How to Use AI for SEO: The Ultimate Guide
- Connor Glaze
- Oct 26
- 11 min read
TL;DR:
Human-written content still generally outperforms AI-generated content in ranking and engagement.
Gen AI can still be useful for scaling content fast, but total dependence on it comes with serious risks.
A careful combination of human-written content and AI can help you scale your website quickly and still maintain a high standard of quality.
Knowing how to use AI for SEO isn’t a requirement to be competitive just yet. However, early adopters are starting to see promising results by applying it to their campaigns, and learning this young technology through trial and error.
This guide will explore the use of AI in the current SEO market, the risks of running into AI SEO blindly, and how to use AI for SEO and get the best results possible.
How to Use AI for SEO: What the Experts Say
When ChatGPT launched in late 2022 and I fed it a few writing prompts, my initial reaction was panic.
As an SEO specialist, a large part of my work comes from writing website content. The results I was getting from ChatGPT weren’t great, but with the pace at which everyone said AI was evolving, I was certain I was going to be out of a job soon.
It made me think of the opening scene in Terminator 2 - a horde of skeletal robots trudging over the burnt-out wreckage of my lifelong dream (read: being able to pay the bills in my pyjamas for the rest of my life).
Well, we’re about three years on from that, and I still haven’t had to go out and work for a living.
Can you use AI content at scale and still expect to rank? Yes. But there are some important nuances.
Here are a few key findings from SEO powerhouses that show how AI has been performing in terms of ranking and engagement:
86.5% of top-ranking pages on Google use some form of AI content. [Ahrefs]
On average, human-written content has a 6.2% higher share of the top 3 positions in Google than AI content, and a 4.6% higher share of the top 5. [Semrush]
AI content tends to experience month-to-month fluctuations in traffic, while human-written content is more likely to see a steady, predictable increase. [Neil Patel].
Human-generated content can generate 5.44x more traffic than AI content. [Neil Patel]
Content created by humans is 3.2x more likely to spark an emotional response than AI-generated content. [MotionPoint]
87% of marketers use AI for content marketing, and AI allows marketers to publish 42% more content each month. [Ahrefs]
As a kicker to these statistics, I’d like to share a quote by Paul Roetzer, founder of the Marketing AI institute.
Paul is no cookie-cutter AI “guru” who was banging on about NFTs a few years ago, and did a backstage quickchange to sling a different brand of snakeoil.
His company, the Marketing AI institute, has been at the forefront of AI in marketing since 2016, when the tech’s only imprint on the public domain was mostly confined to sci-fi movies.
In a 2023 LinkedIn post, he had this to say:
“As AI-generated content floods the web, I believe we will see authentic human content take on far greater meaning and value for individuals and brands.
[...]
It’s going to be too hard to know moving forward if any form of written content (i.e. blog posts, emails, social shares, ad copy, scripts, essays, articles, etc.) we see was generated or heavily assisted by AI.
[...]
And while this AI-generated content can still carry value, people will crave content that they know comes directly from the minds, imaginations and hearts of human creators.”
Based on all the evidence, it seems that AI-generated content is gaining more and more real estate in the top spots on Google. However, when it comes to actual engagement, users tend to prefer a human touch.
Assuming this trend continues into the near future, the ideal way to use AI for SEO will be to harness its increased capacity for content output, while still monitoring your results closely to ensure quality and authority signals are up to the standards of your industry.
Confused about the role of AI in SEO? My bespoke SEO services are delivered based on the latest industry news and best practices, helping you build a future-proof organic strategy.
How to Use AI for On-Page SEO
Human-generated content is the way to go if you want to maximise your chances of both rankings and engagement.
However, that doesn’t mean that using AI for SEO is an absolute no-go. You just need to make sure you’re building high-quality prompts, and only publishing content that’s both up to a competitive standard and aligned with Google’s EEAT principles.
Here are some of the crucial best practices to bear in mind when you’re exploring how to use AI for SEO at your brand.
Focus on Quality
While delegating some of your content production to machines, it’s still crucial to remember it's humans who will be visiting your site and reading the final product.
When using AI for on-page SEO, the key things to prioritise are:
Prompts constrained for accuracy (requesting your tool for citations from sources that meet specific quality criteria).
Writing prompts that are aligned with a specific search intent according to the relevant funnel stage (informational, navigational, transactional).
Stating specific SEO requirements for keyword density, headings, and internal links.
Having an established workflow for human oversight, reviewing prompts before they’re used for a draft, and manually fact-checking any claims made in the actual content.
By integrating these quality checks in your AI content generation workflow, you can ensure a stronger starting point for human revisions after the first draft, and reduce the risk of hallucinations or poor-quality content slipping through the net.
Work From a Branded Prompt Recipe
AI tools perform better at generating content when they’re given specific context for the task, including an imaginary content writer role it can play when developing content.
For consistently high-quality results, it’s good practice to build a “prompt recipe” that covers the following key elements:
Context.
Role.
Task.
Constraints.
SEO inputs.
Here’s a simple prompt recipe that could be used in top-of-funnel content for a high-end chef’s knife brand:
“You are creating SEO content for a premium chef’s knife brand that targets professional chefs, culinary students, and passionate home cooks who value craftsmanship and performance in their kitchen tools.
Act as a luxury brand copywriter and SEO strategist with several years of experience as a professional chef, expertise in culinary products, and e-commerce.
Write a 1,000-1,200-word blog post that highlights the artistry, materials, and precision of high-end Japanese chef’s knives, explaining how they enhance cooking experiences and last a lifetime. Maintain an elegant yet accessible tone that aligns with a luxury lifestyle brand.
Use short, descriptive paragraphs and markdown headings for structure. Avoid keyword stuffing but ensure natural keyword placement. Mark at least two places for internal links to the brand’s product page and one external link to a reputable culinary publication.
Optimise for SEO using the following inputs:
Primary keyword: “high-end chef’s knives”
Secondary keywords: “Japanese kitchen knives,” “premium chef knife,” “professional kitchen tools,” “best chef knives 2025”
Target search intent: informational + transactional; Meta title: How to Recognise High-Quality Japanese Kitchen Knives”
Note that this is a stripped-down example, and adding further details about the kind of writer you want an AI model to play can help you achieve better results from the first draft.
By developing a prompt recipe that consistently gives you on-brand results, you’ll be able to automate a key part of the AI content generation process, and make life easier when you fact check and redraft.
Embrace Iterative Refinement
Iterative refinement is a crucial technique for AI prompt engineering, and can greatly enhance the quality of a model’s output when using AI for on-page SEO.
The basic idea of iterative refinement is that instead of asking an AI to simply write a page and then investing in its first output, you start with a rough version of the prompt, and incrementally improve it until the results align with your content goals.
To use iterative refinement to improve your results, it’s important that you:
Start strong with a prompt that defines the target outcome and utilises your prompt recipe.
Evaluate the initial output for keyword placement and density, tone of voice, factual accuracy, and SEO-friendly structure.
Refine the prompt based on your findings, using simple, straightforward instructions.
Rinse and repeat.
Though iterative refinement drags out the process of creating and publishing (something you’ll probably want to avoid if you’re considering a move towards using AI for SEO) this will lead to ultimately better results, and greater long-term value from every page you push live.
Having trouble getting your SEO prompts right? My SEO prompt engineering services give you bespoke, on-brand prompt templates, with keyword-based content ideation so you can rank higher faster.
How to Use AI for Technical SEO
If you think “technical SEO” is all crawl errors and XML sitemaps, congratulations! You’re already intimately familiar with the least sexy part of digital marketing.
The good news is this is where AI adds some of its biggest wins, giving you a great opportunity to accelerate outputs without risking your site’s reputation.
Some of the most practical ways to use AI for technical SEO include:
Automated Audits and Prioritised Recommendations
Use AI to parse crawl data, Search Console, and Lighthouse runs and turn them into a priority-ordered worklist with estimated impact and difficulty (e.g. “Fix render-blocking JS on category pages - medium effort, high traffic impact”). Today, the majority of audit tools have AI-enhanced recommendation engines, which allow you to speed up triage so you can tackle the 20% of issues that cause 80% of harm.
Log Analysis at Scale
Feed server logs into an AI assistant or analytics pipeline to surface-crawl anomalies (for example bots hitting URLs with parameters, unusual spikes in 4xx/5xx errors, or low-frequency crawler coverage on your most valuable pages). AI can cluster similar issues, propose the likely root cause, and even generate regex patterns to canonicalise or redirect any URLs that are causing problems.
Automating Schema and Structured Data Generation
For large catalogues or publisher networks, AI can generate item-specific JSON-LD snippets (product, FAQ, article schema) from product feed attributes or article metadata.
I use AI for this step in pretty much everything I write now, and while it is faster, AI tends to make mistakes pretty often. Always validate against Schema.org’s tool, and check Google Search Console crawls after deployment. Google’s docs let you use generative tools for structure, but the output must add real value and be accurate.
Content Render and JS Troubleshooting
Use AI to fix and diagnose pages where bots and browsers disagree (server-side vs client-side rendering). An LLM can suggest whether a page needs prerendering, critical CSS changes, or another approach based on Lighthouse scores and your JS framework.
Automatic Meta and Hreflang Suggestions
Let AI draft title/meta templates and hreflang maps for international sites, then have human SEOs review before you push them live. This speeds up your rollout, but still keeps ultimate control in-house.
How to Use AI for Link Building
Link building is part art, part relationship, part detective work - and AI helps most with the detective and templating parts. But make no mistake: links are still earned, not manufactured.
Here’s how to use AI for link building, maximise results, and avoid common pitfalls.
Scaling Link Building Opportunity
AI can scan SERPs, competitor backlink profiles, and topical mentions to generate a ranked list of link building prospects (resource pages, roundups, journalists, niche blogs), while also giving you some handy notes that explain why each target is relevant. Combine these lists with data from your SEO tool to effectively prioritise by traffic and authority.
Hyper-Personalised Outreach Emails
Use AI to craft outreach email templates that reference a target’s recent article, the mutual value you’ll both see from a new link, and propose an angle. Obviously, this will always be faster than starting from a blank document, but you must always human-edit the first 20-50 messages to ensure factual accuracy and tone. AI link building automation should be deployed to increase response rates, not decrease quality.
Data-led Campaign Ideation.
Feed AI key information about your brand, and ask it to suggest PR hooks, data angles, or content formats that tend to attract links in your niche (surveys, toolkits, microsites). Pair the ideas it shoots back with a media list built from journalist beats and topical relevance.
What to avoid when using AI for link building:
Mass-sending cookie-cutter outreach. AI that writes hundreds of identical pitches will burn down your domain reputation and earn only a handful of links. Human nuance ensures better results.
Buying low-quality links or automating link insertion at scale. That’s a quick route to Google penalties, and a long-term loss of trust.
The Risks: When NOT to Use AI for SEO
AI is powerful, and when it’s applied carefully it can have a huge positive impact on your SEO output and results. However, it’s still a fairly blunt instrument, and carries a lot of risk in the wrong hands.
Here are some of the key areas where you should not hand the keys over to SEO.
When Facts Matter More (Medical, Legal, Financial Content)
If your content falls into the YMYL category, and can materially affect a person’s health, finances, or legal standing, never rely on raw AI drafts.
LLMs hallucinate facts and fabricate citations unless tightly constrained and verified. Even when they don’t, these kinds of topics depend heavily on being backed up by a reputable, professional source, and need to be linked to a trusted creator in the right field to be recognised as authoritative. Always use domain experts for such content.
Scaling Low-Value Pages (Thin, Templated Content)
Google explicitly warns against using generative AI to “generate many pages without adding value for users”. This has been gospel for a long time now, but still bears repeating: don’t publish pages unless the content genuinely helps your audience.
When You Lack a Human Quality-Control Workflow
AI needs guardrails: prompt recipes, review queues, fact-checking, and careful versioning. Sites that publish unreviewed AI content have been singled out in Google’s anti-spam work, and last year Google even reported meaningful reductions in low-quality content after policing these kinds of scaled abuses.
When the Brand Voice or Emotional Authenticity is Critical
Even with the best prompt engineering, AI can still struggle to capture authentic emotional nuance. As the web becomes more and more saturated with AI content, and users show a stronger preference for genuinely human-crafted content, this issue will have an increasingly important impact on how we use AI for SEO.
When You’re Ignoring SERP Feature Shifts (AI Overviews)
New SERP features (AI Overviews / AI summaries) are changing click behaviour. Ahrefs found that the presence of AI Overviews reduced clicks to top organic results by a little over a third in its sample of 300,000 keywords.
This illustrates how rank and traffic have become decoupled, and how you may need to shift focus to other KPIs. Traditional rank-tracking reports can lull you into complacency while revenue and lead volume dip. Measure clicks, sessions, and conversions to get a real picture of how your site is performing.
How to Use AI for SEO FAQs
AI isn’t a magic artefact you can delegate all your SEO work to. However, if you understand the tech’s relationship with current SEO conventions, and follow these best practices, it can be an exceptionally powerful piece in your broader toolbox.
I hope this guide has given you a strong starting point as you roll out AI in your strategy and work towards more efficient campaigns.
For more support with using AI for SEO, be sure to check out my other resources, or click the button below for a free, comprehensive SEO audit.
Can I use AI to write every blog post and expect to rank?
Technically you can publish AI-written posts, and a lot of top-ranking pages include some AI assistance depending on the niche. However, human-crafted content still outperforms AI on engagement and steady growth, and Google’s policies stress that AI-generated content is acceptable only if it genuinely helps users and isn’t mass-produced to game rankings. Human editing and EEAT verification remains crucial.
Will Google penalise my site for using AI?
Google doesn’t ban AI content, but it does penalise content that’s unhelpful, deceptive, or scaled purely to manipulate search. If your AI content is high-quality, accurate, and user-first, you won’t automatically be penalised. However, scale and lack of value is playing with fire.
Is AI useful for outreach and link building?
Yes - for prospecting, personalisation drafts, and follow-up sequencing. Having said that, the content creation and relationship-handling should always be human-led. Automation without nuance reduces link success and can hurt your reputation as a brand.
How much traffic will AI-driven channels send to my site?
AI referrals are still tiny compared to search. Ahrefs’ analysis shows LLMs/AI platforms currently account for a very small share of referral traffic (about 0.1% across a sample of 35,000 sites), so organic search and direct channels remain the primary drivers. Plan accordingly.
How do I prevent hallucinations or factual errors?
Constrain your prompts, require source lists from high-quality places, add human fact-checking in your workflow, and avoid publishing claims without citations from authoritative sources. It’s especially important to have humans involved for all claims that could cause reputational or legal harm.
Should I replace my existing SEO team with AI?
Not if you want to rank. AI augments output and reduces repetitive work, but seasoned SEOs provide strategic judgment: prioritisation, relationship-building, brand tone, and EEAT. These are all things that AI models aren’t qualified to own by themselves.
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