Metadata has long helped organizations structure, locate, and manage documents. With AI, it takes on a new and more strategic significance. In a recent analysis on AI data readiness, McKinsey highlights metadata as a key component of the information foundation required for companies to scale AI*. When AI is tasked with determining which information is current, relevant, and permissible to use, the information about the information itself becomes increasingly important.
Copilot, AI assistants, and AI agents are changing the way we search for and use information. Instead of searching for a document ourselves, we can ask a question and get a ready-made answer based on information from multiple sources.
But how does AI know which information it can trust?
Most organizations already have large amounts of digital information in SharePoint, Teams, document management systems, and other business systems. For a long time, the focus has been on making that information searchable. But McKinsey notes that searchability is not the same thing as the information being usable by AI.
AI also needs to understand the context. Which version is current? Is the information still up to date? Who owns it? Is it sensitive, and how may it be used?
This is where metadata becomes important.
From Searchable Information to the Right Information
Metadata can, for example, describe a document’s type, owner, status, validity period, information class, or connection to a specific contract, project, or customer. Traditionally, this information has helped us humans find, sort, and manage documents.
In an AI-driven organization, metadata takes on an additional function. It can help AI systems understand what the information is, the context in which it belongs, and how it may be used. McKinsey describes this as metadata needing to evolve into a kind of control layer for unstructured information.
Consider an organization that has five versions of the same policy. Four of them are out of date, but all of them remain in the system and are searchable. For an employee, this is a source of frustration. For an AI that needs to use this information to answer a question, it can be significantly more problematic.
Furthermore, AI systems do not necessarily use documents in their entirety. The content can be broken down into smaller parts and combined with information from other sources. A document may therefore be accurate as a whole, while the AI provides a misleading answer because it has retrieved an outdated section or lacks important context.
Old information problems have new consequences
Better AI models do not automatically solve poor information management. Old documents, duplicates, unclear information ownership, and incorrect access permissions remain even after an organization has implemented Copilot or other AI services.
As AI becomes capable of searching and processing significantly larger amounts of information, the implications become increasingly clear. It is no longer enough to know who is authorized to open a document. The organization also needs to be able to control what information the AI is allowed to use and in what context.
This becomes even more important with AI agents. An AI assistant that uses an outdated policy may provide an incorrect answer. An AI agent that relies on the same outdated policy may, in the next step, use that information to perform a task or initiate an action.
The more responsibility we give to AI, the greater the demands placed on the information the systems work with.
Metadata is becoming part of the AI infrastructure
Organizing documents has long been about efficiency, information security, and compliance. AI adds another perspective.
A well-structured information environment is essential for AI to provide reliable answers and be integrated into business processes. Version control, information ownership, classification, lifecycle management, and metadata are therefore no longer just issues for traditional document management. They become part of the effort to make the organization AI-ready.
This is also one of the clear conclusions of McKinsey’s analysis. According to the article, only 7 percent of companies have fully scaled AI within their organizations, while more than two-thirds of high-performing companies cite data as the primary obstacle to implementing AI.
For organizations that want to get a return on their AI investments, there is therefore good reason to start with the data.
Because in the age of AI, metadata doesn’t just help us find the right document. It also helps AI understand which information is appropriate to use.
Source: *McKinsey & Company, ” AI Data Readiness: The Key to Scaling Impact,” June 23, 2026.
Prepare the information for AI
MetaShare helps organizations bring structure and control to their document management using metadata. By describing documents based on factors such as content, context, status, and responsibility, information becomes easier to find and manage—for both people and AI.
As AI plays an increasingly important role in business operations, this structure becomes even more critical. With the right metadata and information governance, MetaShare creates a better foundation for Copilot and other AI services to find relevant information and use it in the right context.