Your AI Policy Is Written. Can Your Employees Actually Use It?

Before you read on, ask yourself:

• Do employees know which AI tools they can use for work?
• Can they tell the difference between information they can share and information that needs protection?
• Do they know who to ask when the policy doesn’t answer their question?

Your company has an AI policy. It has been approved, shared with employees, and saved in SharePoint.

That’s a good start.

But now imagine an employee wants to summarize a document before a meeting. Another sees an AI feature inside an application they already use. A third wants help drafting an email to a client.

Do they know what is allowed? Do they understand what information they can share? And do they know where to turn if they are unsure?

If the answer is no, the policy may exist without providing the guidance employees need during their workday.

During our recent AI in the Workplace Thought Leadership Session, Kayvan Yazdi and Latifa Afitou discussed this challenge. Their message was straightforward: writing an AI policy is an important first step, but employees also need the tools, training, controls, and support to put it into practice.

The Moment an AI Policy Gets Tested

AI policies are often written in broad terms. They may instruct employees to protect confidential information, use approved tools, and follow company security practices.

Those expectations matter. But employees experience AI decisions one task at a time.

Someone may wonder:

• Can I use AI to organize notes from a meeting?
• Is a document considered confidential if it does not have a label?
• Does an approved business application mean all its AI features are approved?
• Can I use AI to draft a client email if I remove the client’s name?
• Who can answer these questions before I move forward?

A policy does not need to predict every situation. It does need to give employees enough direction to recognize when they can proceed and when they should ask for help.

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1. Make Approved Tools Easy to Find

If employees need to search through folders or ask several people which AI tools are approved, the guidance may not be reaching them when they need it.

Create a simple, accessible list that explains:

• Which AI tools or features employees may use for work
• Whether a specific company account is required
• Which types of tasks the tool is approved to support
• Where employees can request guidance about another tool or feature

This list should also account for AI capabilities within applications employees already use. An employee may encounter an AI feature while working in a document, meeting platform, or customer relationship management system, rather than opening a separate chatbot.

Why it matters: Employees need a clear path to approved tools before they can follow the policy with confidence.

2. Explain the Rules Through Everyday Tasks

“Use AI responsibly” sounds reasonable, but it leaves room for interpretation.

Employees are more likely to understand the guidance when it connects to familiar work. For example, a business might explain how its rules apply to:

• Brainstorming ideas using public information
• Preparing a first draft of an internal announcement
• Summarizing a document for a meeting
• Drafting a client communication
• Analyzing information that includes financial, employee, or client details

The point is not to declare every task universally safe or unsafe. The appropriate decision depends on the organization’s approved tools, the information involved, and the intended use.

A useful explanation tells employees what they can do, what conditions apply, and when they need approval.

Why it matters: Realistic examples help employees connect a written policy to the decisions they make every day.

3. Make Data Boundaries Clear

One of the most important questions in any AI interaction is: What information is being shared?

Brainstorming presentation titles is different from uploading a document containing confidential client information. The AI tool may be the same, but the information involved changes the risk.

During the webinar, Kayvan described three useful categories for this conversation:

• Public information: Information intended to be shared openly.
• Internal information: Business information that is not necessarily public and needs clear handling guidance.
• Confidential or regulated information: Sensitive client, financial, health, employee, or intellectual property information that requires greater care.

These categories provide a starting point. Employees also need to understand how their organization’s rules apply to each category and which approved tools, if any, may be used with it.

If someone cannot determine what type of information a document contains, the policy should make the next step clear: pause and ask.

Why it matters: A general warning to “be careful with data” is less useful than guidance employees can apply before they enter or upload information.

4. Train Employees on Both Opportunity and Risk

AI education should help employees understand the boundaries, but it should also show them where the technology can be useful.

In the webinar, we discussed practical ways AI can support everyday work, including drafting, research, summarizing, analysis, and repetitive tasks. Training can connect these opportunities with the organization’s rules.

For example, a training conversation might walk through:

1. The task an employee wants to complete
2. The approved tool available for that task
3. The information the employee plans to use
4. Whether the employee can proceed or needs guidance
5. Who should review the result before it is relied upon or shared

This makes the policy easier to understand than simply sending employees a document and asking them to acknowledge it.

Why it matters: Employees need to know both where AI can help and how to use it within the organization’s expectations.

5. Make It Easy to Ask for Help

Even a well-written policy cannot answer every question.

An employee may encounter a new AI feature, an unfamiliar document, or a task that falls between the examples provided. In those moments, uncertainty should lead to a conversation.

Tell employees who they can contact when:

• They are unsure whether a tool or feature is approved
• They need help determining whether information is sensitive
• They want to propose a new AI use case
• They believe information may have been shared in error

The person or team answering does not need to be the same for every question. What matters is that employees know where to start.

Why it matters: An easy way to ask questions gives the organization a chance to address uncertainty before employees make assumptions.

A Practical Test for Your AI Policy

To see whether your policy is working in practice, bring a few common scenarios to a team meeting.

For each one, ask:

1. Which tool would you use?
2. What information would you enter?
3. Is that use covered by our guidance?
4. If you are unsure, who would you ask?

Listen for points where employees interpret the rules differently or cannot find an answer. Those gaps can help you improve the policy, training, and supporting guidance.

You do not need to solve every possible AI question at once. Start with the tasks your employees encounter most often and build from there.

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Your AI policy may be written. The next step is making sure it helps your people make confident decisions when they need it.

Iman Oskoorouchi
President, Co-Founder

Iman Oskoorouchi, President and Co-founder of TruAdvantage, studied Electrical Engineering at UC Davis and holds multiple IT certifications. With over two decades of experience helping Bay Area and California businesses and healthcare practices navigate digital transformation, Iman is known for his personal touch and deep industry expertise. He believes technology should serve people first, then systems, combining technical insight with a human-centered approach to build secure and efficient IT environments. A lifelong learner inspired by books like The Untethered Soul and The 5AM Club, he finds balance in backcountry skiing, philosophy, and Thai green curry.

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