This is a starting-point structure for an internal AI use policy covering logistics, distribution, and quality teams in a GDP/GxP environment. It is a template to adapt with legal, quality, and IT stakeholders — not a ready-to-adopt policy document.
1. Purpose and Scope
State which teams and roles the policy covers (e.g., cold chain operations, QA/compliance, distribution/3PL management, regulatory/customs), and which categories of AI tools are in scope (general-purpose consumer AI, enterprise-tier AI, embedded AI features in existing software, domain-specific logistics AI platforms).
2. Approved Tools and Tiers
List which specific AI tools and account tiers are approved for organizational use, distinguishing consumer-tier (personal accounts, data may be used for training) from enterprise/business-tier (data controls available, typically excluded from training). State clearly that personal/consumer accounts are not approved for use with organizational data, referencing AI Governance: Consumer AI Limitations.
3. Data Classification and Handling
Define what categories of data may never be entered into any AI tool (e.g., patient-identifying information, unredacted batch records with proprietary formulation details), what may be entered only into enterprise-tier tools with data controls, and what is generally low-risk (e.g., publicly available regulatory text).
4. Approved Use Cases and Decision Boundaries
List the specific workflows approved for AI assistance (e.g., excursion investigation drafting, SOP gap analysis, carrier risk narrative drafting) and explicitly state which decisions can never be delegated to AI output alone: product disposition, deviation root cause determination, regulatory interpretation, and any customer- or auditor-facing final documents without human review.
5. Review and Oversight Requirements
Reference the oversight structure in AI Governance: Validation and Oversight: every approved use case must have a named reviewer role, a documented review step, and a periodic re-evaluation cadence.
6. Reporting Unsanctioned Use
Provide a low-friction way for staff to report AI tools or workflows they’ve adopted informally, framed as a way to get the tool sanctioned and supported rather than solely as a compliance violation to be punished — see AI Governance: Shadow AI Risks for why this framing matters.
7. Training and Communication
State how the policy will be communicated (onboarding, periodic refreshers) and how staff will be trained on both the approved workflows and the reasoning behind data-handling restrictions.
8. Policy Review Cadence
Set a defined interval (e.g., annually, or upon a significant change in available AI tools) to revisit the policy, given how quickly the AI tool landscape is changing.
Related
Last updated August 7, 2026
