Date: March 19-23,2026
Duration: 5 Days
Venue: Bangkok, Thailand
Course Objectives
By the end of this program, participants will be able to:
- Understand AI fundamentals and emerging technologies relevant to public sector leadership, policy formulation, and service delivery.
- Apply AI strategically to improve decision-making, planning, and performance across ministries, departments, SOEs, and public agencies.
- Identify and manage AI-related risks, including ethical, legal, cybersecurity, data privacy, and operational risks in government environments.
- Design AI-enabled public value initiatives that enhance efficiency, transparency, citizen trust, and service outcomes.
- Strengthen AI governance frameworks aligned with public accountability, regulatory requirements, and national digital strategies.
- Evaluate real-world AI use cases in public administration, policy implementation, healthcare, finance, infrastructure, and citizen services.
- Lead organizational change and workforce readiness for AI adoption while managing resistance, skills gaps, and institutional culture.
- Develop an executive-level AI action plan tailored to participants’ organizations, ensuring responsible, scalable, and sustainable implementation.
Personal Impacts:
After completing the program, participants will gain:
- Enhanced strategic confidence in leading AI initiatives and engaging with technical teams, consultants, and policymakers.
- Improved decision-making capability through data-driven insights, predictive analytics, and AI-supported scenario planning.
- Stronger risk awareness and ethical judgment when approving or overseeing AI-enabled public programs.
- Future-ready leadership skills, enabling executives to remain relevant in rapidly evolving digital governance environments.
- Practical executive tools and frameworks to translate AI concepts into actionable policies and programs.
Organizational Impacts:
Participating organizations will benefit from:
- More efficient and responsive public services through AI-enabled process optimization and service delivery models.
- Improved governance, compliance, and accountability in AI adoption aligned with public sector standards and regulations.
- Reduced operational and reputational risks through structured AI risk management and ethical oversight mechanisms.
- Increased public value and citizen trust by leveraging AI transparently, responsibly, and inclusively.
- A clear, executable AI roadmap supporting national digital transformation goals and institutional modernization.
Course Outline:
BLOCK I: AI Foundations for Public-Sector Leadership (Modules 1–3)
Module 1: AI Demystified for Senior Officials
Purpose: Confidence-building, not hype
- What AI is (and is not) in a government context
- AI vs Automation vs Digitization
- Generative AI, Predictive AI, Decision-Support AI
- Common myths and unrealistic expectations
Executive takeaway: Where AI genuinely helps leadership—and where it doesn’t.
Module 2: Global AI Trends in Government & SOEs
- How governments and SOEs are using AI
- AI in regulation, public services, utilities, finance, transport
- Lessons from global successes and public-sector failures
- Why many governments AI projects stall
Executive takeaway: What to copy, what to avoid.
Module 3: AI-Augmented Executive Decision-Making
- AI as a decision-support tool, not a decision-maker
- Human judgment, accountability, and explainability
- Avoiding “black box” dependence
- When executives must override AI
Executive takeaway: How to remain accountable in an AI-enabled organization.
BLOCK II: Organizational Readiness & Ethics (Modules 4–6)
Module 4: Organizational Readiness for AI in Government Companies
- Leadership mindset and culture
- Skills: what executives must understand vs delegate
- Data maturity and system readiness
- Typical bottlenecks in government entities
Module 5: Ethical AI & Responsible Public Leadership
- Bias, fairness, transparency
- Citizen trust and reputational risk
- Ethical failures in public-sector AI
- Executive responsibility and moral authority
Module 6: Data Governance, Sovereignty & Security
- Data ownership and stewardship
- Cross-border data risks
- Vendor access to sensitive data
- Cybersecurity and AI-enabled threats
BLOCK III: Strategy, Use Cases & Transformation (Modules 7–10)
Module 7: Aligning AI with Mandate, Policy & Strategy
- AI as an enabler of organizational mandate
- Linking AI to national policies and strategic plans
- Avoiding “technology-first” thinking
- Designing a realistic AI roadmap
Module 8: Identifying & Prioritizing AI Use Cases
- High-impact vs high-risk use cases
- Service delivery, compliance, operations, analytics
- Quick wins vs structural transformation
- Portfolio approach for public entities
Module 9: AI in Operations, Services & Citizen Value
- Process optimization and efficiency
- Predictive maintenance, demand forecasting
- Customer/citizen intelligence
- Productivity gains without workforce alienation
Module 10: AI Strategy Case Studies (Public Sector Focus)
- Successful government & SOE implementations
- Why many AI projects fail in the public sector
- Executive-level lessons learned
BLOCK IV: Risk, Governance & Control (Modules 11–14)
Module 11: AI Risk Landscape for Government Companies
- Strategic, operational, legal, reputational risks
- Model risk and data risk
- Vendor dependency risks
Module 12: AI Governance & Oversight Structures
- Board and senior management roles
- AI policies, steering committees, escalation paths
- Alignment with corporate and public governance principles
Module 13: Regulation, Compliance & Audit Readiness
- Global AI regulations and standards (high-level)
- Data privacy and sectoral compliance
- Preparing for audits, inquiries, and investigations
Module 14: AI Failures, Crises & Executive Response
- AI errors and public backlash
- Incident response frameworks
- Media, ministerial, and parliamentary accountability
- Decision-making under pressure
BLOCK V: Execution, Tools & the Future (Modules 15–18)
Module 15: AI Investment & Procurement Decisions
- Build vs buy vs partner
- Vendor evaluation and due diligence
- Managing consultants and technology providers
- Avoiding lock-in and over-dependence
Module 16: Measuring Value & Performance from AI
- Beyond ROI: efficiency, quality, risk reduction
- AI KPIs for senior management
- Monitoring benefits realization
Module 17: Executive Tools & Practical Demonstrations
Hands-on (non-technical):
- ChatGPT / Perplexity for executive research
- Microsoft Copilot / Claude / Gemini
- Gamma App for strategy & presentations
- Custom GPTs for internal use (conceptual)
Module 18: Executive Capstone & Action Plan
- Designing a responsible AI roadmap
- Governance and risk checklist
- Personal leadership commitments
- Organizational next steps
Key Takeaways for Participants:
- Clear executive-level understanding of AI
- Ability to strategically evaluate AI opportunities and risks
- Strong grasp of AI governance and regulatory expectations
- Practical frameworks to assess AI’s impact on public valuation
- Confidence to lead AI-driven transformation at board and C-suite level