AI Training for Board Members: Building Governance Capability

Most board members didn't rise through AI-focused careers. Yet they're now expected to provide oversight of AI systems that affect customers, employees, and business outcomes. This gap isn't a personal failing—it's a structural challenge that requires a structured solution.

This guide provides a practical framework for building AI governance capability at the board level.

Executive Summary

• The AI literacy gap is a governance risk — Directors who don't understand AI basics can't effectively challenge management or identify emerging risks
• Focus on oversight, not technical depth — Board members need to govern AI, not build it; training should emphasize governance concepts
• Three-tier curriculum — Foundation (AI basics), Governance (oversight skills), and Advanced (emerging topics) levels serve different needs
• Multiple delivery formats — Briefings, workshops, site visits, and peer discussions each have a role
• Assessment drives improvement — Track knowledge gains and identify gaps through structured assessment
• Continuous learning is mandatory — AI evolves rapidly; one-time training becomes outdated quickly
• Training supports fiduciary duty — Demonstrable AI education shows reasonable care in oversight

Why This Matters Now

Regulatory Expectation. Singapore's MAS guidelines reference board competence in AI oversight. The expectation is growing that directors can meaningfully engage with AI governance topics. (/insights/ai-compliance-financial-services-mas-guidelines)

Fiduciary Duty. Directors bear responsibility for material risks. If AI creates material risk and the board lacks the capability to oversee it, there's a duty gap. Training helps close it. (/insights/ai-board-oversight-directors-guide)

Effective Challenge. The board's role includes challenging management. Without AI understanding, directors either accept management assertions uncritically (rubber stamping) or resist all AI initiatives (obstruction). Neither serves the organization. (/insights/ai-board-questions-management)

Board Composition Evolution. While adding AI-expert directors is valuable, it's not sufficient. All directors benefit from AI literacy, just as all directors benefit from financial literacy regardless of background.

Definitions and Scope

Board AI training focuses on governance capability, not technical proficiency. The goal is enabling directors to:

• Understand what AI is and what it can (and cannot) do
• Recognize AI risks and their business implications
• Ask informed questions about AI strategy and risk
• Evaluate whether management has appropriate AI governance
• Contribute to AI policy and ethical discussions

What training is NOT:

• Technical deep dives into algorithms
• Coding or model building skills
• Preparation for hands-on AI work

Target audience: All board members, with additional depth for Risk Committee, Audit Committee, or designated AI Committee members.

Training Curriculum Framework

Tier 1: Foundation (All Directors)

Objective: Establish baseline AI literacy for effective governance participation.

Duration: 2-3 hours initial, with annual refresher

Topics:

Assessment: Short quiz to confirm comprehension; discussion to surface questions.

Tier 2: Governance (Committee Members)

Objective: Develop deeper capability for directors with specific AI oversight responsibilities.

Duration: 4-6 hours, plus ongoing engagement

Prerequisites: Tier 1 completion

Topics:

Assessment: Governance scenario exercise; committee effectiveness self-assessment.

(/insights/ai-risk-assessment-framework-templates) (/insights/ai-policy-essential-components) (/insights/ai-incident-response-plan)

Tier 3: Advanced (Specialized Development)

Objective: Develop expertise for directors seeking deeper engagement or considering AI committee leadership.

Duration: Ongoing; 8-12 hours annually

Prerequisites: Tier 2 completion plus demonstrated interest

Topics:

Assessment: Contribution to board AI strategy discussion; peer feedback.

(/insights/responsible-ai-principles-practice) (/insights/ai-regulations-2026-business-guide)

Delivery Methods

Executive Briefings

Format: 30-60 minute presentations from experts (internal or external)

Best for: Tier 1 content, regulatory updates, emerging topic introductions

Tips:

• Schedule within existing board meetings to maximize attendance
• Provide pre-reading to enable informed questions
• Leave time for discussion—passive listening has limited value
• Record for directors who miss the session

Workshops

Format: 2-4 hour interactive sessions with exercises

Best for: Tier 2 governance skills, policy review, scenario planning

Tips:

• Keep group size small (8-12 maximum) for participation
• Use real (anonymized) case studies from your organization
• Include hands-on exercises with actual documents
• Professional facilitation improves outcomes

Site Visits and Demonstrations

Format: Visit AI teams, see systems in action, interact with practitioners

Best for: Making AI tangible, understanding operational reality

Tips:

• Structure the visit with clear learning objectives
• Include demonstrations of actual AI systems
• Meet the teams who build and operate AI
• Follow up with Q&A session at board level

Peer Discussions

Format: Informal exchange with directors from other organizations

Best for: Comparing approaches, learning from peer experiences

Self-Directed Learning

Format: Online courses, reading materials, industry reports

Best for: Foundation concepts, ongoing updates, individual pace

Step-by-Step Implementation Guide

Phase 1: Assessment (Weeks 1-2)

1. Survey current capability — Distribute AI literacy assessment, identify gaps, determine preferences
2. Define requirements — Map governance responsibilities to knowledge needs

Phase 2: Curriculum Design (Weeks 3-4)

1. Map content to needs — Match topics to responsibilities, sequence appropriately
2. Select formats and providers — Choose delivery methods, identify trainers, set budget

Phase 3: Delivery (Ongoing)

1. Launch foundation training — Schedule Tier 1 for all directors
2. Roll out governance training — Deliver Tier 2 to committee members
3. Enable advanced development — Offer Tier 3 for interested directors

Phase 4: Assessment and Iteration (Quarterly)

1. Measure progress — Conduct assessments, gather feedback
2. Refine curriculum — Update content, add emerging topics (/insights/ai-training-for-executives)

Common Failure Modes

One-and-Done Training. Single session without follow-up becomes quickly outdated. Fix: Annual refresher plus ongoing updates.

Too Technical. Training that focuses on algorithms rather than governance alienates non-technical directors. Fix: Governance-focused curriculum with technical concepts explained simply.

Passive Consumption. Directors listen but don't engage; knowledge doesn't stick. Fix: Interactive formats, discussions, exercises.

Generic Content. Off-the-shelf training that doesn't address your industry or organization. Fix: Customize with sector-specific cases and organizational examples.

No Assessment. Training delivered but effectiveness not measured. Fix: Pre/post assessments, governance scenario exercises.

Opt-Out Culture. Attendance treated as optional; some directors never participate. Fix: Chair expectation setting, integrated with board calendar, tracked participation.

Board AI Training Checklist

Planning:

• AI literacy assessment completed for all directors
• Training requirements defined based on governance responsibilities
• Curriculum mapped to three tiers
• Delivery methods and providers selected
• Budget approved
• Training integrated with board calendar

Tier 1 Foundation:

• All directors completed foundation training
• Comprehension assessment passed
• Annual refresher scheduled

Tier 2 Governance:

• Committee members completed governance training
• Governance scenario exercise completed
• Ongoing topic updates scheduled

Continuous Learning:

• Curated resource list available
• Quarterly AI updates scheduled
• Peer learning opportunities identified
• Training effectiveness reviewed annually

Metrics to Track

Participation:

• Training completion rate (target: 100% for Tier 1)
• Average hours of AI training per director annually
• Attendance at optional sessions

Learning:

• Pre/post assessment score improvement
• Self-reported confidence in AI topics
• Quality of AI-related questions in board meetings

Governance Outcomes:

• Board engagement in AI agenda items
• Quality of AI risk challenge
• Audit findings on board AI capability

Tooling Suggestions

Learning Management: Track training completion and assessments. Many board portals include learning features.

Content Library: Curated collection of AI governance resources—reports, articles, videos. Keep current and organized.

Assessment Tools: Simple surveys or quizzes to measure learning. Avoid making this burdensome.

Calendar Integration: Schedule training in board calendar with appropriate notice and pre-reading.

Frequently Asked Questions

Ready to Build Board AI Capability?

Effective AI governance requires informed directors. Training is an investment in better oversight.

Book an AI Readiness Audit to assess your current governance capability and receive a tailored board development plan.

[Contact Pertama Partners →]

References

1. Institute of Directors Singapore. (2024). "Director Development in the AI Era."
2. NACD. (2024). "AI Literacy for Directors: A Competency Framework."
3. World Economic Forum. (2024). "Board AI Skills Development Guide."
4. Deloitte. (2024). "Director Education in Technology Governance."
5. MIT Sloan. (2024). "Executive AI Education: What Works."