The AI Governance Checklist for Construction Teams

Your team is using AI. But do you know what data they're putting into it? Who's reviewing the outputs? What happens if something goes wrong?

AI governance sounds like big-company bureaucracy. But even small subcontractors need basic guardrails.

Here's a practical checklist for construction teams.

Why Governance Matters

The Risks

Data exposure: Confidential project information, pricing, client data—where does it go when you paste it into AI?

Quality failures: AI makes mistakes. Who catches them before they become expensive problems?

Compliance issues: Some contracts restrict AI use. Some clients prohibit it. Do you know what you've agreed to?

Liability questions: If AI-assisted work causes a problem, who's responsible?

The Reality

You're probably already using AI. The question isn't whether to allow it—it's how to manage it responsibly.

Good governance protects you without slowing you down.

The Governance Checklist

Section 1: Data Security

What data goes into AI tools?

• Project documents (specs, drawings, contracts)
• Pricing information
• Client information
• Employee information
• Financial data

For each data type, determine:

• Is this allowed?
• Are there restrictions?
• What's the sensitivity level?

Create clear guidelines:

Data Type	AI Use Allowed?	Restrictions
Public specs	Yes	None
Client contracts	Limited	Redact pricing, remove client name
Internal pricing	Yes	Use tools with data protection
Employee data	No	Never upload to AI
Financial data	Limited	Aggregate only, no account numbers

Tool selection criteria:

• What's the data retention policy?
• Is data used to train models?
• What security certifications exist?
• Where is data processed/stored?
• What happens if there's a breach?

Recommended practices:

• Use enterprise versions with data protection
• Avoid consumer/free tiers for sensitive data
• Review tool privacy policies
• Document which tools are approved

Section 2: Contract Compliance

Review your contracts:

• Do any contracts prohibit AI use?
• Do any require disclosure of AI use?
• Are there confidentiality clauses that affect AI?
• Are there technology clauses to review?

Common contract language to watch for:

Confidentiality provisions: "Contractor shall not disclose Confidential Information to any third party..."

AI tools may be considered a "third party" depending on interpretation.

Technology restrictions: "All work shall be performed by qualified personnel..."

Some clients interpret this to exclude AI assistance.

Data handling requirements: "All project data shall be stored only in approved systems..."

Cloud AI tools may not meet these requirements.

When you find restrictions:

• Document the requirement
• Communicate to affected team members
• Create project-specific guidelines
• Track compliance

Disclosure considerations:

• When is disclosure required?
• What level of disclosure is appropriate?
• Who needs to be informed?
• How is disclosure documented?

Section 3: Quality Control

Verification requirements:

• All AI outputs must be reviewed by qualified humans
• Critical items require additional verification
• Errors must be documented and addressed
• Patterns must be tracked and communicated

Define verification levels:

Output Type	Verification Level	Verified By
Bid summaries	Standard	Estimator
Contract analysis	Enhanced	PM + Legal
Technical calcs	Enhanced	Engineer
Meeting notes	Standard	PM
Client communications	High	Manager approval

Error handling:

• How are errors reported?
• Who investigates root causes?
• How are lessons shared?
• When does AI use stop for a process?

Quality metrics:

• Track error rates by process
• Monitor over time
• Set acceptable thresholds
• Review periodically

Section 4: Roles and Responsibilities

Define who does what:

AI Administrator:

• Manages tool access
• Maintains approved tool list
• Reviews new tool requests
• Monitors compliance

Process Owners:

• Define how AI is used in their process
• Create verification requirements
• Train team members
• Monitor quality

Users:

• Follow approved procedures
• Verify outputs
• Report errors
• Suggest improvements

Leadership:

• Set policy
• Allocate resources
• Review performance
• Make risk decisions

Section 5: Training Requirements

Before using AI:

• Tool-specific training completed
• Data security guidelines understood
• Verification requirements understood
• Error reporting process known

Training content:

• What AI can and cannot do
• How to write effective prompts
• When to verify vs. trust
• What data can be used
• How to report problems

Ongoing training:

• Updates when tools change
• Refreshers on policy
• Lessons learned sharing
• New process onboarding

Section 6: Incident Response

If something goes wrong:

Data exposure:

1. Identify what was exposed
2. Assess potential impact
3. Notify affected parties if required
4. Document incident
5. Implement prevention measures

Quality failure:

1. Identify the error
2. Assess impact (was work affected?)
3. Correct the work
4. Document the failure
5. Adjust process to prevent recurrence

Compliance violation:

1. Identify the violation
2. Assess contract/legal implications
3. Notify appropriate parties
4. Remediate if possible
5. Adjust controls to prevent recurrence

Incident documentation:

• Date and time
• What happened
• How it was discovered
• Impact assessment
• Actions taken
• Prevention measures
• Lessons learned

Section 7: Policy Documentation

Document your policies:

AI Use Policy:

• Scope (who, what, when)
• Approved tools
• Data handling rules
• Verification requirements
• Training requirements
• Compliance obligations

Keep it simple:

• One page if possible
• Clear dos and don'ts
• Examples of acceptable use
• Examples of prohibited use
• Where to get help

Communication:

• Policy distributed to all employees
• New employees receive during onboarding
• Annual review and acknowledgment
• Updates communicated promptly

Section 8: Regular Review

Monthly:

• Review any incidents
• Check tool usage patterns
• Address emerging issues

Quarterly:

• Review quality metrics
• Assess new tool requests
• Update training as needed
• Review contract requirements

Annually:

• Full policy review
• Technology assessment
• Risk assessment update
• Training program refresh

Implementation Approach

Start Simple

Don't try to implement everything at once:

Week 1: Data security basics

• Identify what data goes into AI
• Set clear restrictions
• Communicate to team

Week 2: Quality control

• Define verification requirements
• Set up error tracking
• Assign responsibilities

Week 3: Documentation

• Write simple policy
• Distribute to team
• Get acknowledgments

Week 4: Review and adjust

• Address questions
• Refine based on feedback
• Plan ongoing reviews

Scale Appropriately

5-person team: One-page policy, informal processes 25-person team: Documented policy, assigned roles 100-person team: Formal program, dedicated oversight

Match governance to your size and risk.

Common Questions

"Does this apply to personal use?"

If employees use personal AI tools for work tasks, the same data security concerns apply. Either allow it with guidelines or prohibit it and provide company tools.

"What about AI in other software?"

Many tools now include AI features. Apply the same data security and quality control principles regardless of where AI appears.

"How do we enforce this?"

Start with training and communication. Most people will follow reasonable guidelines if they understand the reasons. Address exceptions when they arise.

"What if a client asks about our AI use?"

Be prepared to explain:

• What tools you use
• What data goes into them
• What quality controls exist
• How you verify outputs

Transparency builds trust.

What's Next

Governance protects your company as you adopt AI. The foundation is in place. Now it's time to start using these tools effectively—beginning with the specific workflows that will have the most impact on your business.

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TL;DR

• AI governance isn't just for big companies—every contractor needs basic guardrails
• Focus on four areas: data security, contract compliance, quality control, and roles
• Document simple policies—one page is better than none
• Train everyone before they use AI tools
• Review regularly and adjust based on what you learn