How Do You Get Employees to Actually Follow Your AI Policy?

You've written an AI policy. It's been approved, circulated and probably saved somewhere on SharePoint. So why are employees still using AI tools nobody knows about?

It's a familiar challenge.

Organisations are working hard to introduce AI responsibly. Policies are being written, acceptable-use guidelines are being developed, and IT teams are trying to establish some control over which tools employees can use.

Meanwhile, someone in marketing has discovered an AI presentation tool, a sales colleague is uploading meeting notes into a transcription service, and another team has found a browser extension that promises to save them three hours a week.

None of them necessarily thinks they're doing anything wrong.

They're simply trying to get their jobs done.

And therein lies the problem.

Having an AI policy and having employees who actually follow it are two very different things.

So how do you close that gap without becoming the department that says no to everything?

1. Start by finding out what's actually happening

Before deciding how employees should use AI, it helps to understand how they're already using it.

And that means looking beyond your list of approved applications.

Which tools are people experimenting with? What tasks are they using them for? Are they entering business information into those tools? Have they connected AI applications to company systems?

You might be surprised by the answers.

An employee using an unapproved AI service isn't necessarily trying to bypass IT. They may simply have found something that solves a problem faster than the tools you've provided.

That's useful information.

If ten people are independently using the same AI application, perhaps there's a genuine business requirement that needs addressing.

If employees are uploading sensitive documents to get a summary, perhaps the organisation needs to provide a secure, approved way to achieve the same result.

The objective isn't to catch people out. It's to understand the demand before deciding how to manage it.

Anonymous surveys, conversations with department heads and appropriate application discovery tools can all help build a clearer picture.

After all, it's difficult to govern technology you don't know is being used.

2. Make your policy realistic, not just restrictive

There's a temptation to approach AI governance by creating a long list of things employees mustn't do.

Don't use unapproved tools.

Don't upload confidential information.

Don't connect AI applications to business systems.

Don't do anything without asking IT first.

All reasonable concerns, depending on the circumstances.

But if the policy consists entirely of restrictions, employees may struggle to see how they're supposed to use AI productively.

And if getting approval takes three weeks while an AI tool can complete a task in three minutes, you can probably guess which option will be more appealing.

A practical policy needs to explain what employees can do, not just what they can't.

For example:

• Which AI tools are approved for business use?
• What types of information can be entered into them?
• Which activities require additional approval?
• Can employees use AI to draft emails, summarise documents or analyse spreadsheets?
• What should someone do if they discover a useful tool that hasn't been approved?

Clear boundaries make it easier for people to make the right decisions.

A policy should support the way your organisation works, not assume everyone will abandon useful technology because a document tells them to.

3. Explain the risks without delivering a cybersecurity lecture

Most employees don't need a detailed explanation of AI architecture or data-processing agreements.

They do need to understand why certain behaviours could create problems.

Take uploading a customer spreadsheet into a free AI tool.

The concern isn't simply that the application uses artificial intelligence.

It's whether the organisation has assessed how that service handles information, what protections apply, who might have access to the data and whether the upload is appropriate in the first place.

That's something people can understand.

Likewise, connecting an AI application to a company mailbox isn't just a convenient new feature. It may involve granting access to business communications, attachments and other information.

Employees are much more likely to make informed decisions when they understand the consequences.

Use examples relevant to their roles.

Sales teams might need guidance on customer records and commercial information. HR teams may handle employee data. Finance teams could be working with forecasts, pricing or payment details.

Make the guidance specific enough that people recognise their own working day.

And perhaps keep the 60-slide presentation on AI risk management for another occasion.

4. Give employees approved tools that actually help

This is one of the most important parts of the conversation.

If your organisation wants employees to stop using unapproved AI applications, it needs to understand why they're using them.

Sometimes the answer is straightforward.

The approved alternative doesn't offer the functionality they need.

Or nobody knows it exists.

Or access is so complicated that people give up before they get started.

Providing an approved AI platform is a good step but simply making it available doesn't guarantee adoption.

Employees need to understand what it can do, how to use it and where to get help.

That might mean introducing practical demonstrations, creating simple use-case guides or working with individual departments to identify useful applications.

It also means having a sensible process for evaluating new AI tools.

Not every request should automatically be approved, but employees should know there's a route for suggesting technology that could genuinely benefit the business.

If the approved option is useful, accessible and easy to understand, people have far less reason to look elsewhere.

5. Involve employees instead of writing the rules in isolation

IT and security teams have an important role in AI governance.

But they don't necessarily know every workflow, challenge or productivity bottleneck across the organisation.

The people doing those jobs do.

Involving employees when developing or reviewing an AI policy can highlight issues that might otherwise be missed.

Perhaps a department relies heavily on transcription tools.

Maybe another needs AI-assisted research but isn't clear which information can be shared.

Or perhaps an approved platform already provides the functionality people are buying elsewhere.

These conversations can help shape policies that are both secure and practical.

They also create a sense of shared responsibility.

Rather than treating AI governance as something imposed by IT, employees become part of the process of making it work.

And when people understand why decisions have been made, they're generally better equipped to follow them.

6. Support the policy with appropriate technical controls

Of course, conversations, guidance and training are only part of the answer.

Organisations also need to consider how they can identify risky AI usage and apply appropriate safeguards.

Depending on the environment, this might include:

• Application discovery and visibility: Understanding which AI services are being accessed across the organisation.
• Access controls: Restricting or managing access to applications that haven't been approved.
• Data loss prevention: Helping prevent sensitive information from being shared inappropriately, where supported.
• Permission reviews: Checking what information AI tools and connected applications can access.
• Monitoring and audit capabilities: Identifying unusual activity and reviewing how approved services are being used.
• Third-party application controls: Assessing and managing the permissions granted to connected AI tools.

The right approach will depend on the organisation's systems, requirements and risk profile.

And it's worth remembering that no single tool provides complete visibility or protection across every AI service.

Technical controls should reinforce the policy, not replace the need for clear guidance and employee engagement.

There's also an important balance to strike around monitoring. Employees should understand what is monitored, why it's necessary and how their information is handled.

The goal is appropriate oversight, not turning every AI prompt into an investigation.

7. Keep reviewing what's working, and what isn't

AI governance isn't something you can complete, file away and revisit in three years.

New applications appear constantly. Existing platforms introduce additional capabilities. Employees discover different ways of using the technology.

And the risks change alongside them.

A policy that made perfect sense six months ago might not reflect how your organisation uses AI today.

Regular reviews help identify where guidance needs updating and whether the controls you've introduced are actually effective.

Useful questions include:

• Are employees using the approved AI tools?
• Are requests for new applications being assessed promptly?
• Have any data-handling concerns, or policy exceptions been identified?
• Do employees understand what information they can share?
• Are technical controls working as intended?
• Has anything changed in the AI services or integrations the organisation uses?

You don't necessarily need an enormous governance committee to answer these questions.

But you do need clear ownership, a review process and a willingness to adjust your approach when something isn't working.

Because an AI policy should evolve with the business, rather than becoming another document that everyone remembers receiving but nobody remembers reading.

So, how do you make an AI policy work?

It comes down to something fairly simple.

Make it easier for employees to do the right thing than the wrong thing.

Understand which tools they're using and why. Provide approved alternatives that genuinely help them work. Explain the risks in language that makes sense. Give people a way to ask questions and suggest improvements.

Then support all of that with appropriate technical controls and regular reviews.

AI adoption is happening because people can see the benefits. The challenge for IT leaders isn't to stop that enthusiasm, but to make sure it's supported by sensible governance.

A well-written policy is a good starting point.

A policy that employees understand, can follow and actually use is considerably more valuable.

And if nobody can remember where yours is stored, that might be the first thing to fix.

Making AI governance practical

At Robinscroft, we believe technology should help people work smarter without creating unnecessary complexity or risk.

From understanding how AI is being used across your organisation to reviewing security controls, permissions and data protection, effective AI governance starts with getting a clearer picture of what's happening today.

Because having the right policy matters.

Making sure it works in practice matters even more.