Erzielt jemand tatsächlich Ergebnisse mit Pressemitteilungen für AI-Sichtbarkeit? Teile deine Daten

Been studying this extensively. Here’s what the data actually shows:

From PR Newswire’s 2025 report:

• 57% of companies report increased brand visibility from press releases
• 91% reuse press release content across other channels
• 93% plan to send same or more releases in 2026

The AI mechanics:

Press releases work on AI in TWO ways:

1. Training data - Historical releases help AI “know” your company
2. Real-time retrieval - Current releases can be cited directly

The second one is where Perplexity shines. Their RAG system pulls fresh content.

What actually drives AI citations:

• Topical relevance > publication authority
• Recency matters (especially for Perplexity)
• Specific, factual claims get cited
• Generic announcements get ignored

Your competitor probably has better topical alignment with what people are asking AI.

Small company perspective here. We can’t compete with enterprise PR budgets.

What’s working for us:

We focus on topical relevance over volume. Instead of broad distribution, we:

• Target industry-specific publications
• Write releases that answer specific questions in our space
• Time releases around trending topics

Real example:

Generic release about our product update: 0 AI citations Release specifically about “how AI companies can reduce hallucinations”: 12 AI citations

The second one aligned with what people were actually asking AI about.

Cost comparison:

• Traditional approach: $500/release, mass distribution
• Our approach: $200/release, targeted distribution
• AI citations: 5x higher with targeted approach

I run content marketing for a SaaS company. We’ve completely rethought press releases.

The old model: Press Release → Media Pickup → Brand Awareness

The new model: Press Release → AI Training/Retrieval → AI Mentions → Brand Discovery

Our content matrix:

The insight:

Releases that TEACH something get cited more than releases that ANNOUNCE something. AI prioritizes information that answers questions.

Agency perspective - we’re changing how we measure success for clients.

Old KPIs:

• Media placements
• Impressions
• Share of voice

New KPIs:

• AI platform mentions
• Citation rate across ChatGPT, Perplexity, Google AI
• Brand sentiment in AI responses

We use Am I Cited to track all of this. Without monitoring, you’re just guessing.

Client case study:

Before: 50 releases/year, high traditional pickup, zero AI visibility After: 24 releases/year, strategic timing, 40% AI citation rate

The quality and relevance beat quantity every time.

Something nobody’s mentioning: platform differences matter a lot.

ChatGPT:

• Uses training data
• Your old releases contribute to its “knowledge”
• Real-time browsing capability is limited

Perplexity:

• Real-time retrieval focused
• Cites sources explicitly
• Fresh releases can appear immediately

Google AI Overviews:

• Integrates with existing search index
• SEO fundamentals still matter
• Traditional authority signals apply

Claude:

• Training data focused
• Good at synthesizing complex information
• Prefers clear, natural language

Your distribution strategy should account for these differences. We vary formatting slightly for each platform’s preferences.

Let me drop some hard numbers from our analysis of 200+ press releases:

What correlates with AI citations:

• Specific statistics: +45% citation rate
• Expert quotes with credentials: +38%
• First paragraph answers a question: +52%
• Industry jargon heavy: -30%

What doesn’t matter:

• Wire service (PR Newswire vs BusinessWire vs GlobeNewswire)
• Day of week
• Release length (within reason)

The counterintuitive finding:

Releases picked up by fewer traditional outlets sometimes get MORE AI citations. Why? Less noise, clearer signal for AI systems.

The best strategy: Create press releases optimized for AI consumption, not just journalist appeal.

Enterprise perspective here. We issue 100+ releases per year.

What we’ve changed:

1. AI summary paragraph - Every release now has a first paragraph that could be used verbatim by AI
2. FAQ sections - Added to releases, great for AI extraction
3. Structured data - Schema markup on our newsroom pages
4. Multi-channel amplification - Same content across social, blog, email

The measurement challenge:

Traditional PR measurement is broken for AI. You can’t:

• Track clicks from AI responses
• See impressions in AI
• Measure sentiment without tools

We implemented Am I Cited 6 months ago. Now we actually know which releases are working in AI.

ROI finding: Releases that get cited in AI responses drive 3x more branded search queries within 30 days.

This thread delivered. Here’s my synthesis:

The press release playbook for AI visibility:

1. Format for AI first

   • Lead with key info in first 50 words
   • Include specific, citable facts
   • Use natural language, avoid jargon

2. Focus on relevance over reach

   • Topical alignment matters more than volume
   • Answer questions people actually ask
   • Educational content > announcement content

3. Platform-specific thinking

   • Perplexity: real-time, needs freshness
   • ChatGPT: training data, consistency over time
   • Google AI: traditional SEO signals still matter

4. Measure what matters

   • Track AI citations, not just placements
   • Use monitoring tools (Am I Cited mentioned multiple times)
   • Branded search lift is a proxy for AI impact

Our next steps:

• Restructure release format with AI optimization
• Reduce volume, increase topical relevance
• Implement proper AI visibility tracking

Thanks everyone for the real data. This is exactly what PR professionals need to hear.