Employees are already using AI tools in many companies. Some are using approved tools. Others are experimenting with public tools, personal accounts, browser extensions, or disconnected apps.

The risk is not only that people use AI. The risk is that they use it inconsistently, with unclear data rules, no review expectations, and no shared understanding of appropriate use.

Start with practical use cases

Good AI guidelines should explain where AI is useful. Employees need examples, not just warnings.

For many teams, approved use cases may include summarizing non-sensitive materials, drafting internal notes, rewriting content, brainstorming, preparing outlines, comparing options, or creating first-pass analysis for review.

Define restricted use cases

Guidelines should also explain where AI should not be used without review or approval.

Examples may include sensitive customer information, patient information, employee records, financial data, legal matters, regulated decisions, confidential strategy, or any workflow where an AI output could be used without appropriate human review.

Create simple data handling rules

Employees need plain-language guidance on what they can and cannot enter into AI tools. Avoid vague advice like “be careful with data.” Be specific.

• What data is always prohibited?
• What data requires approval?
• What data can be used freely?
• Which tools are approved for which types of work?

Clarify human review expectations

AI output should usually be treated as a draft, summary, or assistant output — not a final decision.

Guidelines should explain when employees must verify facts, check source materials, review tone, validate calculations, or escalate the work to a manager, expert, legal reviewer, compliance reviewer, or other qualified person.

Give examples by role

Generic guidelines are easy to ignore. Role-specific examples are more useful.

Operations teams may need examples for summarizing requests. Support teams may need guidance for drafting responses. Managers may need help preparing meeting summaries. Healthcare or pharmacy teams may need more careful data boundaries.

Keep the first version simple

The first version of AI guidelines should be clear enough to use. You can improve it as the company learns more about real usage patterns.

The goal is not to stop useful experimentation. The goal is to help employees use AI more productively, consistently, and safely.