AI policy template for companies: Examples and best practices

With daily AI use among individual contributors jumping 23 percentage points in just one year, and 44% of HR leaders reporting that junior roles are being replaced by AI, the adoption of these tools is absolutely rapid.* 

That may be exciting for efficiency but it also heightens the risk of serious governance failure.

Therefore, you need policies in place to protect your organization and a framework for effective adoption. However, formalizing AI use for data security, accuracy, and compliance also risks killing productivity.

This guide provides a practical AI policy template and the framework to implement it successfully, enabling teams to experiment with AI at scale while protecting their data and reputation.

In this guide, you'll learn:

• How to build an AI acceptable use policy that employees actually follow
• What to include in your AI policy template to cover security, compliance, and quality
• How to align your policy with ISO 42001 and NIST AI RMF
• Who should own AI policy implementation across IT, legal, HR, and business units
• How to make AI governance sustainable through training, feedback, and monitoring
  
  

Get your free AI policy template

Use our customizable template to align with internationally recognized standards on approved tools, data handling, human oversight, and role-based training protocols.

👉 Download template

*Leapsome 2026 Workforce Trends Report
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Why companies need an AI policy template now

Generative AI tools are already embedded in your workflows, whether you've formalized their use or not. The question isn't whether employees are using ChatGPT, Claude, or similar platforms, but whether they are using these safely and correctly. 

Without clear AI usage guidelines, you're operating with governance gaps that expose your organization to data leaks, compliance violations, and reputational damage.

An AI policy template creates the structure that lets teams experiment confidently while protecting what matters most: 

• Your data
• Your quality standards
• Your AI in HR strategy

Unstructured AI use creates risk at scale

When employees lack clarity on AI tool usage policy, well-intentioned actions can lead to serious consequences.

Common scenarios that create exposure:

• Someone pastes customer data into a chatbot to draft a response faster
• A team member uploads proprietary financial projections to generate a presentation
• Employees share confidential strategic plans with AI tools to create meeting summaries

These governance failures demonstrate that, without documented AI-policy guidelines, your organization has no mechanism to prevent sensitive information from entering third-party systems.

And this risk compounds with scale. The more people using AI without guidance, the higher the probability of a breach or compliance incident that could have been avoided with clear boundaries.
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Most employees aren't sure what's allowed

The gap between what HR leaders think exists and what employees actually experience is significant.

According to Leapsome's 2026 Workforce Trends Report:

• 76% of HR leaders believe their company has a clear AI policy and reliable guardrails
• Only 48% of individual contributors agree

That's a 28-percentage-point perception gap, which signals systemic confusion.

When employees don't know the rules, they either avoid using AI entirely (limiting productivity) or use it without proper safeguards (creating risk). Both outcomes hurt your business. An AI policy template bridges that gap by giving everyone the same playbook. 
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Policies support safe innovation, not just restriction

A well-designed AI usage policy template creates psychological safety for experimentation.

What effective policies provide:

• Approved tools and clear use cases
• Guidance on when to use AI vs. when to rely on human judgment
• Accountability structures for output validation
• Escalation paths when employees encounter gray areas

Research shows that when teams understand where the guardrails are, they're more likely to adopt new technologies productively. Your policy becomes an enabler, not a barrier. It tells employees "yes, and here's how" instead of "no, and here's why." 

Companies that approach AI governance as a growth strategy rather than a compliance checkbox see higher adoption rates, better quality outputs, and fewer incidents requiring remediation.

"Lack of speed isn't the real risk. People readiness is. When ambition outpaces readiness, trust breaks. People start to feel left out rather than brought along. Bold leadership isn't about simply racing ahead. It's about translating urgency into understanding and giving your teams clarity, context, and space to learn."

— Jenny Podewils, Co-CEO at Leapsome

What an AI policy template should achieve beyond compliance

The most effective AI usage policy templates start with enablement.

According to Leapsome's 2025 HR Insights Report, nearly two-thirds of HR leaders are struggling to integrate AI into their workflows in an ethical manner. The real challenge isn't just compliance. It's creating an environment where employees feel confident using AI tools responsibly.

A responsible AI policy serves as both a guardrail and a growth accelerator, ensuring outputs meet your quality standards while building the psychological safety teams need to experiment. 

This approach transforms AI governance policy from a reactive compliance measure into a proactive framework that supports AI upskilling and sustainable adoption.

👀 Essential best practice:

Specificity is critical to effective governance.

For example, a policy that says "use AI responsibly" creates no clarity. A policy that says "ChatGPT is approved for internal brainstorming and draft creation, but all outputs must be reviewed by a subject matter expert before external use" gives employees actionable guidance.

When people understand the boundaries, they stop wasting time second-guessing themselves.

That clarity directly impacts productivity. Employees can confidently integrate AI into their workflows without constantly checking with managers or compliance teams about whether their approach is acceptable.

Clarify ownership, not just tool restrictions

Too many AI policies focus exclusively on what employees can't do. The better approach defines who owns what throughout the AI workflow.

And, since ownership spans multiple stages, your ownership model needs to establish accountability without creating bottlenecks. When each stakeholder understands their specific responsibility in the AI workflow, work proceeds smoothly.

For example, when using AI to draft job descriptions:

• The hiring manager defines the job description and the necessary requirements for the role. 
• The recruiter ensures the language aligns with company values and removes potential bias.
• The HR manager reviews the final version for legal requirements before posting.

Clear ownership also reduces the risk of AI outputs slipping through without proper review, which is where most quality and compliance issues emerge.
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Inside the AI policy template: the backbone of safe usage

A strong AI policy should act as a practical operating manual that employees can reference when they're unsure whether their planned AI use is appropriate. It should balance being comprehensive with clarity, and address the most common risk scenarios without overwhelming readers with legal jargon.

Use the following checklist as you write or customize your own policy documents.
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What to include in a defensible, future-ready policy

• Scope of tools and permitted use cases
  Distinguish between approved tools (like your organization's enterprise ChatGPT account) and banned tools (consumer AI apps without business agreements).
  Define acceptable use cases explicitly.
  AI can draft internal meeting summaries but cannot generate final client deliverables without human review and oversight.
  
  

• Human oversight and escalation protocols
  Require that all AI-generated content be reviewed by a qualified human before it reaches external audiences.
  Specify who counts as a qualified reviewer for different content types.
  Include clear escalation paths for edge cases where employees aren't sure if their use requires review.
  
  

• Rules for handling sensitive or regulated data
  Prohibit entering personally identifiable information, financial data, intellectual property, health records, or any regulated data into generative AI tools.
  Make this restriction prominent and provide examples of what counts as sensitive data in your industry context.
  Educate employees to understand that data entered into AI systems may be used for model training or stored indefinitely.
  
  

• Accuracy review and accountability
  Assign clear responsibility for verifying that AI outputs are factually accurate and contextually appropriate.
  Specify that the person using AI owns the accuracy of the output, not the tool.
  For high-stakes applications, such as performance reviews or compensation decisions, consider adding a second reviewer to the workflow.
  
  

• Prompting guidelines and output ownership
  Provide concrete examples of effective versus problematic prompts.
  Clarify that your organization owns all work product created using company-provided AI tools.
  Employees cannot claim personal IP rights to AI-generated content created during work.
  
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• Role-based training requirements and literacy thresholdsRequire employees to complete AI literacy training before using AI tools for high-stakes work, such as core HR processes, client communications, or financial analysis. Consider different training levels based on use case complexity.
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Why align your AI policy with global standards now

Just like AI adoption, AI regulations are evolving fast. The EU AI Act is already reshaping how global companies approach AI governance, even for US-based teams. Meanwhile, US regulations are emerging piecemeal, with state-level laws and sector-specific requirements appearing rapidly.

This is why there’s a strong case for building your policy around established frameworks now. Two internationally recognized standards provide the foundation most organizations need: ISO 42001 and the NIST AI Risk Management Framework (RMF).

ISO 42001 is a management system standard specifically designed for AI governance. It defines how to classify AI systems by risk level, establish governance structures with clear roles, and implement controls across the AI lifecycle from development through deployment.

NIST AI RMF is a US-focused framework for building trustworthy AI systems. It emphasizes mapping risks to specific AI characteristics, measuring system performance against defined metrics, and managing those risks through technical and organizational controls.

Both frameworks have been tested across thousands of organizations. Aligning with these standards now means you're building governance that can adapt when new laws emerge. Instead of rebuilding your entire policy, you can update specific controls to accommodate new requirements.
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The practical benefits of using international AI frameworks

Together, these frameworks give you a common language for discussing AI governance with auditors, regulators, and business leaders.

Aligning with established standards reduces duplicative work. At the same time, organizations can implement structured approaches to risk classification, lifecycle management, and control implementation without building everything from scratch.

Meanwhile, standardization can also increase employee trust as, by following industry best practices rather than arbitrary internal rules, you can show your team you're taking governance seriously.

For companies with global operations or enterprise teams managing complex AI deployments, these frameworks provide tested approaches that adapt as regulations evolve.
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Who should own AI policy implementation, and why it can’t just be IT or Legal

Most AI policy documents start in IT or legal departments. That makes sense for initial drafting, but long-term success depends on cross-functional ownership, with HR playing a central role.

IT can define technical controls. Legal can outline compliance requirements. However, neither department possesses the necessary relationships, trust, or change management expertise to effectively implement an AI policy example in practice. 

Successful implementation requires bridging the gap between what the policy says and how employees actually work. That's where HR becomes essential, transforming an AI policy document into a living part of your organization's culture and connecting it to broader HR strategy planning.
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Assign policy ownership by lifecycle stage

Effective AI governance requires clear ownership at every stage of the process. Here's how you can often break it down across your organization with a structure that prevents bottlenecks:

• IT owns technical implementation: they define approved tools, configure security protocols, and manage system integrations.
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• Legal owns risk and compliance: they draft compliant language, review regulatory exposure, and ensure the policy aligns with employment law and data privacy regulations.
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• HR owns the AI adoption framework and training: they socialize the policy, deliver employee education training, answer daily questions, and monitor the effectiveness of the policy in practice.
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• Business unit leaders own validation: they ensure policy requirements actually work for their teams' workflows and flag friction points that need adjustment.
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• Compliance oversees auditing: they track adherence, investigate violations, and report on the effectiveness of policies.
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With this approach, each department has a clearly defined role that doesn’t overlap with that of another team.

IT doesn't field employee questions about why certain tools are banned — HR handles that through targeted communication. Legal doesn't monitor daily compliance — managers do that through regular check-ins.
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HR is the trust engine and change agent

HR has direct access to employees at every level and the relationships needed to make change stick. People leaders bridge the gap between executive AI strategy and employee reality, ensuring decisions are transparent and grounded in actual work needs.

But trust isn't automatic.  

As you can see in our 2026 Workforce Trends Report, 81% of HR leaders believe they successfully advocate for employees, while only 54% of individual contributors agree. Leading AI policy rollout with transparency and empathy rebuilds that trust.

HR answers the questions that matter most to employees, like "How does this change my daily work?" By translating technical requirements into practical guidance, HR addresses job security concerns and creates feedback channels that surface problems before they become resistant.

This helps you reframe AI policy as a growth opportunity, positioning responsible AI use as a valuable skill, and so shifting the conversation from compliance to professional growth.

"For me, AI skills aren't about mastering prompts or knowing the latest tools. It's about how people think, learn, and adapt. When I test for 'AI skills,' I'm really testing for the human capabilities that help people thrive in a world shaped by AI."

— Jenny Podewils, Co-CEO at Leapsome

Real governance starts with ongoing oversight

Your AI policy document only works if employees understand it and if you monitor its effectiveness in practice. Static policies are susceptible to being ignored and becoming outdated, so effective risk management requires continuous review, reporting, and iteration.
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✅ Make training role-specific and scalable: Someone drafting meeting notes with AI needs different guidance than someone analyzing engagement data. Platforms like Leapsome Learning support AI upskilling that evolves with your tools.
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✅ Use feedback loops to surface problems early: Regular surveys reveal where employees are confused or where the policy creates friction. Leapsome Surveys flag issues before they become security incidents, and Leapsome AI summarizes patterns for faster action.
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✅ Tie policy compliance to development conversations: When managers discuss responsible AI use during performance reviews or 1:1s, it signals importance. When learning paths include AI literacy tied to role progression, employees see capability building, not compliance.
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✅ Track incidents to improve your policy: When someone accidentally shares confidential data with an AI tool, use it as a learning moment. Review whether the training was clear, the restrictions made sense, or if the escalation processes need adjustment to close the AI skills gap.
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AI policy isn't a document you write once and file away. Real governance lives in how your team uses AI daily, how managers reinforce responsible practices, and how quickly you adapt when something goes wrong. Effective policies evolve with your organization, updating based on what actually happens in practice.

Get your free AI policy template

Use our customizable template to align with internationally recognized standards on approved tools, data handling, human oversight, and role-based training protocols.

👉 Download template

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FAQs about AI policy templates

How often should an AI policy be reviewed or updated? 

Review your AI policy at least quarterly during the first year of implementation, then consider moving to biannual reviews once processes stabilize. Monitor employee feedback continuously through surveys to identify friction points that need policy adjustments before your scheduled review cycle.
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What are examples of tools or platforms covered in an AI tool usage policy? 

Most AI policies address generative AI platforms like ChatGPT, Claude, Gemini, and Microsoft Copilot. They also cover AI-powered features in everyday tools like Microsoft 365, Google Workspace, Salesforce, and Slack. Your policy should distinguish between enterprise versions with data protection agreements and consumer versions without business safeguards.
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How do I train employees to follow our AI policy guidelines? 

Start with onboarding training that covers policy basics, approved tools, and data handling rules. Then provide role-specific training based on how different teams use AI. Use real workplace scenarios wherever possible. Platforms like Leapsome Learning let you create learning paths tied to job functions and track completion, which you can reinforce through manager check-ins during 1:1s.
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How do I ensure human oversight in AI tool usage at work? 

Define clear approval workflows for AI-generated content before it reaches external audiences or influences decisions. Assign qualified reviewers based on content type: managers review performance summaries, legal reviews contracts, compliance reviews regulated communications. Build oversight into your processes by requiring human sign-off in project management tools.