your Search Console. Their web search returns pages, not numbers. When you ask for those numbers anyway, a model can still answer with one that looks precise, so treat any number it gives you without a tool as a guess.

You fix that by connecting a data source. In a chat app that means a connector, and in a coding agent such as Claude Code, Codex or Cursor it means an MCP server. Our list of the best SEO MCP servers compares 12 of them, from Ahrefs and Semrush to pay-as-you-go options.

AI for SEO, task by task

The rest of this guide takes the tasks where the gap between a good and a bad result is biggest.

Keyword research with AI

Keyword research is two jobs, and AI does only one of them on its own. Finding ideas, grouping them and judging what a searcher wants is reasoning, and any model does it well. Saying how many people search each idea, and how hard it is to rank, needs a keyword database such as Ahrefs, Semrush, DataForSEO or a tool built on one.

With data connected, ask for the whole job and tell the model where numbers must come from:

Find keyword ideas for [your product's main topic] with monthly search volume, difficulty and the change over the last year. Keep the ones someone who needs a product like mine would search. For the best 5, read Google's current top 10 and tell me whether my site can compete. Use only numbers from the tool, and say when you don't have one.

The last sentence matters more than it looks. It stops the model from filling gaps with plausible numbers, and it makes missing data visible.

The judgment call stays with you. When we planned this post, the agent's first proposals were Semrush and Ahrefs pricing pages, with 1,900 and 1,000 searches a month. The numbers were right, and the people searching them are shopping for an SEO suite, which Rillow isn't. Our agentic SEO guide shows that whole run.

Briefs and writing with AI

AI writes a usable first draft in minutes when you give it a brief. A good brief comes from Google's results for the keyword today: what every ranking page covers, which readers expect, and what none of them covers well, which is your angle.

Get Google's top 10 for "[keyword]". Read the three strongest pages and list what all of them cover and what none of them covers well. Then outline a page that answers the search better, using these facts about my product: [facts, numbers, screenshots, examples]. Show me the outline before you write.

A draft written only from the top 10 repeats the top 10. What makes a page worth ranking is what the other pages can't have: your own numbers, your product's real screens, a test you ran, an opinion you'd defend. Google's guide to optimizing for its AI features asks for "non-commodity content" with a unique point of view, and that applies to every page you publish.

Can AI write SEO content that ranks? Yes, if a person checks it. Google's guidance on generative AI content says AI "can be particularly useful when researching a topic, and to add structure to original content", and also that "it is critical to manually factcheck and review all AI-generated content for accuracy and trustworthiness before publishing." Using AI to generate many pages without adding value for users may break Google's spam policy on scaled content.

AI drafts also share habits that readers spot, such as a dash in every paragraph, stock openers about a fast-paced world and every list grouped in threes. Ask the model to cut those, or use a writing checklist. Rillow runs one on every draft an agent saves, next to the SEO checks.

Technical SEO with AI

A coding agent can read your templates, find the pages with a missing canonical tag, a duplicate title or an image without alt text, and fix them in your repository. That makes technical SEO one of the best uses of AI for a site you build yourself.

The common mistake is auditing the wrong copy of a page. The Australian agency One Egg tested more than 50 Claude SEO plugins and found Claude flagging canonical tags that were correct and missing product schema that was on every page, because it read an unrendered or cached copy. Their summary: "Use Claude. Don't trust it blind."

Audit [URL] for SEO. Fetch the page as Googlebot gets it, without running JavaScript, and check the title, meta description, H1, headings, canonical tag, internal links, image alt text and structured data. List each issue with its fix, most important first, and say which ones you confirmed in the HTML. Don't change anything yet.

Then ask for the fixes in your repository, small and one at a time, and read the diff before you commit. Redirects, robots.txt, noindex and canonical tags deserve the closest look, because one wrong rule can drop many pages from Google at once.

Reporting with AI

AI is good at turning Search Console exports into a short note on what changed. It is bad at telling a real trend from a normal week-to-week wobble unless you give it enough history. Compare equal periods and ask it to separate the two:

Compare the last 28 days of Search Console clicks and average position with the 28 days before, for my 20 pages with the most impressions. List the pages that moved most. For each one, say whether the change is bigger than its usual week-to-week range, and what changed on the page or in Google's results.

Search Console data runs a few days behind, so a change you shipped yesterday won't show yet.

Which AI to use for SEO

The tool depends on where your site lives and how much of the work you want the AI to do.

Chat assistants such as ChatGPT, Claude and Gemini suit research, outlines and drafts. They can't change your site, and they need connectors for SEO data.

Coding agents such as Claude Code, Codex, Cursor and Gemini CLI suit sites built in a code repository. They read your pages, make fixes, write posts as files and show every change as a diff. Connected to an SEO MCP server, they can run the whole loop from keyword research to tracking, which our agentic SEO guide walks through.

SEO suites with AI features, such as Semrush, Ahrefs and Surfer, add AI to their own data inside their own apps. Our list of AI SEO tools compares them, with prices checked on each maker's site.

If you build with an agent already, connecting data to it costs less than a new suite. Rillow's MCP server gives the agent keyword data, Google's top 10 with its AI answer, backlinks, daily rank checks, Search Console and Google Analytics, and a memory of your site, from $19 a month with 30 free credits to start. The setup page has the steps for each agent.

SEO for AI search: getting cited in AI answers

"AI SEO" also means the opposite direction: making your pages show up in answers from Google's AI Overviews, AI Mode, ChatGPT, Perplexity and Claude. People also call it GEO (generative engine optimization) or AEO (answer engine optimization).

For Google, the work is ordinary SEO. Google's guide to its generative AI features, updated July 10, 2026, says those features "are rooted in our core Search ranking and quality systems", and that optimizing for them is still SEO. The same guide says you don't need llms.txt or other "AI text files", and there is "no requirement to break your content into tiny pieces for AI". A page can be cited when it is indexed and can be shown with a snippet.

The stakes for clicks are real. Pew Research tracked the browsing of 900 US adults in March 2025 and found they clicked a regular result on 8% of Google visits that showed an AI summary, against 15% of visits without one.

Other assistants use their own crawlers. OpenAI's OAI-SearchBot crawls for ChatGPT search, PerplexityBot for Perplexity and Claude-SearchBot for Claude, and blocking one in robots.txt or at your CDN can keep your pages out of that assistant's answers. Blocking training crawlers such as GPTBot or ClaudeBot is a separate choice and doesn't remove you from search answers. Check your robots.txt and your CDN's bot settings before you assume an assistant can read your site.

Beyond access, what plausibly helps is what helps in search: rank for the searches behind the questions people ask, answer the question near the top of the page, state facts and numbers with their sources and dates, keep pages current, and earn real mentions on sites people trust. Nobody can make an assistant cite a page, and the same question gets different answers from run to run, so measure with a sample of searches over time.

How we use AI for rillow.ai's own SEO

We do rillow.ai's SEO with Claude Code connected to Rillow, and a person reads every page before it goes live. This guide is one example.

On October 28, 2026, the brief for "ai for seo" showed 6,600 searches a month and a keyword difficulty of 41, with "ai seo" and "ai in seo" at 6,600 each. Google's first page held a list of AI SEO tools from Semrush, guides from Nightwatch and Kuno Creative, a Reddit thread, a YouTube video and articles from CMSWire, LCN and MarTech. Most of them list tools or define the term, so we wrote a task-by-task version with the prompts we use and the data each task needs.

Claude Code wrote the draft from that brief and saved it to Rillow, which checked the title, headings, links and wording before a person read it. Rillow now checks the post's Google position every day.

Frequently asked questions