How important is multi-modal content for AI search? Video, images, audio - do they matter?

The multi-modal AI landscape is evolving fast. Here’s the current state.

Current multi-modal capabilities:

Google AI (most advanced):

• Google Lens: 1.5 billion monthly users
• AI Overviews: Includes video results
• Image understanding: Extracting info from images
• Video comprehension: Understanding video content

ChatGPT/OpenAI:

• Image input and analysis
• Video understanding developing
• DALL-E image generation

Perplexity:

• Image search integration
• Video results in responses
• Visual answer formatting

What this means for content:

Images (important now):

• AI can understand image content
• Can cite images in responses
• Visual search is growing
• Image context affects understanding

Video (increasingly critical):

• YouTube dominates video search
• AI Overviews feature video
• Transcripts provide text citations
• Video demonstrates credibility

Audio/Podcasts (emerging):

• Transcripts get indexed
• Direct audio understanding developing
• Less immediate impact than video

The investment priority:

1. Video (highest ROI for multi-modal)
2. Images (essential, relatively easy)
3. Audio (nice to have, growing)

Video optimization specifics.

YouTube optimization for AI:

Titles:

• Question-based when relevant
• Clear topic indication
• Brand inclusion

Descriptions:

• Comprehensive summary (300+ words)
• Key points covered
• Timestamps for sections
• Links to related content

Transcripts/Captions:

• Always include
• Manually review for accuracy
• AI reads these as text content

Tags and categories:

• Relevant topic tags
• Proper category selection
• Related keywords

The AI-specific considerations:

Chapters/timestamps: AI can reference specific segments. Use chapters:

0:00 Introduction
2:15 What is GEO?
5:30 How to implement GEO

Clear speech: AI transcription accuracy depends on audio quality.

Visual text: Key points shown on screen can be extracted by AI.

The content approach:

Structure videos like written content:

• Clear question as topic
• Direct answer early
• Depth and examples
• Summary/key points

AI can then cite your video content just like articles.

Image optimization for AI visibility.

Current state:

AI systems increasingly understand images:

• Can describe image content
• Can extract text from images
• Can analyze diagrams/charts
• Can match images to queries

Image optimization:

File names:

❌ IMG_12345.jpg
✓ crm-software-comparison-chart.png

Alt text:

❌ alt="image"
✓ alt="Comparison chart showing CRM software features across Salesforce, HubSpot, and Pipedrive"

Captions:

• Add captions explaining image context
• Helps AI understand relevance

Schema markup:

{
  "@type": "ImageObject",
  "contentUrl": "https://...",
  "description": "...",
  "caption": "..."
}

Quality:

• High resolution originals
• Relevant to content
• Informative, not decorative

The visual search consideration:

Google Lens and visual search are growing. Images optimized for this:

• Can be discovered through image search
• Can be cited in visual AI responses
• Connect users to your content

Priority:

Optimize existing images first. Higher ROI than new production in most cases.

Audio/podcast perspective.

Current AI audio handling:

• Transcripts are key - AI reads text
• Direct audio understanding is emerging
• Podcast visibility in traditional search
• Emerging AI audio features

Podcast optimization:

Transcripts (essential):

• Full episode transcripts
• Published on website
• Optimized for search

Show notes:

• Detailed episode summaries
• Key points and timestamps
• Links to resources

Platform distribution:

• Apple, Spotify, etc. for audience
• YouTube (with video) for search
• Website for SEO/GEO

The AI visibility path:

Podcast → Transcript → Website → AI citation

The content is valuable, but AI accesses it through text currently.

Future potential:

Audio understanding is improving. Podcasts may eventually be directly cited. But transcripts remain the bridge for now.

ROI assessment:

If you’re already podcasting, optimize transcripts. If not, video likely higher priority for AI visibility specifically.

Data on multi-modal content performance.

What we’re seeing in AI responses:

Current citation mix:

• Text content: ~70% of citations
• Video content: ~20% of citations
• Image citations: ~10% of citations

Trend direction:

• Video increasing rapidly
• Image increasing steadily
• Text still dominant but declining share

Query types by modality:

Video citations highest for:

• How-to queries (demonstrations)
• Product reviews (visual proof)
• Tutorial content (step-by-step)

Image citations highest for:

• Comparison queries (charts)
• Product queries (visuals)
• Location queries (photos)

Text still dominant for:

• Definitional queries
• Analysis/opinion
• Complex topics

The strategic implication:

Match modality to query type:

• How-to content → Video essential
• Comparison content → Images/charts essential
• Educational → Text primary, video supplemental

Monitoring:

Track citation by modality in Am I Cited. Understand which content types drive your visibility.

Practical multi-modal strategy.

The integrated approach:

For key topics, create all modalities:

Text (foundation):

• Comprehensive article
• AI-optimized structure
• Expert authority

Video (amplification):

• YouTube video covering same topic
• Links to article
• Transcript adds text content

Images (enhancement):

• Custom diagrams/charts
• Process visuals
• Comparison graphics

The production efficiency:

Create content once, adapt for modalities:

1. Write comprehensive article
2. Record video covering key points
3. Create visuals from content
4. Cross-link everything

One research effort, multiple content assets.

Resource allocation:

If budget is limited:

Phase 1: Optimize existing images (low effort) Phase 2: Add video for top 5 topics (medium effort) Phase 3: Build systematic video production (high effort)

Start where you are, build capacity over time.

Great guidance on multi-modal prioritization.

My action plan:

Immediate (this month):

• Audit and optimize existing images
• Add alt text and schema to all images
• Improve image file naming

Short-term (next quarter):

• Identify top 5 topics for video
• Start YouTube channel if needed
• Create initial videos with transcripts

Medium-term (6 months):

• Systematic video for key topics
• Integrate video into content process
• Monitor multi-modal citations

Budget justification:

Video investment justified by:

• 20% of AI citations are video
• Video share growing
• YouTube as discovery channel
• Transcript provides text visibility
• Demonstrates expertise

Measurement: Track modality mix in AI citations with Am I Cited.

Thanks for the comprehensive multi-modal perspective!