ThinkAhead Advisory

A successful first Global Dialogue on AI Governance should mark a clear shift from principles to implementation. It should strengthen alignment on leadership accountability, build implementation capacity across regions, and improve interoperability between governance approaches. Equally important is advancing transparency, accountability, and trust as foundations for responsible AI. If the Dialogue translates global discussions into practical, coordinated action, it will represent a meaningful step toward effective and inclusive AI governance.

• Safe, secure and trustworthy AI
• Protection and promotion of human rights
• Transparency, accountability, and human oversight
• Interoperability of governance approaches

My selection reflects a governance-focused perspective on artificial intelligence, grounded in leadership, accountability, and implementation. Safe, secure and trustworthy AI is essential to ensure reliability, resilience, and sustained public confidence in AI systems. Interoperability of governance approaches is critical in a global environment, enabling alignment across jurisdictions, reducing fragmentation, and supporting responsible scaling of AI solutions. Protection and promotion of human rights must remain central, ensuring that technological progress does not come at the expense of fairness, dignity, and inclusion. Finally, transparency, accountability, and human oversight are key to responsible deployment, particularly as AI increasingly influences strategic and operational decisions.

Two cross-cutting issues deserve greater attention. First, the need to ensure protection of vulnerable groups, particularly women and children. AI systems can reinforce existing biases, expose individuals to harm, or enable misuse if not properly governed. Strong safeguards, inclusive design, and accountability mechanisms are essential to ensure that AI advances do not come at the expense of safety, dignity, and rights. Second, the importance of positioning AI as an enabler of human capability, not a replacement for human work. Governance approaches should promote augmentation rather than substitution, supporting workforce transition, skills development, and responsible integration of AI into organizations. Addressing these issues will be critical to ensuring that AI contributes to inclusive, human-centered development.

Governance gaps are most visible in implementation capacity and regulatory fragmentation. Many organizations lack the structures and expertise needed for responsible AI deployment, while differing frameworks create uncertainty and slow adoption. At the same time, expectations around transparency and accountability are increasing, raising both operational and reputational risks. These challenges also create opportunities to strengthen governance capabilities, improve competitiveness, and align more closely with international standards, supporting more responsible and sustainable AI adoption.

The AI Dialogue can act as a platform for alignment and practical cooperation, helping reduce fragmentation across governance approaches. By facilitating knowledge-sharing, capacity-building, and cross-sector exchange, it can support more coordinated and implementable AI governance globally.

The Dialogue should build on initiatives such as the OECD AI Principles, UNESCO Recommendation on AI Ethics, and regional regulatory frameworks. Its added value lies in connecting these efforts, promoting interoperability, and translating principles into practical, globally applicable governance approaches.

Stakeholders can contribute through policy input, practical experience, and research insights. A mix of plenary sessions and small, thematic working groups would support more meaningful exchange, with a focus on actionable outcomes and follow-up collaboration.

Underrepresented voices include emerging economies, SMEs, and diverse leadership groups, including women. They can be included through targeted outreach, regional consultations, and ongoing engagement mechanisms, not only one-off participation.

Effective formats include small group discussions, case-based sessions, and interactive digital tools. These approaches support practical exchange, broader participation, and more dynamic engagement.

Effective AI governance is emerging where policy frameworks are combined with real implementation mechanisms. Examples include the EU AI Act, which brings a risk-based, operational approach, and global frameworks such as the OECD AI Principles and UNESCO Recommendation, which provide common direction. At organizational level, the most effective practices are those that embed AI governance into leadership structures - such as board oversight, clear accountability, and integrated risk management. In addition, regulatory sandboxes and cross-sector partnerships are proving valuable in testing solutions and translating principles into practice. These examples highlight that the key is not only defining rules, but ensuring they can be applied, monitored, and scaled in real-world contexts.