Hey again. I’m back.
I’m been working with SEO since I can remember, been lucky to have some great cases and wow, I must say this time we’re living in is so crazy, perhaps the first time in my decade+ career that I have seen such af shift – SEO and the way we find information will change rapidly.
This time I want to dig deeper into an area of AI that has completely shifted how modern marketing teams operate — AI tools for SEO.
If you’ve followed me for a while, you know I don’t jump on trends just because they’re hot. I dive in when I see something changing the game in how we build visibility, scale results, and free up brainpower.
And AI in SEO? That’s not hype. That’s infrastructure.
This guide is not a random list of tools slapped together. It’s not affiliate fluff. It’s what I’d give to a sharp founder or performance marketer if they asked, “What’s the real deal with AI and SEO? What should I actually be using — and how?”
This is my way of answering the whole SEO and AI discussion.
We’ll get into the workflows. We’ll cut the BS. And we’ll look at how this tech, used right, can give you compound advantages in traffic, relevance, and time.
This it not like a regular SEO post; This is more for you to get some new tools in your stack and looking at SEO in a new way.
Let’s go.
What we mean by “AI tools for SEO” — and why it’s a gamechanger
Let’s start clean.
When I say “AI tools for SEO,” I don’t mean tools that just throw in a chatbot and slap an AI badge on the homepage.
I’m talking about platforms that use machine learning, natural language processing, or large language models (LLMs) to remove bottlenecks, surface insights you’d miss manually, and streamline decision-making in your SEO workflow.
Not replace you. Upgrade you.
The shift here is massive.
You move from:
- Guessing what keywords make sense → to seeing patterns backed by data
- Writing content from scratch → to starting with AI-powered outlines or topic briefs
- Trying to match search intent → to understanding it from user behavior and SERP signals
What took 5 hours now takes 45 minutes — and you’re making sharper decisions the whole way through.
But let me be real about the flip side too.
AI in SEO is still only as good as the person using it. You can have the best tools in the world, but if your strategy sucks, your results will too. Garbage in = garbage out.
So think of these tools as assistants — not oracles.
The real unlock comes when you know:
- When to use AI
- What not to automate
- And how to blend data with instinct
That’s what we’ll cover next.
The use cases — how AI slots into modern SEO workflows
AI can plug into nearly every part of your SEO process — if you know where to look. But instead of throwing the whole kitchen sink at it, I break it down by outcomes. Where can AI give you time back? Where does it help you see what others miss? And when does it give you unfair leverage?
Here’s how I break down the real-world use cases:
Keyword research
- Cluster analysis from massive SERP data in seconds
- Identifying semantically related queries with user intent built in
- Tools like Keyword Insights or ChatGPT with scraped data can speed up topical discovery fast
Content generation & optimization
- Generating smart first drafts based on your outline and intent
- Tools like Surfer and Frase don’t just score content — they guide your structure
- MarketMuse and Clearscope use AI to spot content gaps your competitors miss
On-page SEO & structure
- AI can review your pages for proper heading use, internal link density, and readability
- It can help draft schema markup without needing to write raw JSON
- Useful for multilingual setups when scaling SEO across markets
Technical SEO
- Analyze crawl logs with GPT to spot patterns
- Pair Screaming Frog exports with ChatGPT to generate action plans
- Generate hreflang tags or resolve canonical issues at scale
Competitor analysis
- Use AI to cluster your competitors’ top pages by topic
- Spot backlink trends or anchor text patterns without manual review
- Feed backlink reports into AI to write outreach campaigns
Search intent mapping
- LLMs are good at reading SERPs — better than most SEOs
- You can run prompt chains to group keywords by intent and format
- That means you know when to write a guide vs a comparison vs a feature page
Internal linking & topical depth
- Use AI to auto-map internal link strategies across a content hub
- Generate glossary term anchors and semantically connected links
- Better crawl paths = better signal to Google
This isn’t theoretical. These are daily workflows in my own stack. And I’ll show you the tools and methods in the next sections.
My key criteria for evaluating AI SEO tools (don’t buy crap)
Not all AI SEO tools are created equal. Some are sleek on the surface but broken under the hood. Some are powerful but clunky. Others promise everything and deliver noise. Here’s how I personally vet whether a tool is worth my time:
1. Speed and responsiveness
- If it lags, it’s gone. A tool should be a time-saver, not another bottleneck.
- Good AI tools feel instant — and don’t glitch every time you push them.
2. Data transparency
- If I don’t know where the data is coming from, I don’t trust it.
- Tools should show sources or at least explain methodology (looking at you, vague scoring systems).
3. Integration power
- It should work with my existing stack: WordPress, Google Search Console, Ahrefs, GA4, Airtable.
- Bonus points for API access or Make/Zapier integrations.
4. LLM-ready outputs
- Does the tool give me clean data I can prompt with or feed into ChatGPT workflows?
- CSV downloads, JSON outputs, or well-structured text are all signs of a solid backend.
5. Real value, not bloat
- Does it actually make me faster, better, or smarter?
- If it takes 20 clicks to get one decent insight, it’s dead weight.
AI tools for keyword research
Let’s cut the noise: most keyword tools aren’t built for modern SEO. They flood you with data you don’t need, prioritize search volume over actual intent, and waste your time giving the same 200 keyword suggestions everyone else is looking at.
Here’s how I approach keyword research with AI. It’s not just about finding terms — it’s about mapping out opportunities, user psychology, and business potential.
My current stack includes:
- Keyword Insights – auto-clustering, intent detection, and SERP-based grouping. Fast and reliable.
- ChatGPT + search exports – paste in keyword lists and prompt for categorization, semantic clusters, and missing angles.
- AlsoAsked – great for pulling real-world user questions. Pair it with GPT for building outlines.
- WriterZen – good for topic mapping when you need structured keyword trees fast.
And here’s the wildcard in my stack:
- Morningscore – the tool I personally use for everything SEO-related. From keyword tracking to competitor insights to health audits — it’s my daily dashboard. They’re pushing hard on AI, even showing you when your site is visible in LLMs like ChatGPT. It’s a must-use in my book, and still priced way more affordably than Ahrefs, Moz, or Semrush.
What I’m doing differently:
- I don’t chase high-volume keywords. I look for topical coverage and user journeys.
- I care about intent format: does this need a listicle, comparison, tutorial, or landing page?
- I validate SERPs through AI — run SERP extractions and ask GPT what format dominates.
AI tools for content optimization
This is where things get fun — and fast.
AI content optimization isn’t about replacing writers. It’s about making sure what you write actually ranks. And not in theory. I mean ranking in competitive niches where every position matters.
Let’s be honest. Most content audits are too slow or too generic. You run a few keyword checks, scan for H1s, maybe look at word count. That’s not enough anymore.
Here’s what I look for:
- Does the content match the dominant format in the SERP?
- Is the structure aligned with user behavior and scroll depth?
- Are we giving the answer fast — and then building depth?
SurferSEO is my go-to here. It’s not just about content scores. It’s about making data useful for actual output. I use it to shape outlines, optimize existing posts, and validate content briefs before writing a word.
It shows you what top-performing pages are doing differently — and where you can win by being sharper, clearer, or just faster to the point.
Morningscore also plays a role. It’s not just a tracking tool. Their AI integrations are evolving fast, and they now give insight into how your pages show up in generative engines too — meaning you’re optimizing not just for Google, but also for LLMs.
Bonus tip: Once I’ve optimized with Surfer, I feed the content back into GPT with custom prompts for tone and structure polishing. That’s where the human-AI blend really wins.
Next up: the tools for technical SEO — and where AI actually makes a difference under the hood.
AI tools for technical SEO
Technical SEO isn’t sexy. But it’s the stuff that quietly makes or breaks your rankings.
For years, doing it right meant hours in spreadsheets and server logs. AI flips that script. It reads, sorts, and surfaces patterns you’d never spot manually — and does it in seconds.
Here’s what I use it for:
- Analyzing crawl logs to detect orphaned pages or crawl traps
- Auditing site architecture for broken links, redirect chains, or bloated structures
- Spotting crawl depth and indexation issues without needing a dev
The combo I like best:
- Run a Screaming Frog crawl → export everything
- Feed the data into ChatGPT or Claude with a detailed audit prompt
- Get back summaries of duplicate content, missing meta data, broken canonicals, and more
It turns what used to be a 6-hour audit into something I can knock out in 25 minutes.
Morningscore again steps in here. Their health section gives me a quick overview — but the best part is that their AI suggestions are readable. No fluff, no tech-speak. Just things you can act on.
And no, I’m not sponsored. I just like keeping my toolstack tidy, and Morningscore covers 80% of what most sites need in one place. For more complex setups or large-scale architecture, Screaming Frog plus ChatGPT still wins. That combo is still gold when you need to go deep, fast.
Schema? I generate it in GPT. LocalBusiness, Article, FAQPage — done in 30 seconds. If you know what you need, the prompt handles the markup. Copy, paste, validate.
Hreflang and international SEO? Same deal. Feed your URL map into AI and it will spit out the correct tags for each market. No Excel gymnastics.
This stuff doesn’t need to be complicated. AI makes it faster, cleaner, and honestly — way more fun to do.
Should you automate content?
This is where people either get excited — or nervous.
The idea of publishing blog posts at scale with the click of a button sounds amazing… until you read the results.
Here’s my stance: automation isn’t the enemy. Bad automation is.
You can use AI to generate outlines, build first drafts, even structure a content hub. But if the output reads like a chatbot or looks like it was written in a hurry, you’re just polluting the web.
Here’s how I do it:
- Brief it right – I never let AI write blind. I give it structure, internal links, tone, and audience context.
- Build rough → refine sharp – First pass is fast. Second pass is critical. I’ll often rewrite intros and CTAs completely.
- Blend tools – Surfer for SERP data, GPT for creativity, my own judgment to make it land right.
I don’t automate for volume. I automate for momentum.
So no — I’m not publishing 100 articles a week. I’m building scalable workflows that keep quality high while removing bottlenecks.
That’s the only automation worth doing.
AI for SEO & link building
This is the part where most guides drop off — but it’s also where a lot of long-term SEO value lives.
AI isn’t just for writing content or optimizing metadata. It’s a serious asset in building better, smarter link strategies.
Here’s how I’m using it:
- Prospecting at scale – I feed niche keywords, competitor domains, or brand terms into GPT to generate long lists of potential backlink sources and outreach targets.
- Email personalization – Using scraped bios, social info, and website blurbs, I prompt AI to write highly customized intros that actually get replies.
- Anchor text planning – I generate semantic variations with GPT based on keyword clusters and page purpose — helps avoid over-optimization and keeps things natural.
- Content ideas for link bait – AI helps brainstorm unique angles that would work as expert surveys, controversial takes, or data visualizations.
But none of this works if the links themselves are garbage.
That’s why I only use Bazoom for link building.
It’s not just fast — it’s reliable. Bazoom doesn’t push low-quality, made-up placements. It gives you direct access to a curated network of real, relevant websites across dozens of industries. Clean, scalable, and no back-and-forth mess.
For me, it replaced every other link building method I used for over a decade.
If I’m using AI to find strategic opportunities, I’m using Bazoom to turn those into actual links — because what’s the point of great strategy if execution breaks it?
Link building is still hard. But it doesn’t have to be slow or sketchy.
Use AI to think and plan fast. Use Bazoom to make it real.
LLMs as secret weapons for SEO
This is where the real fun begins. Most SEOs still see ChatGPT as a copy tool. That’s like using a Ferrari to go grocery shopping.
LLMs like ChatGPT, Claude, and Gemini aren’t just for content generation — they’re strategic leverage when used right. They’re how you scale thinking, not just output.
Here’s how I actually use them:
- Content strategy building – Feed in SERP exports or full content inventories, and prompt the LLM to suggest structure, categories, and angles.
- Intent mapping – Give the model a keyword list and ask it to group by funnel stage, content format, or user mindset.
- Topic clustering – Paste in scraped H1s, URLs, or People Also Ask data. Let the LLM do the clustering in seconds.
- Outline creation – Ask for a 10x structure based on top 3 results, inject your tone, and keep the output tight.
- Localization checks – Feed GPT your translated headlines and ask it to critique tone, wordplay, or local nuance.
The best part? These workflows are repeatable. Build your prompt templates once, then copy-paste whenever you need them.
Bonus move: Stack these models with tools like Make, Airtable, or Notion. Turn LLMs into backend engines that run 24/7.
The difference is this: while most people use AI like a gimmick, the best SEOs are building systems with it.
The future of SEO with AI
We’re not heading toward a future where AI is part of SEO — we’re already in it.
The next frontier isn’t about adding AI to your stack. It’s about rethinking what SEO is when the user journey is shaped by generative answers, predictive intent, and algorithmic interpretation of meaning.
Here’s what’s shifting:
- SGE and AI-overviews – Google is already testing AI-packed SERPs. If your content isn’t structured for summarization, you’re invisible.
- Search intent is fracturing – One query can lead to multiple paths depending on who’s searching. AI helps personalize content for micro-intents at scale.
- Ranking factors evolve – It’s not just backlinks and speed anymore. It’s engagement patterns, scroll behavior, and user satisfaction metrics.
- SEO meets product – Your UX, copy, and site structure are now part of your ranking potential — not just your blog posts.
The SEOs who win? They aren’t the ones who fight the changes — they’re the ones who build new systems around them.
My advice:
- Learn how LLMs interpret information — and write for that.
- Test your content in AI models. Ask GPT how it would summarize your page.
- Prioritize clarity, structure, and unique insights over keyword density.
- Own your vertical with topical depth, not just single-page rankings.
In this next wave, SEO becomes less about hacks — and more about building digital assets that communicate clearly to both humans and machines.
That’s a future I’m betting on.
Bonus – Jump here for my current stack and how I use it
Let’s end this with something practical. Here’s how I actually run my day-to-day SEO work. This isn’t theory — it’s what’s open in my tabs every week.
Core tools I rely on:
- Morningscore – The backbone of my SEO stack. I use it for keyword tracking, site health, AI visibility in LLMs, and overall momentum checks. It’s where I go first to check if things are moving.
- SurferSEO – My go-to for content structure and optimization. I use it when building new blog outlines, cleaning up underperforming content, and aligning with the top of the SERP.
- ChatGPT & Claude – For building outlines, clustering keywords, drafting, rewriting, and prompt testing.
- Screaming Frog – When I need to go deep. Still unbeatable for technical audits and pairing with GPT for pattern analysis.
How I glue it together:
- I track all site performance in Morningscore, then jump to Surfer when I want to optimize specific pages.
- I use ChatGPT to generate copy and outlines, then pass it back through Surfer for structure alignment.
- For dashboarding and reporting, I hook it all into DashThis — clean interface, easy for clients and execs to scan.
Nothing fancy. Just sharp tools in the right places.
And that’s how I stay lean, fast, and focused while still delivering results that scale.
Final thoughts
AI won’t do the work for you — but it will make the work easier to do at scale, with clarity and consistency. That’s the edge.
You don’t need to master every tool. You just need to understand what problem you’re solving — and pick the fastest, sharpest way to get there.
Whether you’re bootstrapping your own growth or running campaigns for 100+ clients, the goal is the same: fewer bottlenecks, more leverage.
If this helped clarify your next steps, feel free to share it, build on it, or DM me. I’m always testing new stacks and happy to swap notes.
See you out there. If you want more tips and tricks, feel free to join me on LinkedIn.
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