You sit down to plan your content calendar. You open your keyword research tool. You find a few terms with decent volume. You write the articles. And then... nothing. The AI models that increasingly drive discovery in your niche don't cite you.
The problem isn't your writing. It's your research process. Traditional keyword research tells you what people type into Google. It doesn't tell you what AI models explore when someone asks "What's the best project management tool for remote teams?"
Those are two very different things. And if you're only doing keyword research, you're missing the content AI actually needs.
Why keyword research falls short for AI visibility
Traditional keyword research was built for a search engine that returns ten blue links. You find a term, check the volume, estimate difficulty, and write a page optimized for that exact phrase. That worked for a decade.
AI doesn't work like that.
When someone asks ChatGPT a question, the model doesn't look for pages optimized around a specific keyword. It synthesizes an answer from dozens of sources, exploring sub-topics, comparing perspectives, and pulling from the most authoritative and complete content it can find.
A single user question like "What CRM should a freelance designer use?" triggers a cascade of internal reasoning. The model considers pricing, ease of use, integrations with design tools, invoicing features, mobile access, and more. Each of those sub-topics is a potential content opportunity you'd never find in a keyword tool.
Keyword tools might show you "CRM for freelancers" with 1,200 monthly searches. They won't show you that AI models frequently explore "CRM with invoice tracking" or "simple CRM without enterprise features" as part of answering that question. Those hidden sub-queries are where the real opportunities live.
How AI models actually explore topics
Understanding this is the key to finding real content opportunities.
AI generates sub-queries internally. When a user asks a broad question, the model breaks it into smaller pieces. "Best accounting software for restaurants" becomes sub-queries about restaurant-specific features, pricing tiers, POS integration, inventory tracking, and tax compliance. Each sub-query is a topic AI needs good content about.
AI follows topic clusters. Models don't just answer the surface question. They explore adjacent topics to build a complete picture. If someone asks about email marketing for e-commerce, the model might pull from content about abandoned cart sequences, segmentation strategies, and deliverability. Your content on one topic can earn citations for several related queries.
AI synthesizes from multiple sources. Unlike Google, which sends users to one page, AI blends information from many sources into a single answer. This means even a niche topic with limited competition can get you cited if your content is the most direct and complete source available.
This creates a fundamentally different opportunity landscape. The topics that matter for AI visibility often don't match the keywords that matter for Google rankings.
Method 1: Manual discovery
You don't need tools to start. You need 30 minutes and access to the AI platforms your audience uses.
Step 1: Ask the questions your audience asks. Open ChatGPT, Perplexity, Gemini, Grok, and Claude. Type the exact questions your ideal customers would ask. Not SEO-optimized queries, real questions in natural language. "I'm a freelance copywriter, what invoicing tool should I use?" "How do I track my marketing ROI as a solopreneur?" "What's the best way to manage client projects without spending a fortune?"
Step 2: Study what AI explores. Read the full responses carefully. Notice:
- What sub-topics does AI cover? These are the content pieces AI is looking for.
- Which sources get cited? Those are your content benchmarks.
- Where does the answer feel thin or vague? That's your opportunity.
- What follow-up questions does AI suggest? Each one is another content opportunity.
Step 3: Check "People Also Ask" on Google. These questions often mirror the sub-queries AI models explore. They're a fast way to discover related topics you might have overlooked.
Step 4: Map what you found to your existing content. For each sub-topic AI explored, ask: do I have content that directly addresses this? Not content that sort of touches on it, content that answers it head-on. The gaps you find are your content opportunities.
This manual process works well for 10-15 core queries. You'll typically discover 3-5 content opportunities per query, giving you 30-75 potential topics from a single research session.
Method 2: Using a content opportunity finder
Manual research is valuable but time-consuming. If your niche has dozens of important queries, doing this analysis one by one takes hours.
The free content opportunity finder automates this process. Enter your domain and a few core topics. The tool analyzes what sub-queries AI models explore when users search in your niche, then clusters them by topic and intent.
What you get back is a prioritized list of content opportunities, each with:
- The specific topic and why it matters
- Which of your existing pages (if any) partially cover it
- A priority score based on how many AI queries the topic serves
- Suggested content format (guide, comparison, FAQ page, etc.)
This doesn't replace manual research. It accelerates it. The tool finds patterns you'd miss in manual testing, especially when looking at how multiple AI queries overlap on the same underlying topic.
How to evaluate and prioritize opportunities
You'll end up with more opportunities than you can pursue. That's a good problem. The key is prioritization.
Score each opportunity on three factors:
Breadth: How many AI queries does this topic serve? A topic that's relevant to 8 different user queries is more valuable than one that serves just 1. Comprehensive guides tend to score high here because they capture clusters of related sub-queries.
Opportunity gap: How weak is the existing content? Search for the topic yourself. Read what AI currently cites. If the existing sources are thin, outdated, or overly generic, that's a wide-open opportunity. If strong, comprehensive content already exists from authoritative brands, the barrier is higher.
Business relevance: How closely does this align with what you sell? Not every content opportunity is worth pursuing. A freelance designer shouldn't write about enterprise procurement workflows, even if there's a gap. Focus on topics where appearing in AI answers drives real business outcomes for you.
Create a simple spreadsheet. Score each factor 1-5. Multiply them together. The highest scores are your priorities.
A topic scoring 4 (breadth) x 5 (gap) x 4 (relevance) = 80 gets written before a topic scoring 2 x 3 x 5 = 30, even if the second one feels more directly "on brand."
Content formats that win AI citations
Not all content is equally useful to AI. Certain formats earn citations more reliably.
Comprehensive guides work best for broad topics. "Complete guide to project management for freelancers" gives AI a single authoritative source to cite across many sub-queries. Aim for 2,000-3,000 words with clear sections covering different aspects of the topic.
Comparison articles are gold for commercial queries. "Notion vs Trello for small teams" directly answers a question AI gets asked constantly. Be honest. Acknowledge both sides. AI favors balanced, specific comparisons over one-sided sales pitches.
FAQ-rich pages let AI extract precise answers to specific sub-queries. Structure each Q&A clearly. Make the answer self-contained, don't require reading the rest of the page to understand it.
Data-driven analysis stands out because it's hard to replicate. Original data, surveys, benchmarks, or industry analysis gives AI something unique to cite. "We analyzed 500 freelancer invoicing workflows" is more citable than "here are some tips."
Problem-solution pages match how users actually query AI. "How to handle late-paying clients as a freelancer" directly maps to a real user question. The more specific the problem, the more likely AI is to cite your solution.
What all these have in common: direct answers, clear structure, genuine depth, and honest perspective. AI models reward content that's actually useful, not content that's just optimized for a keyword.
From opportunity to published content
Finding opportunities is half the work. Turning them into published content that earns citations is the other half.
Start with the answer. Before writing anything, articulate the direct answer to the core question in 2-3 sentences. This becomes your opening paragraph. AI needs to find this answer quickly.
Outline around sub-queries. Use the sub-topics you discovered during research as your H2 headings. Each section should answer a specific sub-question completely. This mirrors how AI breaks down the topic internally.
Add specificity. Names, numbers, dates, examples. "Mentionable tracks visibility across ChatGPT, Perplexity, Gemini, Grok, and Claude" is more citable than "you should track visibility across multiple AI platforms." AI prefers concrete details.
Include structured elements. Comparison tables, numbered lists, FAQ sections, and clear headings all help AI extract information. Don't overdo it, but make sure the most important information is easy to find.
Publish and verify. After publishing, wait 2-4 weeks for AI platforms to discover your content. Then test it. Ask the original queries on each AI platform and see if your content gets cited. A free AI visibility check can help you see where you stand across all five platforms.
Iterate based on results. If you're not getting cited, look at what is getting cited instead. How is that content different from yours? Is it more comprehensive, more specific, better structured, or from a more authoritative domain? Use that analysis to improve.
Getting started this week
You don't need a perfect plan. You need a starting point.
Pick 5 queries your ideal customers ask AI. Run them on ChatGPT and Perplexity. Note every sub-topic AI explores. Map those sub-topics to your existing content. Circle the gaps.
You'll likely find that you have content for the obvious topics but nothing for the specific sub-queries AI actually explores. Those gaps are your first content opportunities.
Start with the one that has the widest gap and the highest business relevance. Write one comprehensive piece. Publish it. Track the results.
Then do it again. The businesses winning AI visibility right now aren't doing anything magical. They're just systematically finding and filling the content gaps that AI models need. You can do the same.
