How To Create an AI Policy: A Step-by-Step Guide for Businesses

Employees are already using AI at work, whether their employer has approved it or not. From drafting emails to analyzing data, AI tools have moved into the workplace faster than most businesses can keep up. According to Paychex's 2025 State of Small Business AI Report, more than 80% of small business owners view AI as helpful, and 61% use it daily. But embracing AI without a plan for managing risk can lead to improper data sharing, compliance violations, biased outputs, and inaccurate information presented as fact.

An AI policy gives your business the guardrails it needs to support the responsible use of AI safely and effectively. Here's how to build one that works for you.

What Is an AI Policy?

An AI policy defines how employees, contractors, and business leaders may and may not use AI tools within an organization. It provides guidelines on what information can be shared with an AI tool, when and how AI can be used, how humans should oversee AI.

Your AI policy should be distinct from your IT acceptable use and data privacy policies, although these policies often go hand in hand. An AI policy addresses AI-specific use cases, both internal (how employees use AI day-to-day) and external (how you disclose AI use to customers and vendors).

It's also a living document. As AI evolves and your organization adopts new tools, expands use cases, and navigates new regulations, the policy will need to adapt.

Why Your Business Needs an AI Policy

No matter the size of your business, AI use comes with both opportunity and risk. Our research found that companies primarily use AI to boost productivity (39%), improve employee skills (39%), and automate tasks (36%). Business also use AI to solve different problems depending on their size:

• 1–19 Employees: Generate higher-quality data and drive more sales.
• 20–99 Employees: Automate workflows and strengthen cybersecurity.
• 100–1,000 Employees: Scale faster and improve efficiency with virtual assistants and agentic AI.

Across every stage, however, there are also inherent risks associated with AI. A clear, functional AI policy helps you create guidelines to reduce risk in several key areas:

• Data Privacy: The policy protects confidential customer or business data by identifying (or “providing guidance on”) what can be entered into AI tools.
• Legal and Compliance Risk: An AI policy should identify ethical, appropriate AI uses and prohibit or provide requirements for AI use in any regulated high-risk activities like hiring, finance, and customer interactions.
• Biased or Discriminatory Outputs: AI systems can reflect and amplify bias, creating risk in high-stakes decisions like hiring or promotions. An AI policy should require testing to identify and prevent bias.
• Consistent Use Across Teams: Without shared standards, different departments may use AI in conflicting or incompatible ways. This can create quality issues and collaboration problems.
• Human Oversight: Reliable AI outputs require thoughtful human oversight. When employees use AI without verification, errors and inaccuracies can occur.
• Employee Trust: A clear policy creates transparency and sets expectations for how AI is allowed to be used.
• Business Reputation: Customers, partners, and regulators expect businesses to demonstrate responsible AI use. A well-structured policy shows you take that seriously.

How To Create an AI Policy: Step-by-Step

You don’t have to build an AI policy from scratch. The nine steps below walk you through the process start to finish, from assembling the right team, to rolling out the policy and keeping it current.

Step 1: Assemble a Cross-Functional Team

Don’t rely on just one person to write your AI policy. Instead, involve knowledgeable team members from across the business. This helps ensure your policy will address true business needs, include multiple perspectives, and gain support as you implement new rules.

Your AI policy team should include:

• HR: Addresses the policy's impact on employees, hiring, performance management, employee communication, and training.
• IT/Security: Evaluates AI tool security, architecture, and monitors AI use.
• Legal and/or Compliance: Provides guidance on applicable federal, state, local, and international laws and regulations. If you don't have an in-house legal or compliance team, consider engaging outside counsel or consult a trusted HR advisor.
• A Leadership Sponsor: Provides strategic direction and ensures the policy gets prioritized, developed, and enforced.
• An Employee Representative: Offers a ground-level perspective on day-to-day AI use.

Before your first meeting, clarify who owns the draft, who will review, and who provides final approval.

Step 2: Audit Current AI Use Across the Organization

You can't write a policy for tools you don't know about. Start by assessing how AI is already being used by your employees, including any tools they have adopted on their own.

• Survey employees anonymously. Ask which AI tools they use, how often, and for what purposes. Anonymous responses tend to be more candid and complete.
• Inventory AI embedded in existing software. AI features are built into many tools your business may already use. Your CRM, payroll software, email platform, HR platform, design tools, and other applications may include agentic AI or other AI capabilities. These should still be considered, even if no one thinks of them as AI.
• Document use cases by department. Understanding how different teams use AI reveals which risks need to be addressed and where policy guidance is most needed.
• Document what data is entered into AI tools. Identify what data employees are currently using in AI tools to identify if it meets existing data use policies.

Step 3: Identify Your Business's Specific AI Risks and Priorities

Every business faces different risks based on industry, data sensitivity, and how employees use AI. As you prepare to draft the policy, determine which risks matter most for your specific situation.

Key areas to consider include:

• Data Privacy and Security: What types of data does your business handle? Do employees work with customer information, employee records, financial data, or protected health information? The sensitivity of your data will help you determine how strict your policy needs to be.
• Bias and Discrimination: Do employees understand how to recognize and prevent potential bias in AI interactions? Bias can creep into AI outputs when tools are trained with flawed or non-representative data. Proactively address any areas where AI could impact compliance or employment law.
• Intellectual Property: Are employees uploading proprietary content or copyrighted material into AI tools? If the tool is publicly accessible, this information could be compromised.
• Accuracy and Accountability: Where does your business rely on AI-generated information for decision-making? AI outputs should always be reviewed by a human before they're acted on or shared. This is especially critical in high-stakes decisions like hiring, performance management, or financial planning, where errors can carry the greatest risk.
• Customer Trust and Disclosure: If AI use is customer-facing, or used with customer data, are customers aware? Transparency is critical for protecting your business reputation and building trust.

Step 4: Review Applicable Laws and Regulations

The legal landscape for AI is shifting fast. AI use in recruiting, healthcare, finance, and other business applications may be strictly regulated, and those regulations will evolve as technology advances. Before you finalize your policy, review the regulations that apply to your business right now and build in flexibility for what's coming.

The following are examples are illustrative only and not a complete list — consult legal counsel to understand which laws may apply to your business.

• Federal Employment Law: Title VII and the Americans with Disabilities Act (ADA) may apply when AI is used in hiring, promotion, or performance management, even if a human makes the final hiring decision. Employers should consult legal counsel to understand their obligations under federal employment law as it applies to AI.
• State and Local Legislation: Several jurisdictions, including New York City, Illinois, and California, have enacted or proposed laws affecting AI use in hiring and employment decisions, and others are actively exploring legislation in this area. Consult with an expert to check which regulations may apply in each location where you operate. Consider building a process to monitor new developments.
• Data Privacy: In addition to AI specific laws, data privacy laws may also apply where you operate. For example, if your business handles data from California residents, laws like the California Consumer Privacy Act (CCPA) may be worth reviewing with legal counsel. If your business works with data from residents of the European Union (EU) laws like the General Data Protection Regulation (GDPR) and the EU AI Act may govern how this data can be processed, including by AI tools.
• Industry-Specific Requirements: Some industries have specific regulations which may affect AI use. For example, healthcare organizations may want to consider how tools align with frameworks like HIPAA, while financial services firms may want to evaluate AI use against GLBA guidelines.

Step 5: Draft the Policy

With your risk assessment and research complete, you're ready to put the policy in writing. The goal at this stage is to develop a working draft that your team can evaluate.

Keep these principles in mind as you write:

• Follow a structured framework. The components listed later in this article give you a built-in framework for structuring your policy. Work through each one systematically to ensure you cover all the essentials.
• Write in plain language. The policy needs to be understood by every employee, not just technical experts. Avoid jargon and overly technical language, and state every guideline clearly.
• Balance specific guidelines with flexibility. Reference types of AI — such as generative AI or agentic AI — rather than specific vendor names or platforms, which are harder to keep current. Avoid rigid language that will become obsolete the next time you adopt a new platform.
• Restrict use to approved tools. The policy should direct employees to use only employer-approved AI tools and prohibit the use of unauthorized AI applications for work purposes. This closes the door on "shadow AI" — tools employees adopt on their own outside of sanctioned channels.
• Include examples. Concrete examples of acceptable and unacceptable use help readers apply knowledge to their daily work. "Do not enter customer names, contact information, or account details into any AI tool" is easier to implement than a vague instruction to "protect confidential data."
• Align with existing policies. The AI policy shouldn't contradict your IT acceptable use policy, data privacy policy, or code of conduct. Review them side by side before finalizing the draft.

Step 6: Get Stakeholder Input and Legal Review

Before rollout, ask for feedback from the people who will use, implement, and enforce the policy. This pressure-testing helps identify any missing elements or oversights.

Structure your review process in two stages:

• Internal Review: Share the draft with department leads, IT, HR, and a small employee focus group. This helps identify places where the policy doesn't align with daily work. Feedback like "this is too restrictive for our team" or "how does this apply to the AI features already built into our CRM?" helps you make guidelines practical for users.
• Legal Review: Any policy language around hiring, discipline, data privacy, industry compliance, or employee monitoring should have legal review. This is especially true if you operate in states with AI legislation. It may be worth investing in outside counsel with experience in employment or technology law at this stage.
• Get Leadership Sign-off: The policy needs leadership support to ensure consistent implementation. Communications about new policies and guidelines should receive leadership approval.

Step 7: Communicate and Roll Out the Policy

Changes to the way people work should be introduced strategically. Follow change management best practices and communicate intentionally to help people understand and adopt new processes.

• Lead with why before what. Employees who understand the reasoning behind the policy are more likely to follow it. Frame the announcement around responsible AI use and employee protection, not restriction.
• Host an introduction session. An all-hands or team-level meeting gives employees a chance to hear from leadership and ask questions. This signals that the policy is a business priority.
• Provide written reference materials. Give employees a one-page summary or FAQ document they can consult after the initial rollout. Written materials help them keep guidelines front and center without referring to the full policy every time.
• Consider formal acknowledgment. Consider how you'll confirm employees have received and understood the policy — a formal acknowledgment or training completion record can help.
• Equip managers to answer questions. Managers are likely the frontline resource for questions about the policy. Make sure they have the information and talking points to respond consistently or that they know where to send their employees to get clear answers.
• Establish a feedback channel. Give employees a clear, low-barrier way to ask questions or flag concerns after rollout.

Step 8: Train Employees on Responsible AI Use

To implement the new policy consistently, employees need practical training that applies policy language to real decisions they'll face on the job. Without it, even a well-written policy will produce inconsistent results.

• Start with baseline AI literacy for everyone. Employees need to generally understand how AI works, as well as what the risks are, and why the guardrails in your policy exist. Focus on practical, accessible training, not a technical deep dive.
• Add role-specific training for higher-risk functions. AI applications for HR, IT, and customer-facing teams each carry different risks. A hiring manager using AI to screen candidates faces different considerations than a customer service rep using an AI chat tool. Tailor the training to each role and the use cases that may arise or create specialized additional training for certain roles.
• Make it concrete. Walk employees through real scenarios: what to enter into an AI tool, what never to enter, and how to handle an AI output that seems inaccurate. Specific examples are more likely to change behavior than abstract guidance.
• Cover verifying and fact-checking. Employees need to know that AI-generated information should be carefully reviewed before it's acted upon or shared. Build this expectation explicitly into the training, not just the policy.
• Plan for refreshers. AI tools and regulations change quickly. Build in quarterly or biannual touchpoints to keep training current and reinforce the policy over time.

Step 9: Monitor, Audit, and Update the Policy

Publishing the policy is a critical milestone, but it’s not the endpoint. As workplace AI standards and regulations evolve, your policy needs to keep pace. Actively maintaining the policy helps you stay up to date on the technology, regulations, and daily use in your business.

As your organization’s AI use evolves, consider building these maintenance practices into your workflow:

• Review on a regular schedule. Review your AI policy at least once a year. If you frequently add new AI tools or operate in states with active AI legislation, schedule quarterly or biannual reviews to ensure compliance.
• Conduct additional reviews as needed. Don't wait for the scheduled review if something significant changes. New tools, regulatory updates, compliance incidents, or significant shifts in how a team uses AI are all signals to revisit the policy sooner.
• Audit AI use against the policy. Periodically assess whether employees are following the policy guidelines. Take note of what's working, what's being ignored, where improvements can be made, and what additional training is required.
• Monitor the regulatory landscape. Track regulatory updates from state legislatures, industry associations, and federal agencies. The regulatory environment for AI is moving quickly enough that waiting for news to find you could be risky.
• Document changes and communicate them. When the policy is updated, distribute the new version to employees immediately. Include a summary of what changed and why, so employees can quickly implement new guidance.

Key Components of an Effective AI Policy

The right components for your AI policy will depend on your industry, size, and how you use AI. Think of your AI policy as the foundation of a broader AI governance framework; a set of standards that guides how AI is adopted, monitored, and controlled across the business. The checklist below serves as a framework for drafting your policy but always consult with legal counsel to ensure you have included everything needed for your specific business circumstances.

Note: While a review and update process isn't typically included in the employee-facing policy itself, it's an important part of policy governance. Document how often the policy will be reviewed, who owns that process, and how changes will be versioned and communicated. Keep that information accessible to the team responsible for maintaining it.

Common Mistakes To Avoid When Creating an AI Policy

Even well-intentioned AI policies fall short when businesses skip key steps or communicate ineffectively. Here are some of the most common pitfalls.

• Copying a generic template without tailoring it. A template can be a useful starting point, but it’s not enough.
• Writing the policy without input from employees. Your employees know how AI is actually being used in their daily work. Leaving them out of the process can create blind spots, resulting in an impractical document.
• Skipping training and communication after rollout. Publishing the policy is not the same as employees understanding it. Without deliberate training and communication, even a strong policy can fail to produce consistent behavior.
• Treating the policy as a one-time document. AI tools, regulations, and organizational needs are evolving quickly. A policy that isn't maintained will soon be outdated, leaving you open to risks.
• Ignoring shadow AI. Employees may already be using AI tools your policy doesn't cover, sometimes referred to as shadow AI. Your policy should explicitly restrict AI use to employer-approved tools to address this risk directly.
• Failing to align with existing policies. Your AI policy should work with your IT acceptable use, data privacy, confidentiality, and HR policies. It should rely on and incorporate them, not contradict or duplicate them.
• Overlooking vendor AI. AI features are often embedded in software you already use such as your CRM, email platform, HR system, and collaboration tools may all fall under your policy's scope. Include questions about embedded AI in your vendor contract review process.