The first two have been around for a while. The third is what makes the workflow in this article possible—especially when the model is also agentic.

Recommendation

A note on agentic AI. Agentic AI is about what an AI does once it has access to tools like APIs or MCP. Instead of just answering one question at a time, it can plan a series of steps, use tools in the right order, ask when it’s not confident, and complete a full task on its own—without you having to guide each step. That’s exactly what systems like Agent A and Claude Cowork with MCP are doing. 

What AI does well

Generating keyword ideas at scale. Give any AI assistant a seed topic and ask for 50 variations. It’ll surface long-tail phrases, question formats, and angles you’d never think to type into a keyword tool. A couple of minutes of prompting can double the size of a starting list.

Data operations and math on large keyword sets. The grunt work. For example:

• Deduping overlapping lists from four sources.
• Scoring keywords on a 0–3 scale for how naturally your product fits.
• Clustering by topic semantically (i.e., grouping by meaning and intent rather than exact match).
• Spotting outliers, growth patterns, and volume distributions across a 500-row sheet.

All of this is minutes of AI work vs. a full afternoon of manual filtering.

Short version: anything requiring real SEO data. Without a keyword database connected, the AI may still give you a volume, a KD score, a SERP feature list, and a trend assessment, all of them fabricated from training data.

The data part gets automated. The judgment part doesn’t. AI can hand you 200 prioritized keywords, but it can’t tell you which ones actually fit your business, which ones your team can actually write about, or which ones are worth the opportunity cost of not writing about something else.

You still run the show:

• Set the strategy. What are you trying to grow: awareness, product signups, brand searches? AI sorts keywords against that goal once you’ve set it, not before.
• Decide what “relevant” means. A keyword about your category isn’t automatically worth writing. You know which topics your product genuinely serves and which ones are traffic for traffic’s sake.
• Judge effort vs. reward. AI can show you a keyword with 5,000 volume and KD 25. It can’t tell you whether your team can produce a better article than what’s currently ranking.
• Own the call on what not to do. The hardest keyword research decision is cutting 180 of the 200 keywords AI just handed you. That’s editorial judgment, and it doesn’t get outsourced.

So, treat AI as a fast research analyst, not a SEO/content director.

Tip: If you already have an Ahrefs subscription, you can try Agent A for free for a full month!

You can run the same prompts today in Claude, ChatGPT, Manus, OpenClaw, or even Lovable, but make sure you have an MCP connection or API connection to a keyword database (or exported data from your SEO tool, at minimum). Once connected, the model can query real keyword data mid-conversation (volume, difficulty, traffic potential, SERP data, competitor rankings) and reason over it in the same pass.

1. How to expand seed keywords and cluster them

Give the AI a topic, your site context, and your constraints (KD, traffic potential, intent). It pulls matching keywords and related terms from the database, including terms top-ranking pages also rank for, filters to your thresholds, and clusters everything by parent topic. Each cluster maps to one article.

Here’s an example prompt:
I run [describe your site]. My audience is [describe audience].
Do keyword research for the topic "[your topic]". I want:
- 30+ keyword opportunities, KD  100
- Grouped by parent topic (one cluster = one article)
- Each cluster: suggested title, primary intent, top keyword by TP
- Prioritized by traffic potential
Visualize 

And here you can see Agent A conducting research using Ahrefs data (my prompt and AI’s thinking on the left, the result on the right).

From there, I can tweak the result to make it more useful in practice. For example, I could tell Agent A something like: “add an option that lets me save each of those topics to my scrapbook.”

By the way, scrapbook is an app that I also built with Agent A from a prompt (much like you would with Lovable) to store ideas, inspirations, and sources I’d like to reference later.

Tell the AI your domain and two or three competitors. It pulls their organic keywords, cross-references against yours, and surfaces what they rank for that you don’t. These are proven opportunities: real people search for them, and someone in your space already ranks.

My site is [mysite.com]. My main competitors are [comp1.com, comp2.com].

Find keywords they rank for in the top 20 that I don't rank for at all.
Filter to KD  200.
Group the gaps into topic clusters and rank by traffic potential.

Agent A has a content gap analysis skill pre-installed, so you could just click “Launch” to get started.

Then the Agent takes you through a short questionnaire to understand your specific context.

Keywords you already rank for in positions 4–20 are close enough to page one that a content refresh, better internal linking, or on-page optimization could push them into the top 3.

In this workflow, the AI pulls your organic keywords, filters to this position range, and sorts by the traffic you’d gain from moving up.

My site is ahrefs.com/blog.
Find keywords where I rank between positions 4 and 20. Exclude branded keywords. 
For each: current position, search volume, traffic potential.
Rank by potential traffic gain from reaching position 1–3. Sort by parent topic. 
Which 100 keywords are the best optimization targets right now? Visualize.

And here’s the result in Agent A—a dashboard you can chat with:

Instead of individual keywords, the AI pulls your top pages and compares their traffic across two dates. Pages with declining traffic are candidates for a rewrite or update, especially if they once performed well.

My site is [mysite.com].
Compare organic traffic to my top pages between [6 months ago] and today. Find the pages with the biggest traffic drops. For the top 10 declining pages: current vs. previous traffic, top ranking
keyword, and current position for that keyword. Which should I prioritize refreshing?

If you’re using Agent A, just launch the preinstalled skill.

The results will look something like this (with an attached CSV file). It’s also an example that AI can just give you the answer in chat and a file to download instead of building an entire visual report if you don’t want to.

Sometimes people search for [your brand] + [something] (a feature, a comparison, a use case), but you don’t have a dedicated page for it. So, this prompt tells AI to filter your organic keywords to branded queries and cross-reference against your sitemap. Any branded keyword landing on a generic page is traffic you’re leaving on the table.

My site is [mysite.com]. My brand name is [Brand Name].
Find keywords containing "[Brand Name]" where I rank, but my ranking page is a generic page (homepage, category page) rather than a dedicated one. Also, mark keywords where other domains outrank me. List by traffic potential. Visualize. 

Here’s the result in Agent A.

Queries in question format (“how to…”, “what is…”) and comparison format (“X vs Y”, “X alternative”) map to specific content types and often have lower competition. These are also the query formats most likely to trigger AI answers, and the content most likely to get cited in them.

My site is [mysite.com]. My topic area is [niche]. Find keyword opportunities in two formats:
- Questions: "how to", "what is", "why", "can I", etc.
- Comparisons: "vs", "alternative", "compared to", "instead of" 
Filter to KD  100.
Group by topic. Suggest a content format for each cluster.

And here’s the result, made by AI from scratch in 3.5 minutes.

The AI can pull your keyword metrics broken down by country. You may have significant keyword visibility in a market where your traffic is low. That’s a localization opportunity. Or it can run keyword discovery for a new market you’re considering entering.

This one is a bit more complex, because there are multiple ways to look for these opportunities:

Do an international keyword analysis for [TARGET] vs [COMPETITORS].
1. Rank non-English countries by opportunity:
(competitor_traffic_sum − my_traffic) × competitor_presence.
Pick top [N].
2. Gap keywords per country: where ≥2 competitors rank top 20
and I don't rank top 50 (volume ≥ [MIN_VOL]).
3. For my top [TOP_PAGES] US pages' main keywords:
a. Check the English term in each target country
(volume + SERP via KE serp_overview).
b. Translate each keyword into the country's native language
via LLM, then check volume + SERP for the translation too.
Classify each (page, country) into:
- open: volume exists, nobody ranks top 20 (biggest prize)
- contested: competitors rank, I don't
- defending: I rank, competitors also rank
- owned: I rank solo
4. Roll up by language — weight both gap-keyword count
and open+contested translation opportunities.
Output: country ranking, gap keywords per country, English
and native-language opportunities per page, top 3 languages
to prioritize (explain whether the driver is gap keywords or
translation opportunities).
Use Keywords Explorer's serp_overview (not Site Explorer's) for international SERPs.

And for this prompt, Agent A built a report looking like this:

Trending keywords are those that gain search volume over time, typically within a few months. The AI can pull volume history for specific keywords to spot upward trends before they peak, especially ones that the model’s training data might not cover, since the keyword database updates continuously.

My topic area is [niche].
Find keywords in this space where search volume has grown consistently
over the past 6–12 months and hasn't peaked yet. For each: current
volume, volume 6 months ago, KD, and whether I currently rank for it.

This is another preinstalled skill in Agent A if you want to try it out:

Result:

Not every valuable keyword has high search volume. Your ideal customer may search for niche, specific queries that keyword tools show as low- or “zero-volume”, but these are exactly the queries that AI chatbots surface answers for.

In this example, the AI brainstorms from a persona description, validates against the database, and flags keywords worth targeting even without traditional traffic potential.

My ideal customer is [describe: role, problems, goals, how they search].
Brainstorm 30 keywords this person would search for, including:
- Problem-awareness queries (they know they have a problem)
- Solution-comparison queries (they're evaluating options)
- Niche queries they might ask an AI chatbot rather than a search engine
Then check which have measurable search volume. Flag zero-volume ones
separately. 

Agent A built the following artifact out of the prompt:

A skill is a saved, reusable instruction for AI. Instead of rewriting the same prompt every time, you can type something like “/keyword-audit” and the AI already knows what to do: what data to pull, how to filter it, and how to format the results.

The AI can run a skill whenever you need it. You can use skills on their own, call them as part of a bigger workflow, or let the AI use them automatically when they fit the task.

Under the hood, a skill is just a markdown file. It is plain-language instructions, not code. To illustrate, here’s an excerpt from a citation freshness skill markdown file I’ve been working on:

Because each skill has a clear input and output, you can chain them together into a pipeline. The output from one skill becomes the input for the next, and Claude can move through the whole sequence on its own.

A keyword research pipeline might look like this:

/keyword-audit → /cluster → /content-brief

You start the first step. The AI audits your site, passes the results into the clustering skill, and then turns the best clusters into content briefs, without you having to manage each handoff.

To build your own custom skills, you just describe what you want each step to do, and the AI creates the skill files for you—no coding skills required.

For example, you could prompt it like this:

Create a skill called `/keyword-audit` that:
* takes my domain and 2–3 competitor domains as input
* looks for competitor gaps, low-hanging fruit, and declining keywords using simple heuristics
* removes duplicates across all results
* returns a table with: keyword, which heuristic found it, KD, TP, and recommended action (`create`, `update`, or `optimize`)
* saves the output in a `reports/` folder with today’s date in the filename

Or you can ask the AI to create a skill out of the conversation you’ve just had.

You do not even need to invent your own skills from scratch. You can also ask the AI to turn a blog post or help doc into a skill.

Here are a few of our blog posts that would be great starting points for skills:

[/post_nav_link]

A few questions that come up a lot when people start working AI into their keyword research.

Can AI do keyword research on its own?

Depends on the setup. A general AI model without any data integrations can brainstorm and database operations, but can’t provide accurate volume, KD, or SERP data. An AI with a keyword database connected via MCP is a different situation: it has live access to real search data and can run the full workflow end-to-end. The capability gap between those two setups is significant.

Yes, but you have to bring the data yourself. ChatGPT has no keyword database of its own, so any volume, KD, or SERP figures it generates unprompted are fabricated. Export your keyword data from a real SEO tool (Ahrefs, for example) and upload the CSV—ChatGPT can then dedupe, cluster, tag intent, visualize the data, and answer follow-up questions against it. For live querying without the manual export step, connect it to a keyword database via MCP.

A combined workflow beats either approach alone. AI handles the analysis and organization that’s slow to do manually. A keyword tool provides the data AI can’t generate on its own. With MCP connecting the two, the manual handoffs disappear entirely.

Will AI replace keyword research tools?

No. AI without a keyword database is guessing—it can generate ideas and reason over data you hand it, but it has no way to know what people actually search for or how competitive a term is. What AI changes isn’t whether you need a keyword tool, but how you interact with it: the tool still provides the data, and the AI handles the filtering, clustering, and synthesis that used to take hours of manual work.

AI changes who does the mechanical parts. As a researcher with MCP access, the AI handles things like discovery, filtering, SERP validation, and clustering in a single conversation. As an analyst working from your exports, it still eliminates the most time-consuming parts of the process. Either way, what used to take a full day now takes a morning.