Begin with a workflow, not a model
A useful AI program starts with work that someone owns. Define the people involved, the decisions being made, the systems being used, the current friction, and the outcome worth improving.
This prevents the pilot from becoming an isolated demonstration that cannot fit into daily operations.
- Name the workflow owner
- Establish a measurable baseline
- Identify exceptions and irreversible actions
- Confirm which information is actually available
Choose an opportunity that can teach you something
The first use case should matter enough to create evidence but remain bounded enough to understand. A repeatable workflow with accessible information and clear review points is often a better learning environment than an ambitious autonomous system.
- Business value
- Workflow clarity
- Data readiness
- Integration effort
- Risk and reversibility
Design the future workflow
A model output is not a finished product. Decide where information enters, what the system recommends or performs, when a person reviews it, how exceptions are handled, and what is recorded.
- Happy path and edge cases
- Human approval
- Fallback behavior
- User explanation
- Audit needs
Build evaluation into the pilot
Evaluation should reflect the actual task. Create representative scenarios before release, agree on a quality threshold, and record where the system is uncertain or requires escalation.
- Task success
- Unsupported output
- User correction
- Escalation rate
- Latency and cost
Integrate for real use
Production value appears when the capability fits identity, permissions, systems of record, and team responsibilities. Plan integration and operational ownership before the pilot is judged successful.
Measure behavior, not just output
A technically accurate solution can still fail if people avoid it or create workarounds. Measure adoption, completion, correction, exception handling, and changes to the original baseline.
Scale what the evidence supports
Use the first production result to decide what to improve, extend, standardize, or stop. Scaling should be a portfolio decision supported by workflow evidence, not excitement around a demonstration.