The examples below illustrate workflow design questions. Product scope and integrations are assessed for each use case.
Start with the type of work
An enterprise workflow can contain several different tasks: finding information, interpreting a document, calculating a result and approving an action. Treating all of them as one undifferentiated AI task makes review harder.
For a first design, label each step by its purpose. Define the inputs, expected output and responsible reviewer. This exposes where an explicit calculation is appropriate and where the task instead calls for interpretation or a request for clarification.
Give explicit rules a visible boundary
Suppose a document describes a requested change and a configured rule determines whether the request is complete. An AI-assisted step might propose a structured summary. A separate check can then evaluate the supplied fields against the specified conditions.
Repeatability alone does not prove that a rule is correct. Rule ownership, versioning and test cases still matter. Missing inputs should produce a visible incomplete state, not be filled with invented values to make a check pass.
Evaluate the connections between steps
A workflow can fail even when each component looks plausible on its own. A summary may omit a qualification needed by the next calculation, or a result may reach someone without the authority to approve it.
Use a small evaluation set covering ordinary cases, ambiguous language, missing evidence and conflicting inputs. Inspect what each stage passes on. Make the distinction between source facts, proposed interpretations and reviewed decisions visible in the interface.
Define an initial operating boundary
Document what the pilot can read, what it can propose and what it can change. Start with permissions appropriate to the chosen use case and a clear escalation path. Any move from recommendation to execution should be an explicit design decision.
The GPIRL platform approach brings knowledge, orchestration, deterministic rules and enterprise controls together. A useful starting point is a workflow map showing exactly where each capability is needed, rather than a broad promise to automate an entire process.
A small example to test the boundary
Imagine an internal request that needs three pieces of information before a reviewer can approve it. An AI-assisted extraction step proposes those fields from a document. An explicit check reports whether each required field is present. A human reviewer can then inspect the source and decide what to do. Neither a fluent summary nor a passed completeness check proves that the request itself is appropriate.
Test documents with a missing field, an ambiguous date and a correction that appears later in the text. Record which step should catch each problem. If a rule changes, preserve its version with the result so the review can be understood later. The aim is to make the handoff between interpretation and checking visible.
Use this map in an initial platform conversation: source, proposed interpretation, explicit check, reviewer and allowed action. It provides a specific basis for discussing knowledge retrieval, orchestration and controls without assuming that every step should be automated.
FROM INSIGHT TO YOUR USE CASE
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