Deploying an AI agent is not flipping a switch. It's an onboarding process with defined stages, milestone checks, and graduated autonomy.
Days 1-7: Shadow mode. The agent processes real data but takes no action. Outputs are compared side-by-side with human work. Key metric: accuracy rate versus human baseline.
Days 8-14: Supervised mode. Outputs are queued for human review before execution. Key metrics: human approval rate (target: 90%+), average review time, modification patterns.
Days 15-21: Graduated autonomy. Low-risk outputs proceed automatically. Medium and high-risk outputs still require human review. Key metric: escalation rate.
Days 22-30: Full autonomous with audit. The agent operates independently with periodic spot-checks. Key metrics: error rate on audited samples, escalation appropriateness, processing volume.
Throughout all stages, every action generates an audit trail. Corrections flow back into the learning loop.
Common mistakes: skipping shadow mode, having the wrong people do reviews (it should be the person who currently does the work), and setting autonomy thresholds too aggressively.
The 30-day process applies whether you're deploying a scheduling agent in healthcare or a bookkeeping agent in accounting.
A workforce discovery session includes a deployment timeline specific to your workflow and team.