Our industry reports get cited by AI 5x more than blog posts - the original research advantage is real

The methodology matters as much as the data. Here’s why.

What makes research AI-citable:

Report structure for AI:

1. Executive summary (most important - this gets cited)
2. Key findings (bullet points with specific data)
3. Methodology (establishes credibility)
4. Detailed findings (depth for authority)
5. Industry implications (analysis)
6. Data appendix (for verification)

The executive summary trick:

Your executive summary should be independently citable. AI often pulls just from the summary.

Write it as: “Our survey of [N] [professionals] found that [specific finding with %].”

This is exactly what AI needs to cite.

Survey-based research tips for AI visibility:

Survey design for citable results:

1. Ask quantifiable questions

   • “What percentage of budget…” vs “Do you spend on…”
   • Numbers are more citable

2. Include trend questions

   • “Compared to last year…”
   • Trends get cited heavily

3. Get comparisons

   • By industry, company size, role
   • Enables multiple citation contexts

4. Target sample size:

   • 100+ for basic credibility
   • 500+ for strong credibility
   • 1,000+ for industry report status

Distribution for credibility:

Our process:

• 500 responses target
• Mix of customer + panel
• Total cost: ~$3,000
• Analysis + write-up: $2,000
• Design: $1,000

Total: $6,000 for credible survey report

B2B research report strategy:

What B2B buyers want (and AI cites):

The dual purpose:

Great research reports serve:

1. AI visibility (citations)
2. Lead generation (gated downloads)
3. Sales enablement (proof points)

Our report calendar:

• Q1: Annual industry benchmark
• Q2: Buyer survey (decision-making)
• Q3: Technology trends
• Q4: Year-end predictions

The lead generation angle:

Reports gated behind forms: Great for leads Reports open for AI: Great for citations

Solution: Ungated executive summary + gated full report

AI cites the summary. Leads download the full report.

The compounding value of research content:

Research vs blog post lifecycle:

Why research compounds:

1. Original data stays unique
2. Historical data becomes reference
3. Updates create citation chains
4. Other content cites your research

Our 3-year-old report:

Still gets 40+ AI citations/month. Blog posts from same time get 0.

The investment lens:

Blog post: $500, 6-month lifespan, 2.8 citations/piece Research report: $5,000, 3+ year lifespan, 45 citations/piece

Report costs 10x, delivers 16x citations, over 6x longer.

Research is content infrastructure, not content.

Small team research strategies:

We have 2 people. Here’s how we do research:

1. Quarterly data analysis (no budget)

• Analyze our own product data
• Look for patterns and trends
• Write up findings with visualizations

2. Annual customer survey (minimal budget)

• SurveyMonkey ($300/month during survey)
• Email to existing customers
• 200-300 responses
• Incentive: early access to results

3. Expert interview series (no budget)

• 10-12 expert interviews
• Compile insights into report
• Experts share → distribution

Time investment:

• Data analysis: 20 hours/quarter
• Customer survey: 40 hours/year
• Expert series: 60 hours/year

Results:

Still get AI citations. Not at enterprise levels, but meaningful.

Key insight:

You don’t need $20K budgets. You need unique data or perspectives. Small teams can find that.

Presentation matters for AI citations:

How to make data AI-accessible:

1. Clear statistics in text

   • Don’t hide data only in charts
   • State key findings as text
   • AI can’t read charts

2. Executive summary formatting

   • Bullet points with numbers
   • Each bullet independently citable
   • Lead with the most important finding

3. Structured data tables

   • HTML tables AI can parse
   • Clear headers and labels
   • Metadata in schema

Example of AI-friendly finding:

Bad: “Survey results show positive trends” Good: “78% of marketers increased AI tool usage in 2025, up from 45% in 2024 (n=1,247)”

The second is citable. The first is not.

Visual content:

Charts are great for humans but invisible to AI. Always:

• Caption charts with key data points
• Include data tables alongside visuals
• Write findings in body text

Fantastic discussion. Here’s my research report framework:

The Industry Report Blueprint:

Types by investment level:

Report structure for AI:

1. Executive summary (most citable section)
2. Key findings (bullet points with data)
3. Methodology (credibility signal)
4. Detailed findings (depth)
5. Implications (analysis)
6. Appendix (data verification)

Critical elements:

• Specific statistics in text (not just charts)
• Sample size stated
• Methodology explained
• Findings as quotable statements

ROI calculation:

Investment: $32K/year AI citations: 180/month = 2,160/year Cost per citation: $15

Compare to:

• Blog posts: ~$180/citation
• Paid media: $50-100/equivalent impression

Research is the highest-ROI content for AI visibility.

Action plan:

1. Start with what you have (your own data)
2. Add customer surveys
3. Scale to industry research
4. Track with Am I Cited

Thanks everyone for the great insights!