The AI Tools I Actually Use for SEO Work, and How I Use Them

AI tools come and go fast. Every week there's a new platform claiming to save hours or replace a whole workflow.

After testing a lot of them, the ones that stuck aren't the flashiest. They're the ones that fit into how I already work and keep solving real problems. This post covers what made the cut, what didn't, and the workflow I actually use them in: going from a client brief to a full SEO strategy in roughly two hours.

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The shortlist: three tools, three jobs

My stack is deliberately small. One tool for creating, one for research, one for critical thinking.

ChatGPT (OpenAI): drafting and structuring

This is the tool I use most, because it adapts across tasks rather than doing one thing. I use it for first drafts, turning messy notes into structure, generating idea lists and clustering large sets of keywords into themes.

Perplexity: current, sourced research

When I need current information, a quick comparison or claims I can check against a source, Perplexity is where I go. It sits between search and synthesis, and the citations make it easy to verify before anything goes in front of a client.

Claude (Anthropic): reasoning and review

I use Claude for longer documents and harder thinking: reviewing long-form content, stress-testing a strategy, or asking "what am I missing here?" It's my second lens when I want to be challenged rather than agreed with.

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The ones I stopped using

I dropped tools that promised everything but added friction. The pattern was usually the same:

• Too much setup for the value they returned.
• Generic output that needed so much editing it wasn't faster than writing it myself.
• Duplicating a stronger tool, often a thin layer over the same underlying model with a subscription on top.

A lot of niche AI apps fall into this group. They look impressive in a demo, but if they don't fit into real work, they become another tab to manage.

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Where AI actually earns its place: SEO strategy

The biggest time saving I've found is in early-stage SEO strategy.

Traditionally, the research phase (keyword discovery, competitor analysis, content audits, intent mapping) is spread across several tools and a lot of manual work. You export spreadsheets, clean the data, and spend more time organising information than interpreting it.

AI doesn't replace the strategy. It shortens the path from raw data to the point where you can make decisions. Here's the workflow.

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The 2-hour workflow, step by step

Step 1: Brief and positioning (15–20 minutes)

Review the client's website, brief and main competitors. Use AI to pull out positioning, likely audience segments and gaps in the messaging.

A starting prompt:

Here is a business's homepage copy, services page and brief. Summarise: what they sell, who it's for, how they describe themselves differently from competitors, and any gaps or contradictions in the messaging. Flag anything you're inferring rather than reading directly.

This gives you a structured first pass. Check it against what the client has actually told you about their goals. AI spots patterns quickly, but it doesn't know which customers are most profitable.

Step 2: Build the keyword universe (25–35 minutes)

Combine AI ideation with exports from a real SEO data source: Ahrefs, Semrush or Google Search Console. AI-generated keyword lists on their own have no reliable search volume, so treat them as ideas, not data.

Then use AI to cluster the export:

Here is a keyword export with search volume. Group these keywords into topic clusters, and label each cluster by search intent: informational, commercial or navigational. Put any keyword that doesn't fit this business in a separate "not relevant" group, and explain why.

Review the clusters by hand. The final keyword map should reflect what the business wants to be found for, not just what has the most volume.

Step 3: Competitor gap analysis (25–30 minutes)

Use real ranking and content data to compare competitors against your keyword clusters. AI helps find the patterns: missing topic clusters, areas where a competitor is clearly strong, and where nobody has a good answer yet.

Here are the topics and pages ranking for three competitors, and our client's current pages. Which clusters do competitors cover that the client doesn't? Where is every competitor's content thin? List gaps in order of likely business value, with your reasoning.

Don't treat AI as a live ranking source unless it's connected to verified data. The goal isn't to copy competitors, it's to find the places where your client can say something more useful.

Step 4: Prioritise and plan (20–30 minutes)

Turn the findings into a roadmap: quick wins, content to create or improve, technical fixes and longer-term plays. Rank them by effort, business impact and likelihood of ranking.

Here are the opportunities from the research. Sort them into quick wins, medium-term projects and long-term plays. For each, estimate effort as low, medium or high and note any dependency.

This is the step where human judgement matters most. AI can sort a list. It can't tell you that the client's busiest season starts in six weeks, or that one service makes most of the margin.

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What this approach gets right, and where it needs checking

AI doesn't replace the strategist. It cuts the time spent on synthesis-heavy work, so more of the two hours goes on decisions.

It needs checking in three places:

• Numbers. Search volumes, rankings and traffic estimates must come from a real data source, never from the model's memory.
• Facts about the business. AI fills gaps with plausible guesses. Anything it says about the client should be verified with the client.
• Priorities. Its ranking of opportunities is a starting point. Business context changes the order.

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What I've learned

The best AI stack isn't the biggest one. A few tools that genuinely improve speed, quality or clarity beat a dozen that each do one thing.

And the most effective SEO workflows aren't AI-only or manual-only. They're hybrid: the tools handle the heavy lifting on data and drafts, and a person makes the calls.

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FAQ

What are the best AI tools for SEO?

For most SEO work, a general-purpose assistant like ChatGPT or Claude, plus a research tool with citations like Perplexity, covers the majority of tasks. Pair them with a real SEO data source such as Ahrefs, Semrush or Google Search Console, because AI tools on their own don't have reliable search volume or ranking data.

Can AI build an SEO strategy?

AI can speed up the research and organisation behind an SEO strategy, such as clustering keywords, summarising competitors and sorting opportunities. It can't replace the judgement about which opportunities matter most to a specific business, and its outputs need checking against real data.

Can I trust keyword data from ChatGPT?

No. AI chat tools don't have live access to search volume data and can produce numbers that look precise but are guesses. Use them to generate and group keyword ideas, then get volumes and difficulty from an SEO platform or Google Search Console.

How long does an SEO strategy take with AI?

A first-pass strategy, from brief to prioritised roadmap, can be done in around two hours with an AI-assisted workflow and good data exports. A larger site or a more competitive market will take longer, especially at the review and prioritisation stage.