AI Implementation Checklist

1. Data Unification Are your core operational systems (TMS, ERP, CRM) accessible via modern REST/GraphQL APIs or secure direct database querying?
2. Exception Tracking Do you have a clear, documented process for how exceptions are currently handled when they arise?
3. Defined Triggers Are the 'triggers' that initiate human action currently visible in the system (e.g., status changes, incoming emails, specific alerts)?
4. Output Verification Do you have a strict "Human in the Loop" gate for any external communication or irreversible financial action?
5. Context Window Readiness Is the historical context needed to solve a problem contained within text (emails, notes) or requires undocumented human intuition?
6. Security Guidelines Have you established a policy on handling PII/PHI or highly confidential financial data through LLM prompts?
7. Team Buy-in Are your operators aware that AI is meant to elevate them from data-entry to decision-making, rather than replace them?
8. Success Metric Definition Do you measure success by error reduction and turnaround time rather than just counting operations completed?
9. Fallback Protocols If the AI service experiences a timeout or hallucination, do you have a standard operating procedure for graceful fallback to manual handling?
10. Architecture Alignment Are you deploying an AI 'orchestrator' rather than just a basic chat wrapper around existing tasks?