AI SEO: how to use AI for SEO work and how to optimise for AI search

What does AI SEO mean?

AI SEO is a label for two jobs that people often mix up. The first is using AI for SEO: asking a language model or an agent to help with keyword research, content briefs, internal linking, technical audits and reporting. The second is optimising for AI search: making your content easy for ChatGPT search, Google’s AI Overviews and AI Mode, Perplexity and Copilot to retrieve and cite. People use “AI SEO” for both. “AI for SEO” and “AI in SEO” lean towards the first, and “SEO for AI” towards the second.

How can you use AI for SEO work?

Use AI where a wrong answer is cheap to catch and a right one saves hours. The pattern that works in every case below is the same: give the model real data, let it propose, and have a person check before anything ships.

Research and clustering

A model is good at grouping hundreds of queries by intent, spotting gaps in a topic and summarising what the top pages cover. It is not a source of numbers. Never ask it for search volumes, difficulty scores or rankings, because it will produce plausible figures that are not measurements. Take those from a data provider or your own Search Console, and let the model work on the data you give it.

Briefs and outlines

Google says generative AI can be useful when you research a topic and to add structure to original content. A brief that lists the question, the audience, the sources to use and the facts to include is a good job for a model. The expertise, the original data and the examples must come from you, because a page that only restates what exists adds nothing for a reader.

Internal linking

Give the model a list of your URLs with titles and ask which pages should link to which, with suggested anchor text. Then verify that each suggested URL exists and returns status 200 before you add a link, because models invent paths. A crawl is the easiest way to get a trustworthy list of pages. With the Serpel CLI you can export it as JSON.

serpel crawl pages --crawl <crawl-id> --limit 200 --json > pages.json

Technical audits and fixes

An audit produces a long list of findings. An assistant can explain each one, group them by cause and propose a code change, which is where it saves the most time for developers. Our SEO audit report example shows what such findings look like, including a link finding where 31 of 43 flagged targets turned out to be fine after a human re-check.

Metadata and structured data drafts

Titles, descriptions, alt text and JSON-LD are all fair game for a first draft. Google’s guidance on generative AI content says the review duty applies to metadata as well, such as title elements, meta descriptions, structured data and image alt text. Validate markup with a tool such as our free schema validator before you publish it.

Automation with APIs, agents and MCP

The step beyond chat is to let an agent read your SEO data itself. The Model Context Protocol (MCP) is an open-source standard for connecting AI applications to external systems. Anthropic created it and contributed it to the Agentic AI Foundation, a fund of the Linux Foundation, in December 2025. Google’s Search Console API exposes the data of the Performance report in batches of up to 25,000 rows, and Bing has a similar Webmaster API, covered in our Bing Webmaster Tools guide.

Serpel’s remote MCP server offers 13 tools that let Claude Code, Cursor, Codex and other agents read rankings, search data, crawl issues and recommendations and, after a serpel scan, relate findings to the routes in your code. Only the keyword research tool spends credits, and only within a budget you approve. Our guide to the Google Search Console MCP shows the same idea for Google’s data. Serpel does not write articles for you. It supplies the data and findings your own assistant works from.

Where does AI go wrong in SEO?

Google puts the core problem plainly: generative models don’t retrieve facts, but predict a likely sequence of words, so outputs can contain inaccuracies, known as hallucinations, and it is critical to fact-check AI-generated content. The 2025 paper Why Language Models Hallucinate by Adam Tauman Kalai and co-authors argues that models guess when uncertain because training and evaluation procedures reward guessing over admitting uncertainty. In SEO that shows up in predictable ways.

What does Google say about AI-generated content?

Google does not ban AI content. Its guidance on using generative AI says to review AI-generated content manually before publishing, and warns that generating many pages without adding value for users may violate its spam policy on scaled content abuse. The spam policies define that abuse as many pages generated for the primary purpose of manipulating search rankings and not helping users, and they apply no matter how the content is created. The examples Google lists include:

• using generative AI tools or similar tools to generate many pages without adding value for users
• scraping feeds or search results to generate many pages, including through automated transformations such as synonymizing or translating, with little value for users
• stitching or combining content from different pages without adding value
• creating many pages whose content makes little sense to a reader but contains search keywords

The tool is not the test. The purpose and the value are. Google’s helpful content guidance also asks whether the use of automation or AI is clear to visitors through disclosures, and says such disclosures are useful where someone might wonder how the content was created. If you publish AI-assisted pages, a short note on how they were made costs nothing.

How do you optimise for AI search?

This is the second meaning of AI SEO, and Google’s position is that it is still SEO. Its optimisation guide says you don’t need special files, markup or rewriting for its AI features, and that unique, non-commodity content will likely matter more than any other suggestion in it. The practical list is short:

1. Let the crawlers in, and keep pages indexable. See AI crawlers.
2. Serve the main content as server-rendered text. See JavaScript SEO.
3. Lead each section with the answer and support it with attributed facts. See answer engine optimization.
4. Cover the follow-up questions a reader would ask, on one useful page.
5. Measure citations over time instead of trusting a single answer. See how to rank in ChatGPT.

Which AI SEO tools exist?

AI SEO tools fall into categories, and a good choice depends on the job, not on a league table. We do not rank products here. Google also advises caution with third-party tools that promise ranking success or claim to use internal Google metrics, because no third-party tool has access to its internal ranking or AI systems.

Before you adopt any of them, ask where the data comes from, whether the tool shows its sources, whether you can export the results and who reviews the output. Serpel sits in three of these categories: a technical audit that renders JavaScript, an AI visibility tracker for ChatGPT with web search and Google AI Overviews, and an API, CLI and MCP server. Compare it with others in our guide to the best AI visibility tools.

A practical AI SEO workflow

1. 1

   Ground the model in your data

   Connect Search Console and Bing, run a crawl and give your assistant those results, through exports or an MCP server, instead of asking it to guess.

2. 2

   Let AI propose, not publish

   Ask for clusters, briefs, link suggestions and fix proposals. Treat the output as a draft that a person owns.

3. 3

   Verify facts and add original input

   Check every number, quote and URL against a primary source, and add the data, examples and expertise the model cannot supply.

4. 4

   Ship in small batches

   Publish a few pages or changes at a time, so you can see what worked and roll back what did not.

5. 5

   Measure and re-crawl

   Read the Search Console performance data, re-run your rank and AI visibility checks and crawl again to confirm that the technical fixes landed.

   serpel crawl compare --project <project-id>