Most small and mid-size companies are now using AI tools in some form. Copilot is writing emails, ChatGPT is drafting proposals, and no one is quite sure where the organization's data is going. The question most leaders eventually face is whether they need a formal policy before something goes wrong. This article covers what actually belongs in an AI governance policy built for a company without a compliance team, and how to structure one that employees will use, as part of our broader guidance on AI and Copilot adoption for mid-market organizations.
Here's the honest answer.
An AI governance policy is a set of organizational rules that defines how employees are permitted to use artificial intelligence tools, who is responsible for managing AI-related risks, and how the organization will stay current as those tools change. That definition sounds corporate. The reality is simpler: it's a document that answers the questions your employees are already asking, before they make a decision you didn't anticipate.
Companies without a compliance team tend to fall into one of two traps. Either they write nothing and trust that employees will use common sense, or they copy an enterprise-grade policy off the internet and end up with something so unwieldy that no one reads it. Neither approach actually protects the organization.
The middle path is a policy built for your company's actual size and risk profile. A 150-person professional services firm has different AI exposure than a Fortune 500 manufacturer. Your policy doesn't need a governance committee, a legal review board, and a biannual compliance audit. It needs clear answers to a few basic questions: what tools are approved, what data can go into them, who decides when something new is added, and what happens when something goes wrong.
If your organization is still assessing where AI fits in its operations, the AI readiness work at Seven Roots is a useful starting point before drafting any policy.
Most AI governance policies, regardless of company size, need to address four areas. Not twenty. Four.
Acceptable use. Which tools are employees permitted to use for work tasks, and which are off limits? This section doesn't need to list every AI product by name; that list will be out of date next quarter. Instead, define what categories of work are appropriate for AI assistance and which are not. Writing assistance, research, and summarization are generally low-risk. Anything involving client data, financial information, or legal documents requires a higher bar of scrutiny and should be addressed explicitly.
Data handling. What kind of company or client information can and cannot be entered into an AI tool? This is where the policy earns its keep. If an employee pastes a client contract into a public AI system to generate a summary, that data has left your environment. Your policy needs to address this directly, not assume employees will figure it out on their own.
Vendor approval. How does a new AI tool get approved for use? Without a simple process here, employees either avoid useful tools entirely or adopt them without anyone knowing.
Review process. Who is responsible for keeping the policy current, and when will it be reviewed? Once a year is a reasonable minimum. After any significant AI-related incident in your industry, revisit it sooner.
The reason most SMBs end up with either nothing or an over-engineered mess is that the available templates are built for enterprises. They assume a dedicated privacy officer, a legal team that can write policy language, and a governance committee that meets quarterly to review AI tool usage across the organization. None of that applies to a 200-person company.
Here's what actually matters at your scale.
| Enterprise requirement | SMB-appropriate equivalent |
|---|---|
| Dedicated AI governance committee | One designated policy owner (ops, HR, or a senior leader) |
| Formal legal review of every AI tool | A short vendor checklist: data retention, where data is processed, opt-out of training |
| Annual third-party compliance audit | Annual internal review by the policy owner |
| Tiered risk classification framework | Simple approved / restricted / prohibited list |
| Employee certification and training program | One-page summary distributed at onboarding and when the policy changes |
The goal isn't minimal effort. It's a policy your organization can actually maintain. An enterprise-grade document that gets reviewed once and filed away offers less real protection than a shorter policy that gets updated and enforced.
Every policy needs an owner, and AI governance is no exception. But AI policy ownership is frequently misassigned, often to HR, because it involves employee behavior. HR is the wrong choice to own this alone.
HR can and should be involved in communicating the policy, handling violations through the disciplinary process, and embedding AI guidelines into onboarding. But the substantive decisions in an AI governance policy, including which tools are approved, what data controls are required, and how vendors are evaluated, require someone who understands the technology well enough to make those calls. That's a technology, operations, or senior leadership function, not a generalist HR responsibility.
At a company without a CTO or CIO, the most practical model is shared ownership. A senior operations leader or COO owns the business risk side. The person responsible for technology systems, whether that's an internal manager, a managed service provider, or a fractional technology leader, owns the vendor evaluation and tooling decisions. HR handles the people side: communication, training, and enforcement.
In practice, your AI policy should name a specific individual as the accountable owner, with a clear mandate to review and update it at least annually. Policies that list a department rather than a person tend to get reviewed by no one.
The hardest part of AI governance isn't writing the policy. It's keeping it current in an environment where the tools are changing faster than annual review cycles can track.
A few practices help. First, build a lightweight notification mechanism into the policy itself: if any employee encounters an AI-related situation the policy doesn't address, there should be a simple way to flag it. This turns your team into an early warning system rather than a source of undocumented workarounds. Second, tie your review schedule to real events, not just the calendar. A major release from a tool your employees use, a public AI incident in your industry, or a new regulatory development are all reasons to review sooner rather than waiting for the annual date.
Third, treat your policy as a working document, not a compliance checkbox. The organizations that get AI governance right approach it as an ongoing conversation between leadership, technology, and the people doing the work, not a document written once and stored away.
If you're working through your organization's broader AI adoption approach, Heartwood offers structured advisory for mid-market leaders on exactly these questions, from policy foundations to evaluating specific AI tools for your environment. It's the same advisory thinking covered in our guide to AI and Copilot adoption for mid-market companies, applied to your specific situation.
Yes, and Copilot is actually a useful illustration of why. Microsoft 365 Copilot has access to your email, documents, calendar, and SharePoint data. How your employees prompt it, what they copy from it, and how outputs get used in client-facing work are all governance questions that Microsoft's default terms don't answer for you. A policy doesn't need to be long. It needs to clarify what data can flow through the tool, who is accountable for outputs, and what the process is when something looks wrong.
The policy owner should be whoever has decision-making authority over your organization's technology and operations, typically a COO, VP of Operations, or a fractional technology leader. HR should be involved in communication and enforcement, but not as the sole owner. The person who owns the policy needs enough technology context to evaluate AI tools, assess vendor data practices, and make judgment calls when employees encounter situations the policy doesn't explicitly address. Naming a department rather than a person is a reliable way to ensure the policy never gets updated.
Your general acceptable use policy covers baseline behaviors: don't install unauthorized software, don't share passwords, use company systems for work purposes. An AI policy addresses a different class of questions. It covers what happens to your data when it enters a third-party AI system, how AI-generated content should be reviewed before use, and how employees should handle situations where AI output is wrong or misleading. These are operational and judgment questions, not just security hygiene. A general acceptable use policy doesn't address them.
AI policy violations should run through your standard disciplinary process. The policy should specify severity: accidentally using a non-approved tool for a low-risk task is a different situation from entering client data into a public AI system. Your first response in most cases should be correction and coaching. Most early violations come from employees who genuinely didn't know the rule existed, which is as much a signal about how well the policy was communicated as it is about individual behavior. Build that assumption into how you respond.
At minimum, once a year. In practice, the AI landscape is moving fast enough that you should also review whenever a major new tool enters wide use at your company, after any significant AI-related incident in your industry, or when you see regulatory or legal developments that affect how AI tools can be used with client data. Build a lightweight process: the policy owner reviews it, notes what changed, distributes the update, and documents that it happened. The entire process can take an afternoon.
Heartwood is an AI advisory panel for mid-market executives who need on-demand technology strategy guidance. Start with your toughest question.
Try Heartwood free