AI has become part of my daily SEO workflow.
But probably not in the way many people imagine.
I don't give an AI tool a website and ask:
“What's wrong with my SEO?”
Then copy whatever it says into a task list.
Instead, I find AI most useful somewhere between data collection and human decision-making.
It can help me process information faster, identify patterns, generate hypotheses, and investigate technical issues.
But deciding whether those hypotheses are actually correct still requires context.
And that distinction matters.
AI Is Good at Finding Things Worth Investigating
Technical SEO produces a lot of data.
Crawlers can return thousands or even millions of URLs.
Search Console contains queries, pages, indexing states, sitemap information, and crawl signals.
Server logs can contain enormous amounts of request data.
Then there are:
redirects,
canonical tags,
status codes,
structured data,
duplicate metadata,
internal links,
pagination,
parameterized URLs,
and rendering behavior.
The problem isn't always finding data.
The problem is deciding where to look first.
This is one area where AI can be genuinely useful.
Instead of manually inspecting thousands of rows, I can use AI-assisted workflows to help identify patterns that deserve investigation.
The important word is investigation.
A pattern isn't automatically a problem.
Finding a Pattern Isn't the Same as Understanding It
Imagine a crawl shows 5,000 URLs containing query parameters.
An AI system might immediately classify this as:
Duplicate content problem.
Maybe.
But those parameters could represent very different things.
Some might be tracking parameters.
Some might control sorting.
Some might represent useful filters.
Some might create indexable landing pages intentionally.
Some might never be internally linked.
Some might already canonicalize correctly.
And some might genuinely be creating crawl waste.
The URL pattern alone doesn't tell you which situation you're dealing with.
You need to understand how the application works.
This is where blindly accepting AI recommendations becomes dangerous.
I Prefer Asking AI Questions, Not Asking for Verdicts
There's a subtle but important difference between these prompts:
“Tell me how to fix these URLs.”
and:
“What hypotheses could explain why these URLs are being discovered?”
The second question is much more useful.
It turns AI into an investigation partner rather than an authority.
Possible hypotheses might include:
faceted navigation,
pagination,
internal search,
JavaScript-generated links,
tracking parameters,
old URLs still linked internally,
sitemap contamination,
or external discovery.
Now I have something useful:
a list of things to test.
The next step is evidence.
Search Console and Crawlers Answer Different Questions
AI becomes more useful when you give it context from multiple sources.
A crawler tells you what the website exposes.
Search Console tells you something about how Google interacts with those URLs.
Analytics tells you how users behave.
Server logs can tell you what crawlers actually request.
These datasets describe different parts of the same system.
Suppose a crawler discovers thousands of parameter URLs.
Before deciding they're hurting SEO, I want to know:
Are they indexed?
Is Google crawling them?
How were they discovered?
Are they internally linked?
Are they in the sitemap?
What canonical do they declare?
Do they receive impressions?
AI can help organize those questions.
It can't replace the evidence needed to answer them.
AI Is Surprisingly Useful for Regex
This is one of the less glamorous applications, but I use it often.
Technical SEO regularly involves grouping URLs.
For example, I might need patterns for:
product URLs,
category URLs,
pagination,
query parameters,
image paths,
API routes,
language directories,
or legacy URLs.
Writing regular expressions manually isn't difficult once you're comfortable with them, but AI makes the process faster.
I can provide examples of URLs that should match and URLs that shouldn't.
Then generate a candidate regex.
But I still test it.
Always.
A regex that works on five examples can behave very differently across 500,000 URLs.
AI Can Help Turn Crawl Data Into Questions
Suppose I export crawl data containing:
URL
Status Code
Canonical
Indexability
Title
H1
Depth
Inlinks
Content Type
Instead of reading thousands of rows individually, I can analyze groups.
Which templates contain most 3xx responses?
Which sections have unusually high crawl depth?
Which indexable URLs canonicalize elsewhere?
Which pages have zero or very few internal links?
Which URL patterns produce duplicate titles?
Which sitemap URLs aren't indexable?
These are excellent tasks for automation and AI-assisted analysis.
But notice what AI is doing.
It's helping prioritize.
It's not deciding the SEO strategy.
One of the Best Uses of AI Is Explaining Anomalies
I particularly like using AI after I've already found something strange.
For example:
A section suddenly contains far more URLs than expected.
A sitemap contains non-indexable pages.
Google selects a different canonical.
A template produces structured data errors.
A pagination pattern creates unexpected crawl paths.
Instead of immediately asking for a solution, I can describe the architecture and ask:
“What mechanisms could produce this behavior?”
That often gives me several directions to investigate that I might not have considered immediately.
Some will be wrong.
That's fine.
Generating hypotheses cheaply is useful.
Deploying fixes cheaply without verification isn't.
AI Doesn't Know Your Business Priorities
This is one of its biggest limitations in SEO.
Imagine two problems:
Problem A affects 100,000 URLs but none generate meaningful traffic or revenue.
Problem B affects 50 high-value commercial pages responsible for a significant portion of organic sales.
Which one should you fix first?
A tool looking primarily at scale might choose Problem A.
A business-aware SEO specialist may choose Problem B immediately.
Technical severity and business priority aren't always the same thing.
Good SEO prioritization often requires knowing:
revenue,
conversion value,
development effort,
business goals,
seasonality,
search demand,
and organizational constraints.
AI rarely has all of that context unless you explicitly provide it.
AI Can Produce Very Confident SEO Nonsense
This is the part that worries me most.
AI-generated SEO recommendations often sound professional.
They may mention:
canonicalization,
crawl budget,
indexation,
structured data,
Core Web Vitals,
internal linking,
and E-E-A-T.
Everything sounds plausible.
But plausible isn't the same as correct.
I've seen recommendations where the proposed “fix” would create a bigger problem than the original issue.
That's why I don't evaluate an AI recommendation based on how technical it sounds.
I evaluate it based on:
What evidence supports this?
My Preferred Workflow
The workflow I've found most useful looks something like this:
Collect → Segment → Investigate → Verify → Prioritize → Implement → Measure
AI can help significantly with the middle of that process.
Collect
Get data from crawlers, Search Console, analytics, logs, or the application.
Segment
Group URLs and issues into meaningful patterns.
Investigate
Generate possible explanations for unusual behavior.
Verify
Check the actual HTML, headers, rendering, application logic, and search-engine behavior.
Prioritize
Combine SEO impact with business value and development effort.
Implement
Turn verified findings into clear development or content tasks.
Measure
Check whether the change produced the expected result.
The dangerous workflow is much shorter:
Ask AI → Copy recommendation → Deploy
That's not automation.
That's outsourcing judgment.
AI Makes SEO Knowledge More Important, Not Less
There's an interesting contradiction happening.
AI makes many SEO tasks easier.
But because it makes recommendations so easy to generate, understanding SEO fundamentals becomes even more important.
If you understand canonicalization, you can evaluate an AI recommendation about canonicals.
If you understand HTTP status codes, you can challenge a bad redirect recommendation.
If you understand crawling and indexing, you can distinguish between a crawl issue and an indexation issue.
Without those fundamentals, every confident AI response looks equally convincing.
That's a problem.
Technical SEO Is Moving Toward Better Tooling
I don't think the future is SEO specialists manually inspecting spreadsheets forever.
More of the repetitive work will be automated.
Large URL datasets will be classified automatically.
Patterns will be surfaced faster.
Anomalies will be detected earlier.
Reports will become easier to generate.
And AI will increasingly sit between raw data and human analysis.
That's a good thing.
It means we can spend less time moving information between spreadsheets and more time understanding why something is happening.
But the final step still matters:
Does the recommendation make sense for this specific website?
The Bigger Lesson
AI is extremely useful for technical SEO when you treat it as an accelerator.
It can help you:
process data,
generate regex,
classify URLs,
identify anomalies,
generate hypotheses,
explain technical concepts,
and turn findings into clearer tasks.
What I don't want it doing blindly is making architectural decisions.
Because SEO problems don't exist in isolation.
They exist inside products, businesses, codebases, content systems, and user journeys.
AI can help you see the map faster.
You still need to decide where you're going.
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