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How to scope a practical AI automation pilot

Start with one painful process, an owner, and a measurable baseline.

Project guide

Map the current process

Capture every manual action, exception, source system, approver and failure mode.

Define an acceptance scorecard

Establish review minutes, quality thresholds, error budgets and exception routing before writing code.

Run in shadow mode

Compare automated recommendations to historical or live human decisions without executing external changes.

Release in controlled steps

Enable a narrow low-risk segment, monitor errors and expand only when the evidence supports it.

Putting the guidance into practice

Start by documenting the process and defining what counts as a successful, reversible and auditable result. Agree on permissions, decision ownership and operational metrics before activating the workflow.

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