Most AI projects fail because they try to transform everything at once. The fastest way to prove value is to run a narrow pilot with clear inputs, clear owners, and one measurable output.
Week 1: Pick one painful workflow
Choose a workflow where your team loses time every day:
- supplier follow-up and ETA collection
- reorder recommendation and PO draft generation
- disruption triage and internal alerting
Set one success metric such as "reduce manual follow-up time by 40%" or "cut disruption response time from 6 hours to 30 minutes."
Week 2: Define data and guardrails
Map the exact systems and fields your agent needs. Keep scope tight: one ERP table, one spreadsheet source, one communication channel. Then define what the agent can and cannot do:
- can draft messages, cannot send external emails without approval
- can propose PO lines, cannot submit final PO
- can escalate high-risk events to operations lead
Guardrails prevent trust issues and speed up adoption.
Week 3: Run shadow mode
Let the agent run in parallel while humans still execute decisions. Compare outputs daily:
- recommendation quality
- time-to-action
- error rate
Shadow mode gives evidence without operational risk.
Week 4: Move to controlled execution
Enable one low-risk action path (for example, internal alerts or draft PO generation). Keep approval checkpoints and publish daily pilot metrics to the team.
At the end of day 30, decide one of three paths: scale, adjust, or stop. The point of the pilot is fast learning with measurable business impact.
