Beyond text: the multimodal moment
For years, production AI meant text in, text out. That changed dramatically in 2025, as multimodal models — capable of understanding images, documents, audio, and video alongside text — reached the point where they're genuinely useful for real-world applications.
But "capable" and "production-ready" are different things. This post provides an honest assessment of where each modality stands and which use cases are delivering value today.
Vision: mature for structured tasks
What works
Document understanding is the breakout use case for vision AI. Modern multimodal models can read complex documents — invoices, receipts, forms, tables, handwritten notes — with accuracy that rivals or exceeds traditional OCR pipelines. And unlike OCR, they understand the content: they can answer questions about a document, extract specific fields, and summarize the key information.
Product image analysis — categorization, quality inspection, attribute extraction from product photos — is another mature application. Retail and e-commerce teams are deploying this at scale.
Chart and diagram interpretation — understanding graphs, flowcharts, and technical diagrams — works well for common formats and enables applications like automated report analysis.
What's still unreliable
Fine-grained spatial reasoning — precisely locating objects, counting items in complex scenes, or understanding spatial relationships — remains inconsistent. Models are significantly better at describing what's in an image than where things are relative to each other.
Medical and scientific imaging — While promising in research, clinical-grade accuracy for medical imaging requires specialized models and regulatory approval that general-purpose multimodal models don't have.
Audio: ready for core use cases
What works
Transcription has reached human-level accuracy for most languages and accents, especially with recent models. Real-time transcription with speaker diarization (identifying who said what) is production-ready for meeting transcription, customer call analysis, and accessibility.
Voice-based interaction — combining speech-to-text, LLM reasoning, and text-to-speech — enables voice assistants that are genuinely conversational. Latency has improved to the point where natural back-and-forth dialogue is possible.
What's still developing
Audio understanding beyond speech — analyzing environmental sounds, music, or non-verbal audio cues — is less mature. Models can describe what they hear but struggle with nuanced analysis.
Real-time multilingual translation works but quality varies significantly by language pair. High-resource language pairs (English-Spanish, English-French) are strong; lower-resource pairs still produce noticeable errors.
Document processing: the quiet revolution
Document processing deserves special attention because it's where multimodal AI is creating the most immediate business value.
Traditional document processing pipelines — OCR → layout analysis → field extraction → validation — are being replaced by single multimodal model calls that handle the entire pipeline. This simplification reduces engineering complexity, maintenance burden, and error propagation.
Use cases in production today: invoice processing and accounts payable automation, insurance claim document analysis, legal document review and extraction, compliance document verification, and medical records digitization.
The key advantage isn't just accuracy — it's flexibility. Traditional pipelines need to be reconfigured for each document type. Multimodal models handle new document formats with prompt changes rather than engineering changes.
Integration patterns
Modality routing
For applications that accept multiple input types, implement a modality router that detects the input type and routes to the appropriate processing pipeline. This keeps each pipeline focused and testable.
Modality conversion
Sometimes the most effective approach is to convert one modality to another: transcribe audio to text, describe images as text, or extract document content as structured data — and then process the result with a text-only pipeline. This is simpler and often more reliable than processing the raw modality directly.
Multimodal context
For tasks that genuinely require understanding across modalities — like answering a question about a chart in a document while referencing a spoken presentation — pass all relevant inputs together in a single model call. Current multimodal models handle this well as long as the total context stays within reasonable bounds.
What's next
Video understanding is the next frontier. Current capabilities are limited to frame-by-frame analysis or very short clips, but progress is rapid. By late 2026, expect production-ready video understanding for use cases like security footage analysis, content moderation, and meeting summarization from video recordings.
The broader trend is clear: the boundary between "text AI" and "multimodal AI" is dissolving. Within a year or two, the expectation will be that any AI system can handle any common input modality — and the teams building for this future will have a significant head start.