Data Management Glossary
Multi-Modal AI
What Is Multimodal AI?
Multimodal AI refers to artificial intelligence systems that process and reason across multiple types of data, including text, images, audio, video, and structured formats, within a single model or integrated pipeline. Unlike earlier AI systems built to handle one data type at a time, multimodal models accept inputs from different modalities and produce outputs that draw on all of them together.
The term “modality” describes a type or format of data. Text is one modality. Images are another. Audio, video, sensor readings, genomic sequences, and medical scans are others. A multimodal AI model can accept a chest X-ray and a radiology report in the same query and reason across both. It can analyze a legal contract alongside its metadata. It can interpret a manufacturing sensor log in combination with an image of the equipment it describes.
Multimodal AI is not a single architecture. It encompasses a range of approaches: models with separate encoders for each modality that share a common reasoning layer, models trained jointly on mixed-modality datasets from the outset, and pipelines that route different data types through specialized processing before synthesis. What they share is the ability to work across data types in ways that single-modality models cannot.
Why Multimodal AI Is Growing Rapidly
Text-only AI has fundamental limits in enterprise environments. Most of the knowledge and operational data that organizations hold is not text. It is in images, scans, recordings, instrument outputs, and file formats that carry rich information in their headers and binary content. A model that can only process text cannot reason about a DICOM medical image, a FASTQ genomics file, an EXIF-tagged photograph, or an LAS well log. Building AI systems that can work across these formats is the defining infrastructure challenge of enterprise AI in 2026.
Gartner predicts that by 2029, AI agents will generate 10 times more data from physical environments than from all digital AI applications combined. Physical AI, including robotics, sensors, imaging systems, and instruments, produces multimodal data by definition. Managing that data at enterprise scale, and making it available to the models that consume it, requires unstructured data infrastructure built for multiple modalities.
The Komprise 2026 State of Unstructured Data Management report, based on a survey of 300 enterprise IT directors, VPs, and C-level executives, found that 74% of enterprise IT leaders are now managing more than 5 petabytes of unstructured data, a 57% increase over 2024. The majority of that data is not text. It is images, video, instrument output, and domain-specific file formats that require multimodal AI to extract value from.
Source: Gartner, “Top Predictions for Data and Analytics in 2026,” March 11, 2026.
Multimodal AI Runs on Unstructured Data
Every modality that multimodal AI processes is a form of unstructured data. DICOM files carry medical imaging data alongside embedded clinical headers. FASTQ and BAM files carry genomic sequence data with quality scores and sample identifiers. Image files carry visual content alongside EXIF, XMP, and IPTC metadata. LAS files carry well log measurements with structured header fields. Video files carry temporal visual and audio data. None of these are indexed by standard file storage systems. The storage layer sees filenames and timestamps – system metadata, The rich content and embedded metadata that makes these files useful to a multimodal model is invisible without targeted extraction.
This is the core infrastructure challenge for enterprise multimodal AI. The files that feed these models are distributed across NAS environments, object stores, and cloud buckets, accumulated over years without consistent classification, enrichment, or governance. Before a multimodal model can reason across them, someone has to find them, identify which ones are relevant to the task, extract the embedded metadata that makes them queryable, remove duplicates and outdated versions, and check them for sensitive content that should not enter an AI pipeline.
That is not a model problem. It is a data management problem.
IDC research found that only half of an organization’s unstructured data is analyzed to extract value from it, and that 22% is unnecessarily replicated because organizations do not know what they have. The Komprise 2025 AI Survey, which surveyed 200 IT directors and executives at U.S. enterprises with 1,000 or more employees, found that 54% cite finding and moving the right data to AI ingestion locations as their greatest challenge in preparing unstructured data for AI. For multimodal AI specifically, that challenge is compounded by the diversity of file formats involved. Finding the right chest X-rays for a cancer research model across a multi-hospital imaging archive is a different problem than finding the right well logs for a geoscience model. Both require unstructured data management that operates at file level, across storage silos, without requiring a full data migration.
Why Unstructured Data Management Is a Prerequisite for Multimodal AI
Multimodal AI models are only as good as the data they receive. Gartner predicted in February 2025 that through 2026, organizations will abandon 60% of AI projects unsupported by AI-ready data, and found that 63% of organizations either do not have or are unsure if they have the right data management practices for AI. Both findings apply with particular force to multimodal projects, where data readiness requires not just volume but modality-specific preparation.
Preparing unstructured data for multimodal AI involves four distinct requirements that standard storage infrastructure does not provide.
- The first is cross-silo discovery. Multimodal training datasets are typically assembled from data that lives across multiple storage systems, acquired at different times, stored in different formats, and managed by different teams. Without a unified metadata index that spans all of those silos, there is no way to identify which files are relevant to a given model, project, or research objective. You cannot curate what you cannot find.
- The second is modality-specific metadata extraction. Each file format carries embedded metadata that the storage layer cannot see. A DICOM file header contains the patient ID, study type, scanner model, imaging protocol, and body part examined. A FASTQ file header contains the sequencer model, sample ID, run date, and quality metrics. An EXIF image header contains the camera model, shoot date, GPS coordinates, and rights metadata. None of that information is available for search or filtering until it is explicitly extracted and indexed. Standard storage metadata is insufficient to curate a multimodal training dataset with any precision.
- The third is sensitive data governance. Multimodal datasets frequently contain protected health information, personally identifiable information, and proprietary content. A medical imaging archive used for multimodal AI training contains patient data. A genomics dataset contains genetic information. A corporate document corpus contains contracts, financial records, and IP. The Komprise 2025 AI Survey found that nearly 80% of organizations have already experienced negative data incidents with generative AI, and that 44% have experienced leaking of sensitive data into AI tools. Sending multimodal data into an AI pipeline without first screening it for sensitive content is the most direct path to the AI data governance failures Gartner warns will cause financial and reputational loss in the near term.
- The fourth is curation at scale. A multimodal training dataset is not the entire data estate. It is a precisely defined subset: the right modalities, the right time ranges, the right quality thresholds, with sensitive content removed and duplicates eliminated. Identifying and assembling that subset across petabytes of distributed unstructured data, without moving everything first, requires automated classification, filtering, and workflow capabilities that operate at file level across all storage environments.
Source: Gartner, “Lack of AI-Ready Data Puts AI Projects at Risk,” February 26, 2025.
Source: Komprise 2025 AI Survey: AI, Data and Enterprise Risk.
How Komprise Prepares Unstructured Data for Multimodal AI
Komprise addresses all four requirements for multimodal AI data readiness across existing storage infrastructure, without requiring data migration as a prerequisite.
Cross-silo discovery starts with the Komprise Global Metadatabase, the centralized metadata index that Komprise continuously builds across every NAS, object storage, and cloud environment an organization connects to it. The Global Metadatabase indexes standard file system metadata across the entire data estate, making every file discoverable from a single query interface regardless of where it lives. Deep Analytics queries the Global Metadatabase to filter and curate data by file type, owner, age, location, and Komprise tags. IT teams can reduce a petabyte-scale data estate to a specific, relevant subset in minutes, without touching the underlying storage.
Modality-specific metadata extraction is handled by KAPPA data services (Komprise AI Preparation and Process Automation). KAPPA executes serverless Python functions against file headers at scale, extracting the embedded metadata that file systems cannot see. For DICOM files, KAPPA extracts clinical header fields including patient ID, study type, body part examined, and scanner model. For FASTQ and BAM files, KAPPA extracts sequencer metadata, sample IDs, project codes, and quality scores. For EXIF-tagged image files, KAPPA extracts camera model, shoot date, rights metadata, and GPS data. For LAS well log files, KAPPA extracts OSDU-standard subsurface fields. Every extracted attribute loads directly into the Global Metadatabase, where it becomes immediately available for Deep Analytics queries. The result is a searchable, modality-aware metadata index across the full data estate, built without moving a single file.
Sensitive data governance is handled by Smart Data Workflows. Smart Data Workflows process file content directly, scanning for PII and PHI using 68 built-in content scanners, searching for custom patterns using regular expressions, and flagging files that should not enter an AI pipeline. When sensitive content is found, Smart Data Workflows tag the file and confine it to a protected admin area, preserving the original folder structure so compliance and legal teams can review it. This step runs against the curated dataset that Deep Analytics has already identified, which means content scanning operates on a precisely filtered subset rather than the full data estate. That efficiency matters at petabyte scale.
Curation and delivery are completed by the Intelligent AI Ingest capability, which makes a high-speed copy of the curated, enriched, governed dataset to the target AI environment, whether that is an S3 bucket, a cloud AI platform, or an on-premises GPU cluster. Only the files that passed classification and governance screening move. The multimodal model receives a dataset built from exactly the right files, with exactly the metadata it needs, with sensitive content removed before transfer.
The Komprise 2026 survey found that future requirements for unstructured data management are led by data classification and tagging (61%), analytics and reporting (60%), and sensitive data detection (57%). For organizations building multimodal AI pipelines, all three are preconditions, not optional additions.
Multimodal AI Use Cases That Depend on Unstructured Data Management
Healthcare and digital pathology. Multimodal AI models for cancer detection, radiology interpretation, and digital pathology require curated DICOM archives with clinical header metadata intact. Identifying the right imaging studies across a multi-hospital network, filtering by modality, body part, and study type, and removing patient identifiers before AI ingestion is a direct KAPPA and Smart Data Workflow use case.
Genomics and life sciences. Multimodal models that combine genomic sequence data with clinical or phenotypic data require FASTQ and BAM files curated by project, sample ID, and quality score. Research teams working against grant deadlines cannot afford months of manual file inventory across petabytes of sequencing archives.
Oil and gas and subsurface science. Multimodal geoscience models that combine well log data, seismic imagery, and reservoir simulation outputs require LAS files enriched with OSDU-standard metadata for cross-vendor discovery. Without metadata extraction at scale, subsurface data remains siloed by platform and invisible to cross-domain AI workflows.
Media and entertainment. Multimodal models for content tagging, rights management, and visual search require image and video archives enriched with EXIF, XMP, and IPTC metadata. Post-production workflows strip embedded metadata from digital assets, severing context from content at the point where multimodal AI needs it most.
Frequently Asked Questions
What is multimodal AI?
Multimodal AI refers to artificial intelligence systems that process and reason across multiple types of data, including text, images, audio, video, and domain-specific file formats, within a single model or integrated pipeline. Multimodal models can accept inputs from different data types and produce outputs that draw on all of them together.
What types of data does multimodal AI use?
Multimodal AI uses any combination of text, images, audio, video, and structured or domain-specific file formats. In enterprise environments, the most common multimodal inputs include medical imaging files (DICOM), genomics files (FASTQ, BAM), geoscience well logs (LAS), image files with embedded metadata (EXIF, XMP, IPTC), and video with associated transcripts or sensor data.
Why is unstructured data management critical for multimodal AI?
Every modality that multimodal AI processes is a form of unstructured data that standard storage infrastructure cannot index or classify. The embedded metadata that makes these files useful to a model, including clinical headers in DICOM files, quality scores in FASTQ files, and subsurface fields in LAS files, is invisible to the storage layer without targeted extraction. Gartner predicted that through 2026, organizations will abandon 60% of AI projects unsupported by AI-ready data. For multimodal AI, AI readiness means modality-specific metadata extraction, cross-silo discovery, sensitive data governance, and curated delivery to the model.
Source: Gartner, “Lack of AI-Ready Data Puts AI Projects at Risk,” February 26, 2025.
What is the difference between multimodal AI and large language models?
Large language models (LLMs) are trained on and optimized for text. Multimodal AI models extend that capability to other data types including images, audio, video, and domain-specific formats. In enterprise environments, the distinction matters because most of the high-value data organizations hold is not text. Medical images, genomics files, and geoscience data require multimodal models to extract value from.
How does Komprise support multimodal AI?
Komprise supports multimodal AI through four integrated capabilities. The Global Metadatabase provides cross-silo discovery of all unstructured data across NAS, object storage, and cloud. KAPPA data services extract modality-specific embedded metadata from file headers at scale, including DICOM, FASTQ, EXIF, and LAS formats, loading results into the Global Metadatabase. Smart Data Workflows scan file content for PII and sensitive data, tagging and confining files that should not enter an AI pipeline. The Intelligent AI Ingest capability delivers the curated, enriched, governed dataset to the target AI environment at high speed. The multimodal model receives only the right files, with the right metadata, with sensitive content removed.
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What file formats are relevant to multimodal AI in the enterprise?
The most common enterprise multimodal file formats include DICOM (medical imaging), FASTQ and BAM (genomics), LAS (oil and gas well logs), EXIF/XMP/IPTC (digital media), ELN (electronic lab notebooks), ESIF (energy research data), and standard document, video, and audio formats. Each carries embedded metadata that standard storage systems cannot index, requiring modality-specific extraction before the files are usable by a multimodal AI model.
How much enterprise data is multimodal?
IDC research found that in 2022, 90% of the data generated by organizations was unstructured, and only 10% was structured. The vast majority of that unstructured data is inherently multimodal: images, video, audio, instrument output, and domain-specific file formats that are not text. Gartner predicts that by 2029, AI agents in physical environments will generate 10 times more data than all digital AI applications combined, the majority of which will be multimodal sensor, imaging, and spatial data.