Ducab

A successful first Global Dialogue on AI Governance would deliver three concrete outcomes for the business community particularly industries like supply chain where AI is already making high-stakes, real-world decisions. First, a shared framework for AI risk classification. Not all AI risks are equal. Autonomous routing decisions, supplier risk scoring, and demand forecasting each carry different risk profiles and failure consequences. The Dialogue should produce agreed principles for categorizing AI applications by their potential for harm, so that businesses operating across borders have a consistent baseline to work from rather than navigating a patchwork of conflicting national standards. Second, meaningful commitments on transparency and explainability. Supply chain AI systems regularly make consequential decisions flagging suppliers, rerouting shipments, predicting disruptions yet the logic behind these decisions is often opaque, even to the businesses deploying them. The Dialogue should advance clear expectations that AI systems used in critical operations provide auditable, interpretable outputs. This protects businesses, workers, and the communities that depend on resilient supply chains. Third, the beginning of regulatory interoperability. Global supply chains cross dozens of jurisdictions. Fragmented AI regulations create compliance burdens that fall hardest on mid-size businesses and disadvantage companies in developing economies. A successful Dialogue would lay groundwork for mutual recognition of AI governance standards across regions, reducing friction while maintaining protection. Above all, success means the Dialogue produces outcomes that are practical and implementable not just principles. The private sector is ready to engage, but needs clear, consistent, and globally coordinated guidance to deploy AI responsibly at scale.

• Safe, secure and trustworthy AI
• Interoperability of governance approaches
• Transparency, accountability, and human oversight
• Social, economic, ethical, cultural, linguistic and technical implications of AI

As a business operating in global supply chains, our priority selections reflect the practical realities of deploying AI in a complex, cross-border, high-stakes environment. Safe, secure and trustworthy AI is our foremost concern. Supply chains are critical infrastructure disruptions have cascading effects on economies and communities. AI systems that forecast demand, assess supplier risk, or automate logistics decisions must be reliable, robust, and secure. A single failure or manipulation of an AI-driven supply chain decision can trigger significant financial, humanitarian, and reputational consequences. Transparency, accountability, and human oversight is equally urgent. When AI flags a supplier as high-risk or reroutes a shipment, human operators need to understand why and have the ability to intervene. Without explainability and clear accountability structures, businesses cannot responsibly deploy AI, and workers cannot meaningfully oversee systems that affect their roles and livelihoods. Interoperability of governance approaches is a practical necessity for any globally operating business. Our supply chains span multiple jurisdictions, each developing its own AI regulations at different speeds. Without interoperability, compliance becomes a fragmented, costly burden one that disadvantages smaller suppliers and businesses in developing economies disproportionately. Harmonized standards would enable responsible AI adoption at scale. Social, economic, ethical, and technical implications of AI rounds out our priorities because the human dimension of supply chain AI cannot be ignored. Automation is reshaping workforces, procurement decisions carry ethical weight, and AI systems trained on biased data can entrench inequities across supplier networks globally. Governance frameworks must account for these broader impacts, not just technical performance metrics.

From a supply chain perspective, several cross-cutting issues deserve explicit attention in the AI Dialogue. AI in critical infrastructure and systemic risk. Global supply chains are deeply interconnected. When AI systems across multiple companies are trained on similar data or built on the same underlying models, they can produce correlated failures meaning many actors make the same flawed decision simultaneously. This systemic dimension of AI risk is not well captured by frameworks focused on individual systems or actors, and warrants dedicated attention at the international level. Third-party and vendor AI risk. Businesses rarely build AI systems from scratch. Most deploy AI embedded in software provided by third-party vendors, meaning the end-user has limited visibility into how the model was built, what data it was trained on, or how it may behave under novel conditions. International governance frameworks must address accountability across the full AI supply chain not just the developer or the end-user in isolation. Environmental impact. The computational demands of large AI systems carry a significant and growing carbon footprint. For industries like logistics and supply chain, which are already under pressure to decarbonize, the energy costs of AI adoption are a material concern. Governance frameworks should require transparency on the environmental costs of AI systems, enabling businesses to make informed procurement decisions. Speed of deployment vs. speed of governance. AI capabilities are being embedded into commercial supply chain software faster than governance frameworks can respond. The Dialogue should consider mechanisms such as regulatory sandboxes or rapid-response expert panels that allow governance to keep pace with technological change without stifling innovation.

The governance gaps in our selected thematic areas are having tangible, immediate effects on the supply chain sector creating both significant challenges and underutilized opportunities. Challenges: The absence of harmonized AI governance standards is already fragmenting how businesses operate across borders. Companies must simultaneously comply with the EU AI Act, emerging national frameworks in Asia, and a largely unregulated environment in other regions. For supply chain businesses managing supplier networks across dozens of countries, this creates duplicative compliance costs, legal uncertainty, and an uneven playing field where less-regulated actors can deploy AI more aggressively and less responsibly. The lack of transparency and explainability requirements is also creating accountability vacuums. When an AI system incorrectly flags a supplier as high-risk, or generates a flawed demand forecast that leads to shortages, there is often no clear mechanism for understanding what went wrong, who is responsible, or how to prevent recurrence. This erodes trust in AI adoption and exposes businesses to unquantifiable legal and reputational risk. Workforce disruption is accelerating faster than governance can respond. AI-driven automation is reshaping roles across logistics, procurement, and warehouse operations often without adequate frameworks for retraining, transition support, or worker consultation. Opportunities: These same governance gaps represent an opportunity for the international community to establish standards that actively enable responsible AI adoption. Clear, interoperable rules would give businesses the confidence to invest more deeply in AI accelerating productivity gains, supply chain resilience, and sustainability outcomes. The supply chain sector is also well-positioned to serve as a governance testbed. Its global, multi-stakeholder, data-intensive nature makes it an ideal domain for piloting transparency requirements, risk classification frameworks, and cross-border accountability mechanisms.

The AI Dialogue occupies a unique and necessary position in the international governance landscape one that no existing forum fully fills. Its potential to advance international cooperation is significant, provided it is designed for action rather than deliberation alone. Bridging the fragmentation gap. The most immediate value the AI Dialogue can offer is serving as a convergence point for the proliferating array of national and regional AI governance initiatives. The OECD, G7, G20, EU, and African Union are each developing frameworks in relative isolation. The Dialogue by virtue of its universal membership is the only forum capable of facilitating genuine interoperability between these efforts. It should prioritize mapping existing frameworks, identifying areas of convergence, and brokering mutual recognition agreements. Giving the private sector a structured voice. Businesses are the primary deployers of AI, yet they are largely absent from intergovernmental governance processes. The Dialogue should establish a formal, ongoing mechanism for private sector input — not as an afterthought, but as a structural feature. Industry perspectives are essential for ensuring that governance frameworks are technically informed, operationally feasible, and reflective of real-world deployment conditions. Building trust between developed and developing economies. The AI divide is not merely a capacity issue it is a trust issue. Many developing nations rightly worry that AI governance frameworks will be shaped by and for technologically advanced economies, locking in existing inequities. The Dialogue must actively center the voices and priorities of the Global South, ensuring that governance outcomes serve all economies equitably. Establishing accountability for commitments. Dialogue without follow-through is insufficient. The AI Dialogue should develop a light but meaningful mechanism for tracking whether commitments made in Geneva translate into action at the national and sectoral level.

Several existing initiatives provide a strong foundation that the AI Dialogue should deliberately connect with rather than duplicate. OECD AI Principles and Policy Observatory. The OECD has developed the most widely adopted international AI principles and maintains a comprehensive database of national AI policies. The Dialogue should build directly on this work using the Observatory as a shared evidence base and avoiding the creation of parallel taxonomies or principle sets that add confusion rather than clarity. ISO/IEC technical standards. International standards bodies are already developing technical standards for AI risk management, transparency, and robustness. The Dialogue should actively reference and reinforce these efforts, helping governments understand that technical standardization and policy governance are complementary rather than competing tracks. The EU AI Act. As the most comprehensive binding AI regulatory framework currently in force, the EU AI Act represents a de facto global reference point for many businesses. The Dialogue should engage directly with its implementation identifying which elements could inform international norms and where its approach may need adaptation for different economic and legal contexts. WTO and trade frameworks. AI governance has significant trade dimensions data flows, algorithmic discrimination, and regulatory barriers all affect market access. The Dialogue should establish a working relationship with WTO processes to ensure AI governance does not inadvertently create new trade barriers, particularly for businesses in developing economies. Industry-led initiatives. Frameworks such as the Partnership on AI and sector-specific responsible AI commitments represent genuine private sector investment in governance. The Dialogue should formally recognize and engage these efforts, creating a bridge between voluntary industry commitments and emerging intergovernmental expectations. The added value the Dialogue brings to all of these is universality the ability to bring every country to the table and translate diverse frameworks into genuinely global cooperation.

The AI Dialogue's legitimacy and effectiveness will depend heavily on whether its format genuinely enables meaningful participation from all stakeholders not just governments and large technology companies. Private sector engagement. Businesses should have a structured, permanent role in the Dialogue not limited to side events or observer status. We recommend establishing a formal Business and Industry Advisory Track with dedicated sessions where industry can present real-world implementation challenges, emerging risks, and technical insights directly to government delegates. This is particularly important for sectors like supply chain, where AI deployment is already advanced but governance engagement has been limited. Civil society and academic inclusion. Independent researchers and civil society organizations provide essential checks on both government and industry perspectives. The Dialogue should reserve dedicated speaking time and working group seats for these voices, with financial support mechanisms to ensure participation is not limited to well-resourced organizations from wealthy countries. Structured thematic working groups. Plenary sessions alone are insufficient for the technical depth this Dialogue requires. We recommend establishing standing working groups organized around the core thematic areas safety, transparency, interoperability, and capacity-building that meet intersessionally and feed concrete recommendations into the annual Geneva and New York convenings. Regional preparatory processes. To ensure the Dialogue reflects genuinely diverse perspectives, regional consultations should be held in advance of each annual meeting particularly in Africa, Latin America, and Southeast Asia with their outputs formally integrated into the Dialogue agenda. Accessible participation mechanisms. Remote participation, multilingual interpretation beyond the six UN languages where feasible, and plain-language summaries of outputs would significantly broaden meaningful engagement, particularly for smaller businesses and stakeholders in developing economies.

Global AI governance discussions remain heavily skewed toward a narrow set of voices predominantly large technology companies, governments of wealthy nations, and English-speaking academic institutions. Several communities remain critically underrepresented. Small and medium-sized enterprises (SMEs). SMEs constitute the backbone of global supply chains and are increasingly deploying or subject to AI systems, yet they are almost entirely absent from international governance conversations. They lack the resources to monitor, engage with, or respond to emerging regulations. The Dialogue should create dedicated SME consultation mechanisms including simplified input processes, translated materials, and outreach through existing business associations and chambers of commerce. Workers and trade unions. The workers most directly affected by AI-driven automation — warehouse operatives, logistics coordinators, procurement staff have virtually no voice in governance processes. Yet their lived experience of algorithmic management, automated decision-making, and workforce displacement is irreplaceable evidence for policymakers. Formal labor representation should be embedded in the Dialogue's structure, not treated as optional. Global South governments and businesses. Many developing nations lack the technical capacity or diplomatic bandwidth to engage meaningfully in complex AI governance negotiations. This risks producing frameworks that entrench the interests of technologically advanced economies. Dedicated capacity-building support, simplified participation pathways, and explicit agenda space for Global South priorities are essential. Indigenous and local communities. These groups are often subject to AI systems in resource extraction, land use, border management, and public services without any participation in how those systems are governed. Their perspectives on data sovereignty, cultural representation, and community consent are largely absent from international discussions. The research informing AI governance is overwhelmingly published in English, creating blind spots around risks, use cases, and governance approaches relevant to other linguistic and cultural contexts. The Dialogue should actively commission and translate research from underrepresented regions.

Traditional intergovernmental meeting formats formal plenary sessions, prepared statements, and panel discussions are poorly suited to the dynamic, technical, and fast-moving nature of AI governance. The AI Dialogue should experiment with innovative engagement formats that prioritize genuine exchange over performative participation. Live scenario and simulation exercises. Rather than debating AI governance in the abstract, delegates and stakeholders should work through concrete, realistic scenarios a cross-border supply chain disruption caused by a faulty AI system, an algorithmic procurement decision that disadvantages suppliers from developing economies, or a cybersecurity breach in an AI-powered logistics network. Scenario-based exercises surface practical governance gaps far more effectively than position statements. Red team sessions. Dedicated sessions where participants are tasked with identifying weaknesses, loopholes, and unintended consequences in proposed governance frameworks would strengthen outputs considerably. Drawing on practices from cybersecurity and policy stress-testing, red teaming builds more resilient governance by anticipating failure modes before they occur. Reverse panels. Instead of experts presenting to audiences, structured formats where government delegates and business representatives are questioned by affected communities workers, SME operators, civil society from the Global South would rebalance power dynamics and surface perspectives that traditional panels miss. Continuous digital engagement between sessions. A moderated online platform allowing stakeholders to submit evidence, respond to draft outputs, and flag emerging issues between annual meetings would transform the Dialogue from a periodic event into a living governance process. This is particularly valuable for SMEs and stakeholders who cannot attend in person. Rapid-response working groups. Given the pace of AI development, the Dialogue should have a mechanism to convene focused expert groups between scheduled sessions when significant new developments a major AI incident, a breakthrough capability, a new national regulation demand urgent collective attention.

Governance Policies, Practices, and Platforms Industry self-governance has emerged as a significant complement to state regulation in addressing AI governance challenges, offering flexibility, technical expertise, and the capacity for rapid iteration that formal legislative processes often lack. Voluntary Commitments and Codes of Conduct: In 2023, major AI developers including Google DeepMind, OpenAI, and Anthropic made voluntary commitments to the U.S. government covering safety testing, information-sharing on risk, and investment in cybersecurity. While critiqued for lacking enforcement mechanisms, such commitments establish baseline norms and create reputational accountability within the industry. Model Cards and Datasheets: Pioneered by researchers at Google, model cards provide standardized documentation of a system's intended use, performance benchmarks, and known limitations. As an industry-adopted transparency practice, they enable downstream users and researchers to make informed deployment decisions, embedding accountability into the model release process itself. Frontier Safety Frameworks: Anthropic's Responsible Scaling Policy and Google DeepMind's Frontier Safety Framework represent attempts by developers to self-impose capability thresholds that trigger enhanced safety evaluations before further development proceeds. These structured, evidence-based commitments reflect a more institutionalized form of self-governance grounded in technical risk assessment.