Conflicting information in a dashboard may be something an employee can investigate. An AI agent facing the same ambiguity can spread the resulting error across its work, according to the article. For organizations to rely on AI-generated outcomes, data needs to be structured for machine use as well as governed and understood by people.
The article identifies four requirements for AI-ready data. It should be trusted, with provenance, ownership, validation history and transformations traceable alongside the data. Governance rules should travel with the datasets, rather than sit separately in downstream tools, where changes can go unnoticed. That traceability also helps organizations explain and take responsibility for a model’s outputs.
Data must also be timely enough for the decisions it supports. Operational uses, such as fraud detection or inventory adjustments, may require updates within seconds, while strategic planning may work with daily or weekly data. The key is to match data freshness to the decision’s timing and make any delay clear.
A shared business vocabulary is another requirement. Terms such as “revenue” can refer to different measures in different systems. Without common, documented definitions enforced at the data layer, a model may combine distinct measures as if they meant the same thing.
Finally, data needs to be discoverable and usable by machines. Analysts often rely on knowledge of which tables and fields to use, but AI agents do not automatically have that institutional context. The data’s organization and the way it is exposed therefore matter alongside its contents.
The article presents data products as one way to meet these needs. A data product is a discrete, owned and documented unit that includes information such as its schema, refresh schedule, access policy and business meaning. This gives agents a consistent, machine-readable way to find and retrieve data.
Organizations can expose data products through MCP, or Model Context Protocol, which the article describes as an emerging standard for giving AI agents structured, permissioned access to systems and data sources. With that approach, an agent could identify a “Customer Lifetime Value” data product, understand its contents and freshness, and retrieve relevant information without a person building a custom integration or hardcoding a query.
By contrast, organizations using raw, undifferentiated data lakes may need to build a navigation layer for each new use case before agents can work with the data. The article argues that companies with established data products can reduce that setup work and the need for human intervention during inference.
Sources: TechRadar.