Most enterprise AI discussions focus on text: chatbots, document processing, search. But the frontier has moved to multimodal AI – systems that work with images, audio, video, and text together. What does this mean for enterprise applications?

What Multimodal AI Actually Means

Multimodal AI systems process multiple types of input (text, images, audio, video) and can generate outputs across modalities. GPT-4o, Claude 3, Gemini, and similar models have native multimodal capabilities.

This isn’t new technology – computer vision and speech recognition have existed for years. What’s new is the integration: single models that handle multiple modalities coherently, understanding relationships between image and text, audio and video.

Enterprise Applications That Actually Work

Based on implementations I’ve seen in 2025, several multimodal applications deliver genuine value:

Document Processing

Processing documents that combine text, images, tables, and charts. Traditional OCR struggles with complex documents; multimodal AI handles them better.

Use cases: Invoice processing with line item images, insurance claims with photos, technical documentation with diagrams.

Value delivered: Reduced manual processing, fewer errors, faster throughput.

Maturity: Production-ready for many document types.

Visual Quality Control

Manufacturing inspection using AI vision, often combined with sensor data and process parameters.

Use cases: Defect detection, assembly verification, packaging inspection.

Value delivered: Consistent inspection quality, reduced manual labour, faster throughput.

Maturity: Well-established in manufacturing. Newer applications expanding.

Meeting Intelligence

Processing meeting recordings – video, audio, and screen shares – to produce summaries, action items, and searchable archives.

Use cases: Meeting summarisation, action item extraction, participant analysis, compliance recording.

Value delivered: Reduced note-taking burden, better follow-through on actions, searchable institutional memory.

Maturity: Multiple commercial products available. Adoption growing.

Customer Service Triage

Handling customer enquiries that include images – product photos, screenshots, damage documentation.

Use cases: Visual product identification, damage assessment, troubleshooting support.

Value delivered: Faster resolution when visual context matters, reduced back-and-forth.

Maturity: Early production deployments, growing rapidly.

Field Operations

Mobile workers capturing photos and video that feed into AI systems for analysis and documentation.

Use cases: Site inspections, equipment assessment, compliance documentation.

Value delivered: Structured data from field observations, consistent documentation.

Maturity: Growing adoption in asset-intensive industries.

Applications With Promise But Limited Deployment

Some multimodal applications show potential but aren’t yet widely deployed:

Video Analytics at Scale

Analysing large video libraries for content, compliance, or operational insights.

Challenge: Compute costs remain high. Processing hours of video is expensive.

Where it works: High-value applications like security, compliance, content moderation.

Timeline: Cost reductions needed for broad enterprise deployment.

Real-Time Visual Guidance

AI providing real-time guidance based on what workers see – assembly instructions, repair procedures, safety alerts.

Challenge: Latency requirements, integration with AR/VR hardware, workflow disruption.

Where it works: Controlled environments with high-value, complex tasks.

Timeline: Niche applications now, broader deployment 2-3 years out.

Multimodal Search Across Content

Searching enterprise content libraries using natural language queries that span documents, images, videos.

Challenge: Indexing costs, content diversity, result ranking.

Where it works: Organisations with valuable visual content libraries.

Timeline: Commercial solutions emerging, enterprise-grade deployment growing.

Implementation Considerations

Multimodal AI adds complexity beyond text-only applications:

Data Volume

Images and video are much larger than text. Storage, processing, and transmission costs scale accordingly. Budget for infrastructure that can handle multimedia at scale.

Privacy and Consent

Visual and audio data often captures people. This triggers privacy considerations that text may not. Ensure clear consent frameworks and compliance with privacy regulations.

Processing Costs

Multimodal inference is more expensive than text-only. A single image analysis call might cost 10-50x a text query. Model this carefully for high-volume applications.

Integration Complexity

Existing enterprise systems often weren’t designed for multimedia. Integration with document management, CRM, and operational systems requires more work than text-based AI.

Quality Variation

Image and audio quality varies widely. Models trained on clean data may struggle with real-world captures. Test with realistic input quality.

Vendor Landscape

The multimodal AI vendor landscape includes:

Foundation model providers: OpenAI, Anthropic, Google, Microsoft – offering multimodal APIs as platform capabilities.

Specialised vendors: Companies focused on specific multimodal applications (document processing, video analytics, visual inspection).

Enterprise platforms: Salesforce, ServiceNow, SAP – embedding multimodal capabilities into enterprise applications.

For most enterprises, consuming multimodal AI through APIs or embedded in applications makes more sense than building custom capabilities. The technology is complex; leverage vendors who’ve solved the hard problems.

Building a Multimodal Strategy

If you’re considering multimodal AI:

Start with high-value, contained use cases. Document processing and meeting intelligence are accessible starting points with proven value.

Understand the cost model. Multimodal processing is expensive. Build realistic cost projections before committing.

Address privacy early. Visual and audio data require careful handling. Build privacy frameworks before collecting data.

Plan for integration. Multimodal AI is most valuable when integrated with business processes. Budget for integration work.

Accept longer timelines. Multimodal implementations typically take longer than text-only. Plan accordingly.

Final Thought

Multimodal AI represents genuine capability expansion for enterprises. The ability to process visual, audio, and textual information together opens applications that text-only AI can’t address.

But multimodal doesn’t mean “automatically better.” These systems are more complex, more expensive, and require more careful implementation. Choose multimodal applications where the additional capability delivers clear value, not because the technology seems more advanced.

The best multimodal AI applications solve problems that couldn’t be solved without visual or audio input. Start there.