An AI policy should do more than tell employees to use AI responsibly. 

It should help them understand what responsible use means when they are making real decisions in the course of their work. 

Can I use this tool? Can I share this information? Do I need to disclose that AI was involved? What happens if an AI agent takes an action on our behalf? Where should meaningful work developedwith AI live? And when do I need another human involved? 

These are no longer hypothetical questions. They are everyday operating questions. 

As AI becomes embedded in more of the tools people use, policies built around a short list of approved platforms or broad instructions to “protect confidential information” can leave important gaps. 

A strong AI policy should create clarity around five fundamental areas: tools, data, transparency, accountability and risk. One principle should connect them all: AI can perform tasks, but people remain accountable for how it is used and the outcomes it helps produce. 

So, what should your AI policy address? 

1. Permissible and Prohibited AI Platforms & Tools 

Start with one of the most basic questions employees have: What can I use? 

AI is no longer limited to a handful of well-known standalone platforms. It is appearing inside software, search tools, productivity suites and specialized applications employees may already use. 

An effective policy needs to give people a clear way to understand which AI tools and capabilities are permitted, what is not permitted and what to do when they encounter something new. 

The question to test your policy against: Do employees know which AI tools they can use and where to go when they are unsure? 

2. Data Sharing & Protection 

An approved AI tool does not automatically mean all organizational information is appropriate to share with it. 

Your policy needs to establish clear expectations for protecting company, client, employee and other sensitive information when AI is involved. The specifics will vary based on the information your organization handles, the tools it uses and the protections those tools provide. 

This is important enough that we explored it in greater depth in our recent article, “The Emerging AI Data Custody Problem: Why an AI Policy Isn’t Enough.” That article looks at why traditional guidance such as “don’t put sensitive information into AI” is no longer enough as AI becomes part of everyday work. 

The question to test your policy against: Do employees know what information they can share with AI and under what circumstances? 

3. Vendor & Third-Party AI Use 

Your organization may have rules for the AI tools employees choose to use. But what happens when AI is being used by a vendor, consultant, agency or other outside partner? 

AI capabilities are increasingly embedded throughout the products and services organizations rely on. That makes third-party AI use part of the governance conversation, not a separate issue that sits outside your policy. 

The question to test your policy against: Do your expectations for responsible AI use follow your information and work when they move beyond your organization? 

4. AI Agents, Automation & Autonomy 

AI that generates a draft is different from AI that can take an action. 

As organizations begin experimenting with agents and more autonomous AI systems, policies need to recognize that distinction. And even organizations that are not using agents today should consider what governance expectations need to exist before they are deployed. 

Greater autonomy requires greater guardrails, not fewer. 

Who is accountable when an agent acts? What authority should it have? When does a human need to intervene? Those questions become more important as AI moves from assisting with work to acting within workflows. 

The question to test your policy against: Does your policy account for increasing levels of AI autonomy, including capabilities your organization may introduce next? 

5. Intellectual Property & Content Reuse 

Generative AI has made it remarkably easy to create, transform and reuse content. It has also created new questions about ownership and appropriate use. 

Employees may be working with copyrighted material, proprietary company content, client materials or AI-assisted outputs without always recognizing that different rules or obligations may apply. 

An effective policy should help people recognize when intellectual property, ownership or reuse considerations require additional care or guidance. 

The question to test your policy against: Do employees know when AI-assisted creation or reuse raises an ownership or permission question? 

6. AI-Generated Images, Audio & Video 

Synthetic media deserves explicit attention. 

The ability to generate realistic images, voices and video introduces considerations that extend beyond text-based AI use, including authenticity, permission, reputation and disclosure. 

Those questions become especially important when content represents real people or is shared publicly, with clients or with other external audiences. 

The question to test your policy against: Have you established clear expectations for the responsible use of AI-generated or AI-altered media? 

7. Transparency & Disclosure 

When should someone know that AI was involved? 

There is no single disclosure rule that works for every organization, audience or use case. But leaving the decision entirely to individual employees can create inconsistency and undermine trust. 

Your policy should make clear that transparency is part of responsible AI use and identify the kinds of situations where disclosure expectations need to be considered. 

The question to test your policy against: Do employees understand when the role of AI may be material enough that transparency matters? 

8. AI Records & Knowledge Retention 

AI platforms are becoming places where people brainstorm, analyze, draft and develop meaningful organizational thinking. 

But an AI platform should not quietly become your organization’s knowledge-management system. 

Organizations need to consider how meaningful work developed with AI connects back to established systems for records, documents and organizational knowledge. 

Your AI platform can help create organizational knowledge. It should not become the only place that knowledge lives. 

The question to test your policy against: When valuable work happens with AI, do employees know what needs to become part of the organization’s lasting record? 

9. Incident Reporting & Response 

Even with strong governance, mistakes and unexpected outcomes will happen. 

The goal is not to create so much fear around AI use that employees hide problems. It is to make sure people know what to do when something goes wrong or falls outside established expectations. 

Good governance depends on visibility. Organizations cannot learn from problems they never hear about, and employees cannot grow without the opportunity to examine and discuss what went wrong. 

The question to test your policy against: Do employees know where to go when an AI-related concern, error or unexpected situation occurs? 

10. Training, Acknowledgment & Policy Review 

Publishing an AI policy is not the same as implementing one. 

People need to understand the policy in the context of their work, know where to ask questions and build confidence applying the organization’s expectations. 

The goal is not a document employees acknowledge once and forget. It is a shared set of expectations that stays connected to how people work and evolves as technologies, use cases and risks change. 

As we explored in our recent article, “AI Governance Cannot Stand Still When AI Doesn’t,” keeping governance effective requires organizations to revisit their policies as both the technology and their use of it change. 

The question to test your policy against: Is your policy supported by the training, communication and review needed to keep it useful over time? 

The Policy Is the Beginning, Not the Governance System 

There is one more principle that should not be confined to a single section of the policy: human accountability. 

Someone still owns the work. 

That principle applies whether AI is helping draft an email, analyze information, create an image or take actions through an autonomous agent. The level of human involvement may change depending on the use and the risk, but accountability does not disappear because AI entered the workflow. 

Knowing what an AI policy should cover is the starting point. Determining what those expectations should look like for your organization is the harder work. 

The right answers depend on your people, your data, your technology, your workflows and your risk. That is why effective AI governance cannot simply be copied from another organization’s policy or generated from a template. It has to be designed around how your organization works. 

And ultimately, the measure of an effective AI policy is not how comprehensive it looks on paper. It is whether people understand it well enough to make sound decisions when AI presents a situation the policy could not predict. 

---

Remember, AI won’t take your job. Someone who knows how to use AI will. Upskilling your team today, ensures success tomorrow. Custom in-person and virtual trainings are available. If you’re looking for something more top-level to jump start your team’s interest in AI, we offer one-hour Lunch-and-Learns. If you’re planning your next company offsite, our half-day workshops are as fun as they are informational. And, of course, we offer AI consulting and GEO strategies. Whatever your needs, we are your partner in AI success.

Read more: Top 10 Things Your AI Policy Should Include