An AI agent can consult authorized sources, organize information and support stages in a digital workflow. That does not mean every repetitive task should be automated. The decision starts with the process, available data and the consequence of an incorrect result.
Start with the operational problem
Document who starts the work, what information is needed, where delays occur and who validates the result. This separates a real opportunity from a technology demonstration without operational value.
Choose a controllable first process
A useful pilot has a clear objective, enough volume to evaluate and limited consequences when an output needs correction. Higher-risk decisions require additional analysis and specialized human oversight.
Define criteria before testing
- What outcome should the agent produce?
- Which sources may it consult?
- Who reviews the output?
- Which data must never be used?
- How will errors and exceptions be recorded?
Test before scaling
A proof of concept should evaluate usefulness, consistency, review effort, limitations and cost. Moving forward, adjusting or stopping are all valid technical outcomes.
Keep human accountability
Automation does not transfer accountability to a model. The organization remains responsible for its data, decisions and impacts.
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