AI Governance for Small Businesses: A Practical Framework

Most AI governance frameworks are written for Fortune 500 companies with dedicated compliance teams and legal departments. If you run a 15-person company or a 200-person mid-market firm, those frameworks are about as useful as a 747 flight manual when you are learning to fly a Cessna.

But ignoring AI governance entirely is not the answer either. Every week brings a new story about a company leaking customer data through ChatGPT, generating biased outputs that damage their brand, or discovering that their AI vendor was training on their proprietary data. These are not hypothetical risks. They are happening right now, to companies of every size.

We work with small and mid-sized businesses across Michigan that are adopting AI tools at a rapid pace. The ones that succeed long-term are not the ones that adopt fastest. They are the ones that adopt thoughtfully, with clear guardrails that protect the business without strangling innovation.

Here is the practical AI governance framework we recommend, scaled specifically for small businesses.

Why AI Governance Matters Even for a 10-Person Team

"We are too small for governance" is something we hear constantly. Here is why that thinking is dangerous.

Data exposure is data exposure, regardless of company size. If one employee pastes customer records into a public AI tool, you have a data breach. It does not matter whether you have 10 employees or 10,000. The regulatory consequences, the customer trust damage, and the remediation costs hit small companies harder because you have fewer resources to absorb the blow.

Your clients care. If you serve enterprise clients, government agencies, or healthcare organizations, they are increasingly asking vendors about their AI policies. Not having one is becoming a disqualifier in RFP processes. We have seen Michigan companies lose contracts specifically because they could not articulate their AI data handling practices.

Your employees are already using AI. A 2025 Salesforce survey found that over 50% of employees use AI tools at work, and nearly half of those do so without any company guidance. That means your data, your customer information, and your proprietary processes are flowing through tools you have not vetted, under terms you have not reviewed.

Liability is real. If your team uses AI to generate content that infringes on copyright, produce analysis that leads to discriminatory outcomes, or make recommendations that cause financial harm, your company bears the liability. "The AI did it" is not a legal defense.

The good news: AI governance for a small business does not need to be a 200-page document. It needs to be clear, practical, and actually followed.

The 5-Component AI Governance Framework

We have distilled enterprise governance frameworks down to five components that every small business can implement in a week or less.

Component 1: Acceptable Use Policy

This is the foundation. It answers one question: what are our people allowed to use AI for, and what is off-limits?

What to include:

Approved tools list. Name the specific AI tools employees may use. ChatGPT Enterprise, Claude, Microsoft Copilot, whatever you have vetted. Everything else requires approval.
Approved use cases. Be specific. Drafting internal emails, generating marketing copy, summarizing meeting notes, writing code scaffolding, brainstorming ideas, and so on.
Prohibited uses. Equally specific. Never input customer PII, financial records, trade secrets, employee records, legal documents, or health information into any AI tool without explicit approval.
Human review requirement. All AI-generated output that goes to a client, gets published externally, or informs a business decision must be reviewed by a qualified human before use.
Attribution expectations. When and how to disclose AI assistance. This varies by industry and client expectations.

Keep it to two pages maximum. If your people will not read it, it does not exist.

Component 2: Data Classification and Handling Rules

Not all data carries the same risk. Your governance framework needs to define what can go where.

A simple three-tier system works for most small businesses:

Green (Public/Low Risk): Published content, general industry information, non-proprietary processes. Can be used freely with approved AI tools.
Yellow (Internal/Medium Risk): Internal communications, non-sensitive business data, general financial information, anonymized customer data. Can be used with approved enterprise-tier AI tools that have data protection agreements in place.
Red (Confidential/High Risk): Customer PII, financial records, health data, trade secrets, legal matters, employee records. Never input into any external AI tool. Period.

This classification does not need to be complex. Print it on a laminated card and put it on every desk. The goal is that when an employee is about to paste something into an AI tool, they pause for three seconds and ask: "Is this green, yellow, or red?"

Component 3: Vendor Evaluation Criteria

When someone on your team wants to adopt a new AI tool, you need a consistent way to evaluate it. Here is a checklist that takes about 30 minutes per vendor:

Data handling:

Does the vendor use your inputs to train their models? (Check the terms of service, not the marketing page.)
Where is data stored? Is it encrypted at rest and in transit?
What is their data retention policy? Can you delete your data?
Do they offer a Data Processing Agreement (DPA)?

Security:

SOC 2 compliance or equivalent?
What authentication options exist? (SSO, MFA)
What is their breach notification policy?

Output quality and reliability:

What are the known limitations?
How do they handle hallucinations or incorrect outputs?
What is the uptime SLA?

Compliance:

Does it meet your industry-specific requirements? (HIPAA, GDPR, PCI-DSS, etc.)
What jurisdiction governs disputes?

Score each vendor on these criteria before approving. You do not need a perfect score. You need informed decisions with documented trade-offs.

Component 4: Monitoring and Review

A policy that is never revisited is a policy that decays. Build in lightweight monitoring:

Monthly (15 minutes):

Review which AI tools are being used across the company.
Check for any new tools that have been adopted without going through evaluation.
Note any incidents, near-misses, or concerns raised by staff.

Quarterly (1 hour):

Review the approved tools list. Remove tools no longer in use, add newly vetted ones.
Update the acceptable use policy based on new use cases that have emerged.
Check vendor terms of service for changes. (Vendors update these frequently and quietly.)
Assess whether the data classification tiers need adjustment.

Annually (half day):

Full policy review and update.
Staff refresher training.
Vendor re-evaluation for all active tools.
Review of any incidents from the past year and lessons learned.

Assign one person to own this process. In a small company, it is often the CEO, COO, or a senior manager. It does not need to be a dedicated role. It needs to be an explicit responsibility assigned to someone specific.

Component 5: Incident Response Plan

When something goes wrong with AI -- and eventually something will -- you need a plan that does not start and end with panic.

Define what constitutes an AI incident:

Customer data entered into an unapproved tool
AI-generated output sent to a client that contained errors, hallucinations, or biased content
Discovery that a vendor changed their data practices
AI-generated content that infringes on copyright or creates legal exposure

The response process (keep it simple):

Contain. Stop using the tool in question immediately. Revoke access if needed.
Assess. What data was exposed? Who is affected? What is the potential harm?
Notify. Inform affected parties as required by law and as dictated by good business practice.
Remediate. Fix the immediate problem. Delete data from the vendor if possible. Correct any erroneous outputs.
Learn. Update the policy to prevent recurrence. Share the lesson (not the blame) with the team.

Write this plan down. Practice it once. When you need it, you will be glad you did.

Template Policy Outline You Can Adapt

Here is a one-page outline you can use as a starting point for your own AI governance policy:

Section 1: Purpose and Scope

Why this policy exists
Who it applies to (all employees, contractors, interns)
Effective date and review schedule

Section 2: Approved AI Tools

List of vetted and approved tools
Process for requesting new tool approval

Section 3: Acceptable Use

Approved use cases
Prohibited uses
Human review requirements

Section 4: Data Handling

Data classification tiers (Green/Yellow/Red)
Rules for each tier
Specific examples relevant to your business

Section 5: Vendor Management

Evaluation criteria for new AI tools
Re-evaluation schedule for existing tools

Section 6: Monitoring and Compliance

Who is responsible
Review cadence
How to report concerns

Section 7: Incident Response

Definition of an AI incident
Response steps
Notification requirements

Section 8: Training

Initial training requirement for all staff
Ongoing training schedule
Resources available

This entire document should be 5-10 pages. If it is longer than that, you have made it too complex for a small business to actually follow.

Common Mistakes We See

After helping dozens of small businesses implement AI tools, these are the governance mistakes that come up most often:

Mistake 1: Banning AI Entirely

Some leaders respond to AI risk by prohibiting all AI use. This does not work. Your employees will use AI anyway, just without guardrails. A clear policy that permits controlled use is safer than a blanket ban that drives usage underground.

Mistake 2: Copying Enterprise Policies

We have seen small businesses adopt 80-page AI governance documents from large corporations. Nobody reads them, nobody follows them, and they create a false sense of security. Your policy must match your company size, complexity, and risk profile.

Mistake 3: Set-and-Forget

AI tools change monthly. Vendor terms change. New capabilities create new risks. A policy written in January may be outdated by June. Build in the review cadence described above.

Mistake 4: Ignoring Shadow AI

If you do not provide approved AI tools, your employees will find their own. The free tier of ChatGPT, random browser extensions, unvetted SaaS tools with AI features. Proactively providing and paying for approved tools is cheaper than cleaning up a data incident.

Mistake 5: No Training

A policy without training is just a document. Every employee needs to understand not just what the rules are, but why they exist. A 30-minute training session when the policy launches, plus annual refreshers, makes the difference between compliance on paper and compliance in practice.

Getting Started This Week

You do not need to build everything at once. Here is a realistic one-week timeline for a small business:

Day 1: Draft the acceptable use policy (Section 3) and data classification tiers (Section 4). These are the highest-impact components.

Day 2-3: Inventory the AI tools your team is currently using. Evaluate each against the vendor criteria. Approve or sunset them.

Day 4: Write the incident response plan. Keep it to one page.

Day 5: Combine everything into a single document. Have your leadership team review it.

Week 2: Roll it out with a 30-minute all-hands training. Set the calendar reminders for monthly, quarterly, and annual reviews.

We Can Help

If you want help building an AI governance framework tailored to your business, or if you need AI consulting that goes beyond just the technology to include the policies and practices that make AI adoption sustainable, we would be glad to talk. We work with small and mid-sized businesses across Michigan to implement AI responsibly and effectively.

You can also explore our free Data Storytelling training module to see how we approach practical, hands-on business education, or reach out directly to discuss your governance needs.