Why AI Policies Fail and How to Write One That Works

These days, it’s trendy to say you “have an AI policy.” It signals responsibility. Readiness. Leadership. But when you actually open up those policies, most fall apart within the first few paragraphs. They tend to live in a forgotten section of the employee handbook, vaguely lumped in with tech use or data security. And while the intent behind them may be genuine, the execution often leaves teams completely unsupported. That’s a problem.

The issue isn’t that companies don’t care. It’s that they assume having any policy is enough. They overlook the fact that AI touches workflows in ways traditional policies weren’t built to handle. Things like automated resume screening, performance scoring, internal communications drafting, or customer service scripts these are all places AI is being used, and yet most policies offer little to no practical guidance on them. If your AI policy doesn’t address the way your people actually use AI, it’s not a policy. It’s a placeholder.

And here's where it gets dangerous: relying on an unclear or outdated policy creates a false sense of security. Leaders assume they’re protected. Legal thinks the bases are covered. But when something goes wrong, like a discriminatory output or a data leak via a public AI tool, the organization is caught off guard. Because no one realized that the AI policy wasn’t doing the job it was supposed to do.

What a Real AI Policy Needs to Do

A strong AI policy is not just about risk prevention; it’s about equipping people to make smart, informed choices when working with AI. That starts with being specific. Your policy should spell out, in plain terms, how employee data can and cannot be handled by AI tools. Is it okay to feed employee records into ChatGPT to write a report? What’s the rule for using AI in performance reviews or hiring decisions? These are everyday use cases. Your policy should speak directly to them.

It also needs to be proactive about risk, not just reactive. That means documenting bias safeguards, setting expectations for how AI outputs will be reviewed, and creating a framework for regular vendor audits. You can’t just hope the tools you’re using are fair or safe. You need a process to confirm it and a paper trail that shows you did the work. Regulators are starting to ask tougher questions. So are employees and customers. A vague “we use AI responsibly” statement won’t cut it anymore.

Lastly, the policy needs to be written for humans, not just lawyers or IT. If employees can’t understand it, they won’t follow it. If managers can’t explain it, they won’t enforce it. Your policy should be short enough to read, clear enough to apply, and actionable enough that someone using AI can refer to it without calling legal every time. That’s the real test. If it’s not useful in practice, it doesn’t matter how “compliant” it sounds on paper.

The Top 3 Mistakes That Keep Happening

The first and most common mistake? Writing policies that are too vague. It’s easy to rely on broad language phrases like “Use AI ethically” or “Do not enter sensitive data”, because they feel safe. But these generalities don’t actually help anyone make decisions. What’s considered “sensitive”? What’s a real-world example of ethical use versus unethical use? Without specifics, teams are left to interpret things on their own, which opens the door to inconsistency, missteps, and preventable risk.

The second mistake is writing for the wrong audience. Too many AI policies are structured like legal disclaimers, technical, defensive, and written in a tone that feels more like a contract than a guide. This might make the legal department feel protected, but it doesn’t help employees understand how to apply the policy in their daily workflows. A frontline recruiter, a content strategist, or a product manager doesn’t need a lecture on model architecture; they need a quick answer on whether they can use an AI tool in their process or not.

The third mistake is failing to update the policy as tools and regulations evolve. AI moves fast. What was a safe use case six months ago might be high-risk today. What seemed compliant last year might now be out of step with new state or federal rules. A strong AI policy isn’t a static document; it’s a living one. If it’s not being reviewed regularly and updated with input from teams across your organization, it will quickly become irrelevant. And an irrelevant policy is worse than none at all; it gives the illusion of control while exposing you to greater risk.

Don’t Just Say You’re Ready. Prove It.

There’s a difference between announcing that your company uses AI responsibly and actually putting the systems in place to back that up. A functional AI policy isn’t something you draft once and forget. It’s a tool. A reference point. A shared agreement that empowers people to work smart without crossing ethical or legal lines. And right now, most companies are skipping this step in favor of speed. But speed without structure is what leads to lawsuits, bias scandals, and data breaches.

What does readiness look like? It looks like a clear policy that defines how AI tools can be used, and how they can’t. It includes a documented process for evaluating third-party AI tools before they’re rolled out. It outlines exactly how employee data will be protected, how outputs will be monitored for fairness, and how errors or concerns can be reported. It’s shared with employees. Trained on. Brought up in meetings. It becomes part of the way work gets done, not something tacked on after the fact.

This is especially important now that regulations are shifting fast. From the EU AI Act to U.S. state laws, the expectation is changing: companies are being asked not just to use AI, but to govern it. A policy that sits on a shelf won’t satisfy regulators, employees, or the public anymore. If you’re going to use AI in the workplace, and most companies already are, you need to be able to show how you’re doing it responsibly.

Want a Head Start?

You don’t need to start from scratch. If you’re realizing your current policy is too thin, or if you’re just getting started, we’ve built resources to help you move faster and smarter. Our 7-Point AI Readiness Kickstart gives you a clear framework for understanding where your gaps are, what needs to be fixed, and how to put smarter safeguards in place.

📩 Download the 7-Point AI Readiness Kickstart now, or schedule a call to get personalized support. You don’t have to figure this out alone!