AI Policy Works
How the work is produced

Workplace AI Policy Methodology

AI Policy Works turns business operating choices into a practical workplace policy package. The process is designed to clarify daily behavior, ownership and review cadence without pretending one template creates legal or regulatory compliance.

Scope

The scope is agreed before research begins so the findings stay relevant to the customer’s actual decision.

  • Approved AI tools, accounts and permitted business uses
  • Sensitive, confidential, personal and regulated information boundaries
  • Required human review and approval points
  • Restricted uses, incident reporting and escalation
  • Policy ownership, employee acknowledgment and update cadence

Process

  1. Collect the business context, common AI use cases and current tool decisions.
  2. Identify the minimum durable rules needed for daily work.
  3. Separate stable policy rules from tool-specific details that belong in the approved-tools register.
  4. Draft editable policy, acknowledgment and rollout materials in plain language.
  5. Flag decisions that require legal, privacy, cybersecurity, HR or regulatory counsel.
  6. For Monthly Updates customers, monitor material changes and revise affected editable materials when warranted.

Evidence standards

Material observations are tied to a dated, reviewable source. Facts, interpretations and recommendations are kept separate. Missing information is labeled as missing or unclear rather than inferred. Customer-provided facts are distinguished from independently observable evidence.

Limitations

  • Materials are operational templates and general information, not legal advice.
  • The customer remains responsible for business-specific review, approval, rollout and enforcement.
  • Industry, jurisdiction, contract and data obligations may require qualified professional review.
  • Monthly monitoring focuses on material changes relevant to the package, not every AI product announcement.

Quality review

Before delivery, names, links, pricing labels, calculations and high-priority claims are checked for internal consistency. Recommendations must be specific enough to assign an owner and review date. Monitoring work uses the same defined comparison set so changes can be evaluated against a stable baseline.