Every organization using AI needs an Acceptable Use Policy. The question isn't whether to have one—it's whether to have one that works.
Most AI policies fail not because they're wrong, but because they're ignored. They're too restrictive, killing adoption before it starts. Or they're too vague, leaving employees uncertain about what's actually allowed. Or they're written once and forgotten, becoming irrelevant as AI capabilities evolve. The result is the same in each case: the policy exists on paper while reality diverges in practice.
A policy that works does something different. It creates a clear, safe space for productive AI use while establishing non-negotiable boundaries. It gives employees confidence to experiment and leadership confidence that risks are managed. It evolves with the technology it governs.
The Two Failure Modes
Organizations writing AI policies typically fall into one of two traps.
The first trap is prohibition masquerading as governance. The policy lists everything employees can't do, with so many restrictions that using AI feels dangerous. Don't input any company data. Don't use AI for customer-facing work. Don't use any tool that isn't explicitly approved. The intent is protection, but the effect is paralysis. Employees who want to use AI productively give up or go underground, using personal devices and unapproved tools where the organization has no visibility at all.
The second trap is vagueness masquerading as flexibility. The policy says "use AI responsibly" without defining what responsible means. It encourages "appropriate use" without explaining appropriateness. Employees are left guessing, and different people guess differently. Some interpret the vagueness as permission and push boundaries. Others interpret it as hidden restriction and avoid AI entirely. The organization gets neither the adoption it wants nor the risk management it needs.
Both traps share the same underlying problem: they don't tell people what they can do. The most important function of an AI policy is positive guidance—clear permission to use AI in specific, valuable ways.
Building a Policy That Works
Effective AI policies share certain characteristics. They're specific without being exhaustive. They're enabling without being reckless. They connect to the organization's broader AI philosophy while providing actionable guidance.
The policy should start with acceptable uses. This is counterintuitive—most policies lead with prohibitions—but it's essential. Employees need to know what's encouraged, not just what's forbidden. Drafting documents, summarizing information, brainstorming ideas, automating repetitive tasks, assisting with code, analyzing data—these uses should be explicitly welcomed, with the qualifier that human review applies before outputs are finalized or shared.
After acceptable uses come the clear prohibitions. These are the non-negotiable lines: don't input sensitive customer data into public AI tools, don't publish AI-generated content without human review, don't use AI to create deceptive or misleading outputs, don't share credentials or proprietary information in prompts. The prohibitions should be specific enough that employees can clearly distinguish allowed from forbidden. "Don't input sensitive data" is better than "be careful with data," but "don't input customer PII, payment information, or unreleased product details" is better still.
The policy should also specify which tools are approved. One of the fastest ways to drive shadow AI usage is failing to provide legitimate options. If employees need AI and the only available tools are forbidden, they'll find workarounds. Naming approved platforms—enterprise versions of ChatGPT or Claude with proper data protections, internal tools, AI features embedded in existing software—gives employees sanctioned paths to productivity.
The Human-in-the-Loop Principle
Running through any effective AI policy is a consistent principle: humans remain accountable for AI outputs. This isn't just a compliance statement—it's an operational requirement that shapes how AI gets used.
Human-in-the-loop means AI can draft, but humans finalize. AI can suggest, but humans decide. AI can accelerate, but humans verify. This principle protects against the reliability issues inherent in current AI systems. It ensures that human judgment—with all its domain expertise, contextual understanding, and accountability—remains central to important decisions.
The policy should make this principle concrete. What does review mean for different types of work? How thorough should verification be for different stakes? Who approves AI-assisted outputs before they're used? These operational details turn the principle into practice.
Evolving With the Technology
AI capabilities change faster than most policy review cycles. A policy written just months ago could already be outdated—not because the principles changed, but because the technology created new possibilities and new risks that the policy didn't anticipate.
Effective policies build in mechanisms for evolution. They specify how often the policy will be reviewed—quarterly or semi-annually is reasonable given the pace of change. They identify who owns the policy and who can propose updates. They create channels for employees to raise questions and edge cases that might require clarification.
This living-document approach has a cultural benefit beyond risk management. It signals that the organization is paying attention, learning, and adapting. Employees see that their questions get answered and their concerns get addressed. The policy becomes a reflection of ongoing organizational learning rather than a static artifact from a moment in time.
The Relationship to Philosophy
An AI policy doesn't stand alone. It's the operational expression of the organization's AI philosophy, as articulated in its AI Charter and Principles. The policy tells people what to do; the charter explains why.
This relationship matters for two reasons. First, it gives the policy depth. Rules backed by principles are easier to follow because they make sense. Second, it gives employees a framework for novel situations. When the policy doesn't explicitly address something, the charter's principles provide guidance.
Consider an employee facing a decision the policy doesn't cover. If they understand the organization's commitment to human-centered AI (AI assists, humans decide) and accountability (humans own the outputs), they can make a reasonable judgment about their situation. They don't need to wait for policy clarification to act responsibly.
From Compliance to Culture
The ultimate goal of an AI policy isn't compliance—it's culture. A good policy creates norms that employees internalize and extend. They don't just follow the rules; they understand the thinking behind the rules and apply that thinking in their own work.
When this happens, AI governance becomes self-sustaining. Employees make good decisions without being monitored. They raise concerns when they see potential issues. They share best practices and learn from each other. The policy is no longer a constraint imposed from above but a shared understanding that everyone maintains.
Getting there requires a policy that respects employee intelligence. It explains the reasoning behind its rules. It provides positive guidance, not just prohibitions. It creates space for questions and evolves in response to them. And it connects to a broader philosophy that gives the rules meaning.
That's what it takes to write an AI policy that actually gets followed. Not perfect rules—there's no such thing—but clear guidance that employees can understand, trust, and build on.