When is process data ready for AI agents?

Agent Readiness describes whether an organization’s process data is structured such that AI agents can work on it reliably. This includes unambiguous identifiers, dependable timestamps, clearly named activities, modeled object relationships, and a documented semantic model. If any of these is missing, an agent will still produce answers – just not dependable ones.

Why does this matter?

Most failed AI initiatives fail not on the model but on the data foundation. Assessing Agent Readiness early prevents effort flowing into agents whose statements nobody can later stand behind.

What does this look like in practice?

An agent is to prepare order approvals. For that it needs to know which field carries the amount, that "freigegeben" and "approved" mean the same thing, and who is responsible above which sum. Without that context it can reach the database but cannot decide reliably.

Not to be confused with

Not to be confused with data quality on its own: complete, clean data is necessary but not sufficient. Agent readiness additionally requires semantics, ownership and rules in machine-readable form.