How I Turned Initial SEO Data Research into an AI-Assisted Workflow

When building a new website or taking over an existing one, I usually start by opening a stack of tools: reviewing GSC, researching keywords, examining competitors and crawling the site, then combining seven or eight spreadsheets.

What consumes time is often the work before analysis: exporting, cleaning, standardizing URLs, merging keywords and comparing periods.

I broke those tasks down and reorganized them into a repeatable workflow.

But it is not what many people imagine as “letting AI do SEO automatically.” A more accurate description is:

Automate data collection where possible, let AI organize and perform initial analysis, and leave final judgments to people.

Here is how the workflow currently looks, including the parts I have not connected yet and why.

1. AI does not automatically possess SEO data

When first using GPT or Codex for SEO analysis, many people send a URL and ask, “What SEO problems does this website have?”

It can read public pages, inspect titles, H1s, canonicals and structured data, and identify some internal-link problems.

But a few qualifications matter. Whether it can execute JavaScript depends on the tools and browsing method. Many sites block access, and the version it sees may differ from what Googlebot sees.

More importantly, it does not know:

• Which keywords have generated impressions in the past

• Which pages are gaining or losing rankings

• Which pages drive most of a competitor's traffic

• Whether visitors submit forms after arriving

• Whether Googlebot successfully crawls important pages

That data still comes from GSC, Semrush, GA4, crawlers and server logs.

The first step in bringing AI into SEO is therefore not asking it to analyze from nothing. It is solving a more basic problem:

How to provide data from different sources reliably.

2. I currently divide the data into four groups

1. GSC: the website's actual search performance

GSC reports what has happened: queries, pages, impressions, clicks, CTR and average position.

This is the most important layer in initial analysis because it reflects the site's actual performance in Google rather than a third-party estimate.

But “actual” still has limits. Average position is weighted, very low-volume queries are anonymized, and the data has a delay of two or three days. It is useful for assessing trends, but there is little point expecting its numbers to match a third-party tool.

Google's official Search Console API is free, but you must handle authorization, queries and returned results yourself. There are also row limits: up to 25,000 per request and 50,000 per day for each site and search type.

I am testing third-party tools such as GSC Wizard to connect GSC to ChatGPT or Codex. Once connected, AI can read a selected period and compare changes in pages and keywords.

The saving is time spent repeatedly exporting, filtering and calculating differences.

Before using a third-party connection, however, confirm whether the client is willing to authorize it. Convenience is not the only consideration for client sites; data permissions matter too.

One reminder: GSC retains only 16 months of data.

Older data cannot be retrieved through its interface or API. Previously exported or backed-up data remains usable. This often causes problems when taking over an older site: a client wants performance from two years before a redesign, but if no one retained it, the history is difficult to reconstruct.

For longer history, Google's official route is GSC bulk export to BigQuery. Configure it once in GSC and data flows into BigQuery daily. Costs depend on storage and queries and are generally modest.

If you expect to work on a site for another three to five years, enable this early. It cannot recover data from before activation either.

2. Semrush: market and competitor data

GSC covers only your own site. It cannot tell you what other search demand exists in the industry or how competitors attract traffic.

That requires third-party databases such as Semrush or Ahrefs.

Semrush now has an official MCP that can connect directly to tools such as ChatGPT and Claude. It uses OAuth or API-key authorization, and each query consumes the account's API units.

Whether you can connect therefore depends on the account's permissions and MCP/API allowance. Being able to sign in and export CSV files does not necessarily mean MCP access is included. With multiple users, authorization ownership and quota allocation also need checking.

My current approach is simple:

When market data is needed, export CSV files from the dashboard and let GPT or Codex process them together.

It is not fully automatic, but costs stay controllable and the scope of data entering AI is clearer each time.

Automation does not require every tool to remain connected in real time. A process that can be repeated reliably at low cost is useful in its own right.

3. Screaming Frog or Codex: website technology and structure

Technical SEO starts with understanding the site's current state:

• Can pages be accessed normally?

• Are there incorrect redirects?

• Are titles, H1s or descriptions missing or duplicated?

• Are canonical, hreflang and schema settings correct?

• Is the site hierarchy too deep?

• Are there orphan pages or broken internal links?

Screaming Frog added MCP in version 24, released in May 2026. Once connected, AI can start crawls, read results, export data and compare changes between crawls.

For small sites with up to 500 URLs, the free version can be sufficient: crawl the site, export CSV files and give them to AI. Its limits matter, though: it cannot save crawls or change configuration and does not include JavaScript rendering. It works for a one-off check, but not ongoing tracking.

Codex can also write crawl scripts to check status codes, titles, links and page tags. For a small, simply structured site, it can handle some basic checks.

But Codex is not equivalent to a mature SEO crawler. Professional crawlers are more reliable for JavaScript rendering, complex redirects, hreflang validation, internal-link depth and bulk historical comparisons.

For what Googlebot actually crawls, GSC's Crawl Stats report already provides an outline: total requests, response-code distribution, breakdowns by file type and crawl purpose, and example URLs. URL Inspection can also show when an individual page was last crawled.

These are samples and summaries, however. To confirm request by request whether a product directory has gone uncrawled for a long time, or how long old URLs continue being requested after a redesign, you still need server logs.

Crawl Stats shows the outline; logs show individual requests.

I would therefore not say “AI can already replace Screaming Frog.” A more accurate assessment is:

AI makes basic checks more accessible, while professional crawlers still provide more reliable, systematic data collection.

4. GA4: conversion validation added when useful

GA4 and GSC answer different questions. GSC tells me what users searched for to reach the site; GA4 tells me what they did afterward.

If a site has been running for some time and events such as form submissions, email clicks, WhatsApp contacts and downloads are configured correctly, GA4 helps assess whether SEO traffic turns into business.

Google has also released an official Google Analytics MCP Server. One detail matters: it runs locally and works with clients such as Claude Desktop, Gemini CLI and Codex CLI that can start a local MCP server. The web version of ChatGPT cannot use it directly at present; it needs to be deployed as a remote service. This is easy to miss in the documentation and can leave setup stuck.

However, I have not made GA4 mandatory for every project.

If traffic is very low or key conversion events are not configured, connecting it produces visits and engagement figures with limited value for business decisions.

In my workflow, GA4 is added as needed, rather than simply to accumulate more data.

3. What AI does once the data arrives

With GSC, Semrush, crawl data and client materials in one environment, AI is particularly useful for three tasks.

First, cleaning. URLs, keywords and dates vary between tools. AI can standardize fields, remove duplicates and identify branded terms and clearly irrelevant keywords.

Second, cross-comparison. One task I often do is joining GSC's page-level data with a crawler's title export by URL. It immediately reveals pages with impressions but missing or duplicate titles. Work that once required checking two spreadsheets back and forth now takes only a few minutes.

Third, organization. AI turns scattered data into structured summaries, flags pages, keywords and technical issues worth examining, and arranges them into follow-up tasks.

But I do not let AI change the website directly. A person still reviews its findings and recommendations before deciding whether to change pages, content, structure or internal links.

4. Judgment is the part that cannot simply be automated

A keyword having search volume in a tool does not mean it fits the client's business.

A traffic decline may reflect rankings, changing search demand, a redesign, a measurement change or simply fluctuations in branded searches.

AI can identify correlations, but it does not know the client's product margins, delivery capabilities or target markets, or how good the inquiries actually are.

This is especially apparent on export-oriented B2B sites. Many valuable terms have low search volumes. Looking only at tool data can turn SEO into chasing traffic wherever it appears, eventually bringing visits unrelated to the business.

In this workflow, AI narrows the scope of investigation, while a person answers:

Is this opportunity actually worth pursuing?

That still requires SEO experience and a real understanding of the client's business.

5. My current combination keeps the budget low

• Connect GSC to AI through a connector, while enabling BigQuery export for long-term history

• Export Semrush CSV files for each project

• Use the free version of Screaming Frog or Codex for basic checks on small sites

• Add GA4 only when conversion events are meaningful

• Use GPT or Codex to clean, merge, compare and organize

• Keep judgment, review and priority setting with people

• Record results in an SEO Review and Change Log for later assessment

My clearest conclusion from using it is that AI has not made SEO data free or made professional tools obsolete.

GSC, Semrush, Screaming Frog and GA4 still cover distinct data layers. AI changes how those layers are used. Previously, they were scattered across tools and spreadsheets that had to be opened, filtered and organized individually. Now AI can bring them together so people see problems and opportunities sooner.

I therefore do not describe this method as “AI doing SEO automatically.” I prefer:

An AI-assisted SEO data analysis workflow.

It removes repetitive work and retains judgment.

That is probably one of the most practical ways to bring AI into SEO at this stage.

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Related reading

• Should SEO for an Export Business Website Start Before the Build or After Launch?

• From SEO Roadmaps to Real-Time Decision Systems: A Workflow for the AI Search Era

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