From storage to synthesis: How AI solves the multimodal content explosion

Document management systems (DMS) and enterprise content management (ECM) are undergoing a radical shift. Traditional storage-centric models are failing to keep pace with the modern data explosion. By the end of 2026, over half of all enterprises will manage more than 300 petabytes of data. Much of this is "dark data"—unstructured assets that are inexpensive to generate but prohibitively expensive to manage, clean, and curate.

Legacy DMS and ECM frameworks failed because they relied on static schemas and a "mirage" of federated content that ignored the reality of evolving business processes. This creates a massive "agility tax," where employees lose 20% of their productivity simply searching for information.

The explosion of multimodal content

Content remains the backbone of business, but its format has evolved. We are seeing a surge in media creation with approximately 474 million terabytes of data being generated daily—from recorded video conferences to massive digitization efforts. This results in a sea of multimodal content—intelligence trapped in images, audio, and video rather than standard text.

Integrating this variety is a complex technical challenge, often leaving organizations with siloed, hard-to-maintain legacy environments where extracting actual insights remains a major obstacle.

Re-engineering the information layer

Modern AI solves these challenges by transforming unstructured multimodal content into structured, usable data. This paradigm shift represents a total re-engineering of how users interact with information.

• Dynamic taxonomy: AI-driven extraction automates classification, replacing rigid taxonomies with flexible, metadata-rich frameworks.
• Context-aware extraction: AI understands diverse content styles to extract valuable information accurately without requiring a static data schema.
• Vector intelligence: By replacing rigid schemas with high-dimensional vector embeddings, applications move beyond simple text recognition to true multimodal understanding.
• Deep learning: Models can now convert asynchronous audio and video into searchable, indexed text or utilize facial recognition and object detection effectively.

The end-user experience: From search to conversation

One of the most significant transitions for the user is the move from traditional keyword search to semantic, conversational search. Previously, users were limited to sifting through folders and relying on exact word matches. Today, users can interrogate and converse with their data repository using natural language.

This enables a more intuitive, human-centric way of working. Users can search within images, audio, and video files semantically—finding specific moments in a meeting or objects in a photo as easily as a word in a PDF. Instead of clicking through file links, users query complex datasets to get precise, summarized answers. This transition from “searching for content” to “conversing with intelligence” is the new standard for the enterprise.

Transforming multimodal content into active intelligence

Iron Mountain InSight^® DXP is an intelligent content platform designed to eliminate "content chaos" by transforming passive archives into a searchable, AI-ready library. Unlike traditional systems that rely on rigid, manual tagging, InSight DXP utilizes multimodal AI processing to analyze the actual pixels of an image or the audio within a video.

This allows users to conduct semantic, natural language searches to find specific moments—such as a specific person speaking about innovation or a partner's logo appearing in a video—in seconds. By replacing legacy static schemas with high-dimensional vector embeddings, the platform moves beyond simple text recognition to true multimodal understanding. This re-engineering of the information layer transforms your data from a passive cost center into a driver of active intelligence.

Stop searching for files and start discovering moments.