Posted on behalf of CIO Association of Canada’s sponsor, Oproma.
Organizations consistently pay approximately five times more than necessary for data storage. Not because hardware prices remain high, but because most enterprises have become digital hoarders—accumulating vast quantities of Redundant, Obsolete, and Trivial information (ROT data) across disparate systems. Research consistently shows that 80-85% of organizational content delivers zero business value yet consumes resources, creates liability, and undermines efficiency. This is one corporate symptom that AI will exacerbate because it generates mountains of new content without concern for where or how you manage it.
Do nothing or ignore it, either strategy here is going to explode your costs, deepen data management issues and cause significant governance concerns as you bring more AI and advanced content solutions onto your systems.
Recent excitement around large language models has created a mistaken impression that AI alone can solve the ROT data crisis. This perspective misses a crucial insight: like humans, AI systems achieve their greatest impact not through raw intelligence but by using advanced tools. Just as a human accountant leverages spreadsheets to manage complex financial data, AI systems need specialized tools to effectively govern information at enterprise scale.
The emergence of tool-enabled, agentic AI systems transforms information governance from an insurmountable challenge into a tractable problem. These systems don’t merely understand content—they interact with existing enterprise applications, orchestrate complex workflows, and execute remedial actions autonomously.
The Crucial Distinction: LLMs vs. Tool-Enabled AI
Language Models: Cerebral Cortices in Jars
Large language models demonstrate remarkable abilities to understand and generate text, classify content, and reason about information. However, language models operating in isolation resemble cerebral cortices disconnected from bodies—capable of impressive thought but unable to effectively manipulate their environment.
This limitation manifests clearly in information governance scenarios. A standalone LLM might correctly identify redundant documents but cannot access repository APIs to consolidate them. It might recognize outdated policies but lacks mechanisms to update references across systems. It might detect trivial content but cannot implement retention actions.
In essence, LLMs without tool access can diagnose problems but remain powerless to solve them—an insufficient approach for organizations drowning in digital debris. We need a better way.
Tool-Enabled Systems: From Intelligence to Agency
The transformative leap occurs when AI systems gain the ability to use tools—to extend their capabilities beyond understanding into action. This tool use mirrors a fundamental aspect of human intelligence that separates us from other cognitive beings: our ability to create and manipulate external technologies that augment our native capabilities.
Modern agentic AI systems achieve this through structured protocols that enable API-based interactions with enterprise systems. These systems can:
1. Access information across disparate repositories through standardized connectors
2. Analyze content using specialized tools beyond their native capabilities.
3. Execute actions through existing application interfaces.
4. Document processes in auditable, transparent formats.
This capability turns theoretical governance into practical action—the difference between identifying a warehouse full of unnecessary content and data, versus actually cleaning it out as part of your corporate standard for MetaData management.
What is a MetaData Supply Chain
A MetaData Supply Chain is the standardization of tools and methodology needed to accelerate and manage the free flow of rich high MetaData across high value enterprise programs. Using tool enabled AI your MetaData Supply Chain becomes a pipeline across your systems automatically generating important activities like:
1. Enrichment: All data, all systems, all generation points. Mandatory metadata.
2. Clean up and ROT.
3. Migrations and movement
4. Authoring: date, time, location, version, ownership, and approvers
5. Residency: Approved locations, Appropriate Usage and Access
6. Governance: Certifications, legal confirmations,
The unglamorous part of the future of AI is ensuring you have Clean Data. Building a Metadata Supply Chain and corresponding sanitation pipeline is the best way to prepare yourself for the volumes of data and content we all understand is growing around us. Talk to Oproma at info@oproma.com for more information on how to begin the journey towards MetaData Supply Chain.