Using AI for SEO content isn’t really about churning out drafts at speed. Understanding SEO optimised content using AI matters for any business serious about their online presence. The wins come from quality control, nailing search intent, adding genuine information gain, building sensible internal links, and using a structure that’s easy for Google to crawl and for humans to skim.

Start with intent, not keywords

If you start with a keyword list and tell an AI to “write an article”, you’re almost guaranteed to end up with thin content. It’ll read like a bland remix of whatever’s already ranking, and Google won’t have much reason to keep it near the top.

Better results come from defining the job the page needs to do. “Best accounting software for tradies” is usually comparison intent with real constraints, GST, job costing, mobile invoicing, Xero integration, pricing traps. “How to lodge BAS” is task intent, and it needs steps, screenshots, and warnings about the mistakes people make. Same industry, completely different page shape.

At this stage, AI is more useful as a research assistant than a writer. Get it to map likely intents, decision factors, and the elements a proper answer must include, then sanity check it against a live SERP. If the top results are listicles and you publish a 3,000 word essay, you’re not being “thorough”, you’re mismatching the query.

Build a brief that forces specificity

AI produces fluff when the brief is loose. A good content brief is basically a set of constraints that stops the model defaulting to generic advice. In practice, you want to lock in the audience, geography, where it matters, the product/service context, and the “non negotiables” that actually demonstrate expertise.

For small businesses, those non negotiables are usually the operational details competitors dodge because they’re fiddly. Minimum spend ranges, lead times, compliance, what can go wrong, who it’s not for, and what to have ready before you call, this is the stuff that makes a page useful. If it’s not in the brief, AI won’t, and shouldn’t make it up.

If you want a more systematic way to tighten prompts, the approach in Prompt Engineering for Content Creation: A Practical Guide is as close as we’ve found to “measure twice, cut once”.

Use AI to create a content outline that matches how Google parses pages

Once you’re past the basics, structure is SEO. A strong outline isn’t just headings that sound nice, it’s an information hierarchy that lets Google and readers pull meaning quickly.

We usually aim for one clear H2 per major subtopic, then H3s that answer the follow on questions people naturally have once they’re in that section. AI can generate outline options quickly, but you still need to edit like a subject matter expert. If the outline doesn’t surface the obvious objections, caveats, and edge cases, it’s not ready to draft.

And don’t let AI bury the lead. If the query is about “cost”, the cost section shouldn’t be tucked into the last 10% of the page. If it’s “best”, your comparison criteria should show up early, with a transparent method.

Stop keyword stuffing by switching to entity coverage

Keyword stuffing happens when someone treats SEO as repeating a phrase until it “looks optimised”. That era is long gone. You end up with a page that reads poorly and still doesn’t rank because it hasn’t actually covered the topic.

A better model is entity and concept coverage. If you’re writing about “solar rebates Queensland”, the page should naturally touch the related entities and constraints, eligibility, STCs, approved installers, system size, paperwork, timing, and common exclusions. When those are properly covered, the primary keyword generally appears on its own at a sensible frequency.

AI can help by listing the entities, terms, and subtopics that commonly sit around the query, then you decide what’s relevant for your audience. This is also where thin content gets exposed. If you can’t include those concepts because you don’t offer the service or you don’t have the knowledge to be accurate, don’t publish the page.

Write the first draft with AI, then add the parts AI can’t do

With a tight outline and clear constraints, AI can produce a perfectly serviceable first draft. The ranking lift usually comes from what you add afterwards, original examples, real process, local nuance, and the “we’ve seen this go wrong” details that only come from doing the work.

When we edit AI drafts, we watch for three common red flags. First, confident claims with no mechanism behind them, it sounds right, but doesn’t explain how. Second, “everyone” language that could apply to any industry. Third, missing trade offs. Real advice has constraints and downsides.

Thin content also sneaks in through repetition, same idea, different wording. Be ruthless. If two paragraphs don’t add new information, keep the stronger one and move on.

If you want a quick checklist of the failure modes we see most often, The Biggest AI Content Mistakes People Make and How to Fix Them covers the patterns.

Optimise for crawling and internal linking, not just on page keywords

Small businesses often publish genuinely good pages that never get properly discovered because the site structure is a mess. AI written content won’t fix that. If anything, it can amplify the problem if you pump out lots of similar pages that end up competing with each other.

Before publishing, look at where the page sits in your information architecture. It should be reachable in a few clicks from relevant hubs, and it should link to supporting pages that help the reader finish the job. Internal links also signal topical clusters and which pages you consider priority.

If you’re scaling content with AI, crawl behaviour matters more than most people assume. A useful primer is Understanding Crawl Budget and Why It Matters, especially if you’re adding lots of new URLs.

Handle E-E-A-T the practical way

On most small business sites, E-E-A-T becomes real when you can show experience and accountability. AI can help polish and format, but it can’t replace proof.

Include specifics that show you’ve actually done the work, photos from jobs, screenshots of real dashboards, with sensitive info removed, examples of deliverables, and plain English explanations of what you do differently. And if you’re making claims about results, anchor them in context. “We lifted traffic” is meaningless without the timeframe, the starting point, and what changed.

Be especially careful with YMYL adjacent content, finance, health, legal. AI drafts in these areas need tighter review, clearer disclaimers, and often a narrower scope. If you can’t verify it, don’t publish it.

Make AI part of your update cycle, not just production

Plenty of pages slide in rankings because they go stale, not because they were ever “bad”. AI is handy for refreshes if you treat it like an analyst. Give it your page, your target queries, and a list of competitor headings, then ask it to flag gaps and outdated sections. You still decide what’s accurate and what’s worth keeping.

We’ve had strong results by tightening intros to match intent, adding missing comparison criteria, updating screenshots, and improving internal links. Often the biggest win is deleting sections that don’t serve the query. More words isn’t better, more relevance is.

What a solid AI + SEO workflow looks like in practice

Put together, the workflow is simple, check the SERP and define intent, write a constraint heavy brief, build an outline that reflects real subtopics, generate an AI draft, then edit like you mean it, specificity, accuracy, usefulness. Finish with technical hygiene, a title and meta that match the query, clean headings, sensible internal links, and a URL that fits your site structure.

If you’re publishing at volume, treat AI output as raw material. The editing and QA is the product.

About the Author

Nicholas McIntosh

Nicholas McIntosh is a digital strategist driven by one core belief: growth should be engineered, not improvised. 

As the founder of Tozamas Creatives, he works at the intersection of artificial intelligence, structured content, technical SEO, and performance marketing, helping businesses move beyond scattered tactics and into integrated, scalable digital systems. 

Nicholas approaches AI as leverage, not novelty. He designs content architectures that compound over time, implements technical frameworks that support sustainable visibility, and builds online infrastructures designed to evolve alongside emerging technologies. 

His work extends across the full marketing ecosystem: organic search builds authority, funnels create direction, email nurtures trust, social expands reach, and paid acquisition accelerates growth. Rather than treating these channels as isolated efforts, he engineers them to function as coordinated systems, attracting, converting, and retaining with precision. 

His approach is grounded in clarity, structure, and measurable performance, because in a rapidly shifting digital landscape, durable systems outperform short-term spikes. 


Nicholas is not trying to ride the AI wave. He builds architectured systems that form the shoreline, and shorelines outlast waves.

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