How do you find AI content opportunities? Looking for queries where we can actually compete

Here’s my framework for identifying AI content opportunities:

Step 1: Start with what you know

Use traditional data sources to create a seed list:

• Keywords you rank for
• Questions customers ask
• Topics competitors cover
• Industry trends

Step 2: AI opportunity audit

For each seed topic, test in AI platforms:

• Does AI provide a good answer?
• Are you mentioned?
• Who IS mentioned?
• Are there gaps in the AI answer?

Step 3: Categorize opportunities

Step 4: Prioritize by impact

Score each opportunity:

• Business value (1-5)
• Achievability (1-5)
• Competitive advantage (1-5)

Focus on high scores first.

Competitive analysis approach to finding opportunities:

The logic:

If competitors are getting cited for certain queries, those queries are proven valuable. Your opportunity is to get cited instead of or alongside them.

Process:

1. List top 5 competitors

2. For each competitor, identify:

   • What are they known for?
   • What content do they have that you don’t?
   • Where do they show up in AI that you don’t?

3. Test overlap queries: “Best [product category]” “[Problem you both solve]” “[Industry] + [topic]”

4. Document findings:

   • Query: “best marketing automation tools”
   • Competitor A: Cited 1st
   • Competitor B: Cited 3rd
   • Us: Not mentioned

5. Analyze the gap:

   • What do they have that we don’t?
   • Is it content? Authority? Format?
   • Can we create better?

The output:

A prioritized list of queries where competitors win that you want to target.

Connecting traditional keyword research to AI opportunities:

Start with proven demand:

Traditional keyword tools show what people search. These same queries are asked to AI.

Keyword types that translate well to AI:

1. Question keywords: “How do I…”, “What is…”
2. Comparison keywords: “X vs Y”, “Best X for Y”
3. Recommendation keywords: “Best X”, “Top X”
4. Problem keywords: “How to fix…”, “Why doesn’t…”

The translation:

Keyword: “best email marketing software for small business” AI query: “What’s the best email marketing software for a small business?”

Same intent, different format.

Finding gaps in existing AI answers:

What makes an AI answer “incomplete”:

1. Outdated information - References old data, tools, practices
2. Missing options - Doesn’t mention obvious alternatives
3. Shallow depth - Answers without detail or nuance
4. Generic advice - Not specific to query context
5. No sources cited - AI unsure or lacks good sources

Opportunity indicators:

Testing methodology:

Ask the same question 3 different ways. Note where AI struggles or gives inconsistent answers. Those are opportunities.

Example:

• “What’s the best CRM?”
• “What CRM should a 10-person startup use?”
• “CRM for managing 500 customer relationships”

Different contexts, different answer quality. Find where AI struggles.

Scrappy approach for finding opportunities without tools:

The manual audit I do monthly:

Step 1: List 30 queries that matter to your business

• 10 branded queries (“What is [your company]?”)
• 10 category queries (“Best [product type]”)
• 10 problem queries (“How to [thing you solve]”)

Step 2: Test each across 3 platforms

• ChatGPT
• Perplexity
• Google AI

Step 3: Document in simple spreadsheet

Step 4: Identify patterns

• Where do you never appear?
• Where do competitors always appear?
• Where are AI answers weak?

Step 5: Prioritize action

• Pick 3-5 highest-priority gaps
• Create content to address them
• Retest next month

Time investment: ~4-6 hours monthly. Not scalable forever, but good for getting started.

Enterprise approach with tool support:

Our process:

1. Set up monitoring (Am I Cited)

   • Input our target query set (500+ queries)
   • Input competitor brands
   • Track across all major AI platforms

2. Weekly analysis

   • Where are we appearing vs not?
   • Where are competitors gaining?
   • Which content is getting cited?

3. Opportunity scoring

4. Monthly prioritization

• Top 10 opportunities reviewed
• Content assignments made
• 60-day measurement cycles

The key:

Systematic tracking reveals patterns that manual testing misses. Trends matter more than snapshots.

This thread has given me a complete framework. Here’s my approach:

Phase 1: Build query inventory (Week 1)

• Start with traditional keyword data
• Add customer questions (sales, support)
• Include competitor topic coverage
• Target: 100 queries to audit

Phase 2: Initial audit (Week 2-3)

• Test all queries across ChatGPT and Perplexity
• Document current state
• Categorize opportunities

Phase 3: Prioritization (Week 4)

• Score by business value + achievability
• Identify top 20 opportunities
• Create content plan

Phase 4: Ongoing monitoring

• Set up tracking (Am I Cited)
• Monthly review of new opportunities
• Quarterly strategy adjustment

Opportunity categories I’ll track:

1. Gaps: We should be there but aren’t
2. Swaps: Competitor cited, we’re not
3. Improvements: AI answer is weak
4. Emerging: New topics in our space

Key insight:

Start with queries proven valuable (competitors being cited) rather than guessing. Let the competitive landscape guide priorities.

Thanks everyone for the frameworks and practical advice.