Your company is using AI. Maybe you've invested deliberately. Maybe it's crept in through SaaS tools and employee experiments. Either way, AI is now a board-level topic.
Not because boards need to understand transformer architectures or prompt engineering. Because boards need to understand risks and oversight.
Here's what matters at the governance level.
AI Is Already in Your Company
Even if you haven't launched an "AI initiative," AI is present:
In your software. Most enterprise SaaS tools have added AI features. Your CRM, your productivity suite, your customer service platform - they're all incorporating AI capabilities.
In your employees' work. People use ChatGPT, Copilot, and other AI tools to draft emails, summarize documents, write code, and analyze data. Some of this is sanctioned. Much isn't.
In your vendors' systems. Your partners, suppliers, and service providers are using AI in their operations. Their AI decisions affect your data and outcomes.
The governance question isn't whether to allow AI. It's how to oversee AI that's already happening.
Three Governance Domains
Board-level AI governance covers three areas:
Data Use
What data feeds AI systems? Where does that data go?
Customer data might be processed by AI tools. What permissions exist? What agreements govern this use? Are customers aware?
Employee data might train or inform AI systems. What are the privacy implications? What disclosures are required?
Business data might flow to AI vendors. What confidentiality protections exist? What prevents misuse?
The governance question: Do we know what data flows through AI systems, and is that data use appropriate?
Decision Accountability
When AI influences decisions, who is accountable?
Customer-affecting decisions - pricing, credit, service levels - might incorporate AI recommendations. Who owns those decisions? What recourse exists if they're wrong?
Employee-affecting decisions - hiring, performance, compensation - might use AI tools. What protections prevent bias? What transparency is required?
Business decisions - investments, strategy, operations - might rely on AI analysis. How is AI input validated? What happens when AI is wrong?
The governance question: Is accountability clear when AI influences important decisions?
Operational Controls
How is AI use managed day-to-day?
Access controls determine who can deploy AI systems and with what authority.
Monitoring ensures AI systems operate as intended and flags anomalies.
Change management governs how AI systems are updated, modified, or retired.
Incident response handles AI failures, errors, and misuse.
The governance question: Are operational controls appropriate for the AI systems in use?
The Inventory Problem
You can't govern what you can't see.
Most companies don't have a complete inventory of AI use. Corporate IT knows about approved enterprise AI tools. They don't know about:
- Department-level AI subscriptions purchased on credit cards
- AI features activated within existing software
- Employee use of free AI tools for work tasks
- Vendor AI use that touches company data
The first governance action is visibility. Survey the organization. Identify where AI is used, what data it touches, and what decisions it affects.
This isn't a one-time exercise. AI adoption accelerates. The inventory needs ongoing maintenance.
Risk Categories
Board members should understand the risk categories:
Regulatory risk. AI use may trigger regulatory requirements. Healthcare AI has FDA implications. Employment AI has discrimination law implications. Financial AI has fair lending implications. The regulatory landscape is evolving rapidly.
Reputational risk. AI failures become news. Biased algorithms, incorrect decisions, privacy breaches - these incidents damage reputation quickly. The association of "AI" with any negative event amplifies media attention.
Operational risk. AI systems can fail, behave unexpectedly, or produce errors at scale. Unlike human errors that happen one at a time, AI errors can repeat thousands of times before detection.
Strategic risk. Competitors using AI effectively gain advantages. Under-investment in AI capabilities creates competitive exposure.
Liability risk. When AI causes harm, legal responsibility questions arise. Product liability, negligence, and contractual frameworks are still evolving for AI-specific situations.
What Boards Should Ask
Questions for management:
Inventory: "Do we have a complete view of AI use across the organization, including shadow IT and vendor AI?"
Policies: "What policies govern AI use? How are they enforced?"
Risk assessment: "What AI-related risks have been identified? How are they being managed?"
Accountability: "Who is accountable for AI governance? What authority do they have?"
Incident history: "Have we had AI-related incidents? How were they handled?"
Regulatory exposure: "What AI-related regulations apply to us? How are we preparing?"
Competitive position: "How does our AI capability compare to competitors? What are we missing?"
These questions should surface on the agenda periodically. AI isn't a one-time agenda item. It's an ongoing governance topic.
Governance Structures
Some organizations assign AI governance to:
The CTO or CIO - Makes sense when AI is primarily a technology implementation question.
The Chief Risk Officer - Makes sense when AI risk management is the primary concern.
A dedicated AI governance role - Emerging in larger organizations with significant AI operations.
A cross-functional committee - Combines technology, risk, legal, and business perspectives.
The right structure depends on organizational size and AI maturity. The important thing is that someone owns the topic with appropriate authority and visibility.
Board Competency
Boards don't need AI experts. They need enough understanding to ask good questions and evaluate answers.
Basic literacy matters. Board members should understand what AI can and can't do at a conceptual level. They don't need to understand the technology. They need to understand the capabilities and limitations.
Risk perspective matters. Board members should understand AI risks in the same way they understand other operational and strategic risks.
Governance perspective matters. AI governance isn't fundamentally different from other governance. It's oversight, accountability, and control applied to a specific domain.
Some boards add AI-specific expertise through:
- Board education sessions
- Advisory board members with AI background
- Consultant support for specific questions
- Management presentations on AI strategy and risks
The Minimum Viable Governance
Every board should ensure:
- An inventory exists of AI use across the organization
- Policies exist governing AI use, data, and decisions
- Someone is accountable for AI governance with appropriate authority
- Risk assessment includes AI-specific risks
- Regular reporting brings AI topics to board attention
This is table stakes. The starting point, not the destination.
From there, governance matures based on AI complexity and organizational risk tolerance.
The Opportunity Frame
AI governance isn't just about preventing bad outcomes. It's about enabling good ones.
Proper governance creates confidence to move faster. When oversight is clear, risk appetite increases appropriately. Organizations can invest in AI capabilities without undue concern.
The alternative - AI use without governance - creates hidden risks and limits strategic options.
Governance isn't the enemy of AI adoption. It's what makes responsible AI adoption possible.
That's the frame for board discussion. Not "how do we prevent AI?" but "how do we govern AI so we can use it confidently?"
That's a conversation worth having at the board level.