Updated 2026-03-22
AI Policy Template for SMB Teams
A lightweight but practical policy baseline for data handling, approved tooling, human review, and incident response.
Core pillar
AI Governance Framework for Executive Teams
Use this policy template within AILD's main AI governance framework pillar.
GovernanceCornerstone 10 min For Leadership, operations, legal-adjacent teams
What You Will Get
- Deploy a policy baseline without enterprise complexity
- Define usable data boundaries for day-to-day workflows
- Create incident response steps for AI-related failures
Why this matters now
Unregulated AI adoption introduces material business risks: data leakage, compliance violations, and reputational damage. Establishing clear governance before scaling is a core leadership responsibility. This template provides the operational framework to deploy AI with control and accountability.
What leaders should do in the next 90 days
Weeks 1-4: Foundation & Pilot
- Appoint a single policy owner with cross-functional authority (e.g., Head of Operations or CTO).
- Define a pilot scope: one high-value, low-risk workflow (e.g., marketing copy generation or internal report summarization).
- Establish the Approved Tool Registry. Populate it with no more than three vetted tools. Ban all others in production.
Weeks 5-8: Implementation & Review
- Launch the pilot. Mandate that all outputs for customer-facing materials and financial reports undergo human review before release.
- Initiate a weekly governance review with the policy owner. Agenda: review audit logs, assess output quality, and adjudicate any policy exceptions.
Weeks 9-12: Scale & Systematize
- Based on pilot evidence, refine data classification rules (Public, Internal, Restricted) and human-review triggers.
- Formalize the incident protocol: 1) Immediate workflow pause, 2) Causal analysis documented within 48 hours, 3) Policy update to prevent recurrence.
- Schedule the first quarterly policy refresh and mandatory team training.
Failure modes to avoid
- Governance Lag: Allowing AI usage to expand before the approval registry, review protocols, and incident response are fully operational and tested.
- Vanity Metrics: Tracking only usage volume or cost savings instead of measuring impact on core business outcomes (e.g., error reduction, cycle time, compliance audit results).
- Symptomatic Fixes: Addressing individual AI errors with one-off corrections rather than analyzing patterns and redesigning the underlying workflow or policy boundary.