The question has changed
ChatGPT’s public launch in November 2022 brought conversational AI into the mainstream. For enterprises, the question has moved beyond what a model can generate. It is increasingly about which information a system can retrieve, what that information means, and whether it is suitable for the decision at hand. A capable model with incomplete commercial context can still produce an unhelpful answer.
Start with a business decision
Consider a distributor preparing a technical quotation. Product names alone do not establish whether an item meets the customer’s requirements. A useful data product connects specifications, units, compatibility, supporting documents and the date of each observation. These details help specialists review a shortlist and help an AI assistant explain why a product belongs on it. Define the decision first, then the information and relationships needed to support it.
Make context usable
Agentic workflows need more than readable prose. Stable identifiers, explicit field types, source references, and understandable status values make information easier to retrieve and combine. A missing value should remain visibly missing. An observation date should be distinct from the date a report was downloaded. A commercial contact channel should not be labelled verified without an actual verification process.
Access is part of the data product
An AI agent should receive only the records and fields it is authorized to use. The same boundary should apply to a report, a query, and an export. This is a practical product requirement: teams need to know that a useful dataset can be shared with the right workflow without exposing another organization’s information.
Our view
Enterprise intelligence serves both the people making decisions and the systems supporting them. IronBay Data develops custom data products, connected knowledge and intelligence feeds around a defined workflow. The starting point is a concrete deliverable with documented meanings, source evidence and controlled access. Further coverage, updates and integrations should follow demonstrated value in the task.
A useful first step
Choose one research or operational question. Define the domain, required evidence, acceptable source age and intended users. Test the data product in the real workflow: does it reduce research time, improve coverage or support a more defensible answer? Use those results to decide what should be maintained and updated. The value of context is demonstrated by what the organization can do with it.