Vikas Sharma, Independent Advisory

A successful first Global Dialogue on AI Governance would move beyond principles into operational convergence. Three outcomes matter. First, shared minimum baselines. Not uniform regulation, but a small set of globally recognized guardrails around safety, accountability, and human oversight that different jurisdictions can map to. Without this, fragmentation will outpace governance. Second, implementation pathways, not just intent. Many frameworks remain aspirational because they do not translate into enterprise or public sector execution. The Dialogue should surface reference architectures, audit approaches, and lifecycle controls that make governance actionable across varied maturity levels. Third, inclusive capacity enablement. The gap is not only regulatory, it is institutional. Emerging economies, public institutions, and smaller enterprises need access to tools, skills, and governance patterns to participate meaningfully, otherwise AI advantage concentrates quickly. A pragmatic success signal would be a living collaboration mechanism, not a one time outcome, where governments, industry, and practitioners co-evolve standards, share incident learnings, and refine governance in step with technology. In essence, success lies in shifting from "what good looks like" to "how it gets done, consistently, across contexts."

• Safe, secure and trustworthy AI
• AI capacity-building
• Interoperability of governance approaches
• Transparency, accountability, and human oversight

These priorities reflect where the gap between intent and execution is most visible. Safe, secure and trustworthy AI is foundational, but current approaches are uneven and often reactive. Without common expectations, risk scales faster than control. Transparency, accountability, and human oversight are the mechanisms that make safety real. Governance fails not at policy definition, but at traceability, explainability, and enforceability across the AI lifecycle. Interoperability of governance approaches is critical in a multi jurisdictional world. Enterprises and public systems operate across borders, yet governance remains fragmented. Without alignment layers, compliance becomes inefficient and innovation slows. AI capacity building underpins all of the above. Governance cannot be imported as a static model. It requires local capability, institutional readiness, and contextual adaptation. Without this, even well designed frameworks remain underutilized. Together, these areas shift the conversation from high level principles to scalable, implementable governance that can operate across sectors and geographies.

A few cross cutting issues merit more explicit attention. First, operationalization of AI governance within enterprise systems. Most risks emerge not from models in isolation, but from how they integrate into business processes, data pipelines, and decision workflows. Governance needs to address system level behavior, not just model level controls. Second, auditability and continuous assurance. Static certification approaches are insufficient for adaptive systems. There is a need for continuous monitoring, dynamic risk scoring, and audit mechanisms that evolve with model behavior. Third, economic concentration and dependency risk. AI capabilities are increasingly concentrated among a small set of providers. This creates systemic dependencies that have implications for resilience, competition, and sovereign capability. Fourth, human capital displacement and augmentation asymmetry. The pace of capability deployment is outstripping workforce adaptation, creating uneven impacts across sectors and geographies. Finally, governance of open and closed model ecosystems in parallel. Both are advancing rapidly, with different risk and innovation profiles. Policymaking needs to account for this dual track reality rather than treating AI as a uniform category. Addressing these cross cutting themes would help ground governance in how AI is actually built, deployed, and scaled.

In the Indian context, and more broadly across emerging digital economies, governance gaps are less about absence of intent and more about uneven operationalization. A key challenge is the asymmetry between rapid AI adoption and institutional readiness. Enterprises are integrating AI into customer, risk, and operational systems faster than governance frameworks can be embedded into lifecycle controls, auditability, and accountability structures. This creates exposure not at the model level alone, but at the system level where decisions scale. Second, fragmentation of approaches across sectors and jurisdictions introduces compliance complexity. Organizations operating across borders face overlapping, and sometimes conflicting, expectations, while domestic ecosystems are still evolving coherent, interoperable standards. Third, capacity constraints remain significant. Governance requires skilled practitioners, tooling, and organizational maturity. Outside large enterprises, many institutions lack the ability to translate principles into implementable controls, leading to either under adoption or unmanaged risk. At the same time, the opportunity is substantial. India has the potential to leapfrog into operational governance models by embedding trust, accountability, and transparency directly into digital public infrastructure and enterprise modernization efforts. The scale of digital platforms and public systems provides a unique testbed for governance at population scale. There is also an opportunity to shape interoperable frameworks that balance global alignment with local context, particularly across data diversity, linguistic inclusion, and socio economic realities. Finally, investing in capacity building and governance tooling ecosystems can position the region not just as a consumer of AI, but as a contributor to how responsible AI is implemented in practice. The trajectory will depend on how quickly governance moves from principle to repeatable execution across systems.

The AI Dialogue can play a catalytic role by shifting international cooperation from principle alignment to operational coordination. First, it can enable convergence without uniformity. Countries are unlikely to adopt identical regulatory models, but the Dialogue can help define interoperable baselines for safety, accountability, and human oversight that different jurisdictions can map to. This reduces fragmentation while preserving sovereignty. Second, it can act as a platform for shared implementation artifacts. Beyond declarations, there is a need for reference architectures, audit frameworks, risk taxonomies, and lifecycle controls that can be adapted across sectors and maturity levels. Practical toolkits tend to accelerate adoption more than policy statements. Third, the Dialogue can facilitate cross-border learning through real use cases and incident sharing. Governance matures when institutions learn from what fails as much as from what works. A trusted mechanism for sharing lessons, risks, and mitigations would strengthen collective resilience. Fourth, it can anchor capacity building as a cooperative agenda. Many regions face constraints in skills, infrastructure, and institutional readiness. Coordinated efforts around training, governance tooling, and knowledge exchange can reduce the widening gap between AI leaders and adopters. Finally, the Dialogue can help establish a living collaboration model, where governments, industry, and practitioners continuously co-evolve governance approaches in step with technological change, rather than relying on static frameworks. In essence, its role is to make cooperation practical, iterative, and implementation-driven, ensuring that governance keeps pace with how AI is actually deployed across systems and societies.

The AI Dialogue can build on a growing ecosystem of multilateral, standards, and industry-led efforts, while addressing the current gap of fragmentation. Key foundations include the OECD AI Principles, which provide widely referenced high-level guidance; the Global Partnership on AI, which advances applied research and collaboration; and the UNESCO Recommendation on the Ethics of Artificial Intelligence, which brings a strong human-centric and rights-based lens. In parallel, regulatory and policy developments such as the EU AI Act and the G7 Hiroshima AI Process are shaping jurisdiction-specific and plurilateral approaches. Technical standard-setting bodies like ISO/IEC JTC 1/SC 42 and risk frameworks such as the NIST AI Risk Management Framework are further advancing operational elements. However, these efforts often evolve in parallel, leading to overlap without full interoperability. The added value of the AI Dialogue lies in three areas. First, integration and coherence. It can act as a neutral platform to map, align, and connect these initiatives, reducing duplication and enabling cross-framework interoperability. Second, operational bridging. While many frameworks define "what good looks like," fewer address "how to implement." The Dialogue can curate and co-develop implementation playbooks, audit approaches, and lifecycle controls that translate principles into practice. Third, inclusive participation and capacity alignment. By bringing in emerging economies, public institutions, and smaller ecosystem players, the Dialogue can ensure that governance models are adaptable and do not remain concentrated among a few advanced jurisdictions. In essence, the Dialogue can move the ecosystem from fragmented progress to coordinated execution, amplifying the impact of existing efforts while filling the implementation gap.

Effective participation requires moving beyond representation to role clarity and outcome ownership. Governments can contribute by aligning policy intent with implementable regulatory pathways, sharing national experiences, and enabling cross-border coordination. Industry brings deployment reality, including system-level risks, operational constraints, and scalable practices across sectors. Academia and research institutions provide methodological rigor, evaluation frameworks, and forward-looking risk insights. Civil society ensures human impact, inclusion, and rights perspectives remain grounded in real-world implications. Standards bodies and technical communities contribute interoperable specifications, metrics, and assurance models. To make this effective, the Dialogue structure matters as much as participation. First, adopt a multi-layered format: A high-level plenary to align on priorities and signal direction Thematic working groups focused on specific areas such as safety, interoperability, and capacity building Practitioner tracks that translate policy into reference architectures, audit methods, and toolkits Second, shift from static sessions to output-driven cycles. Each track should produce tangible artifacts, for example baseline controls, risk taxonomies, or governance playbooks, with defined timelines. Third, enable continuous engagement, not just periodic convenings. A digital collaboration layer can support ongoing knowledge exchange, incident sharing, and iterative refinement of frameworks. Fourth, ensure inclusion through structured capacity participation, allowing emerging economies and smaller institutions to engage meaningfully, not just symbolically. Finally, introduce feedback loops between policy and practice, where lessons from real deployments continuously inform governance evolution. This structure would position the Dialogue as a living, execution-oriented platform, rather than a one-time deliberative forum.

Global AI governance discussions continue to be shaped by a relatively narrow set of actors, leaving several critical perspectives underrepresented. First, practitioners from emerging economies, particularly those working within public systems and mid-sized enterprises. Their realities involve constrained infrastructure, diverse data contexts, and different risk trade-offs, which are often not reflected in global frameworks. Second, system integrators and implementation partners. Much of AI risk and value emerges during integration into enterprise workflows, yet these voices are underrepresented compared to model developers and policymakers. Third, workforce and sector-specific operators, including those in healthcare, agriculture, education, and public service delivery. They experience the downstream impact of AI decisions but have limited input into governance design. Fourth, linguistic and cultural communities outside dominant data ecosystems. AI systems tend to reflect data availability, which can marginalize non-dominant languages and socio-cultural contexts. Fifth, smaller innovators and open ecosystem contributors, who often lack the resources to engage in global forums despite shaping important parts of the AI landscape. Inclusion requires more than invitations. Structured mechanisms are needed, including funded participation and regional representation models, ensuring consistent engagement from underrepresented geographies. Dedicated practitioner tracks can bring implementation voices into policy discussions. Localized consultations and multilingual engagement frameworks can surface context-specific risks and needs. Finally, creating pathways to influence outcomes, not just participate, ensures these perspectives are reflected in standards, toolkits, and governance artifacts. Broadening participation in this way would make AI governance more context-aware, implementable, and globally relevant.

Meaningful engagement in the AI Dialogue will depend on shifting from passive discussion to interactive, outcome-driven formats. First, use-case driven policy labs. Multi-stakeholder groups work on real scenarios, for example AI in public services or financial risk, to co-develop governance approaches. This grounds discussions in system-level realities rather than abstract principles. Second, live governance simulations. Participants respond to evolving scenarios such as model failure, bias detection, or cross-border data conflicts. These exercises surface decision trade-offs, accountability gaps, and coordination challenges in real time. Third, co-creation sprints. Time-bound working sessions focused on producing tangible outputs such as risk taxonomies, audit checklists, or lifecycle controls. Outputs are shared, reviewed, and iterated within the Dialogue itself. Fourth, reverse panels or practitioner hearings. Instead of experts presenting, policymakers and senior leaders respond to questions from implementers, operators, and affected communities. This inverts the dynamic and surfaces grounded concerns. Fifth, cross-regional peer exchanges. Structured dialogues between countries or sectors facing similar challenges, enabling practical knowledge transfer rather than generic best practices. Sixth, open artifact reviews. Draft frameworks, standards, or guidelines are collectively reviewed in-session, allowing diverse stakeholders to critique, refine, and align on implementation feasibility. Finally, enable a persistent digital collaboration layer, where discussions continue beyond the event, artifacts evolve, and participation is not limited by geography or timing. These formats would make the Dialogue dynamic, participative, and output-oriented, ensuring that engagement translates into actionable governance progress.

A number of emerging policies and frameworks offer practical direction for operationalizing AI governance. Regulatory approaches such as the EU AI Act introduce a risk-based classification model, linking obligations to levels of potential harm. This provides a scalable structure for prioritizing controls without overregulating low-risk use cases. Standards and frameworks like the NIST AI Risk Management Framework translate principles into governance functions, risk mapping, and lifecycle controls, helping organizations embed accountability into development and deployment processes. The OECD AI Principles and the UNESCO Recommendation on the Ethics of Artificial Intelligence provide globally aligned normative baselines, particularly around human-centricity, fairness, and transparency, which many jurisdictions are adapting into national strategies. On the implementation side, model documentation practices such as model cards and datasheets for datasets improve transparency and auditability, enabling better understanding of system limitations and intended use. Within enterprises, AI governance operating models are evolving, including centralized AI risk functions, cross-functional review boards, and integration of AI controls into existing risk, compliance, and audit processes. These approaches tend to be more effective when aligned with enterprise architecture and system lifecycle management. Open ecosystem approaches, including responsible open-source AI frameworks and shared evaluation benchmarks, contribute to transparency and collective scrutiny, though they require complementary safeguards. Collectively, these examples highlight a shift toward risk-based, lifecycle-oriented, and implementation-aware governance, where the focus is not only on defining principles, but embedding them into how AI systems are designed, deployed, and monitored in practice.