THE BRAVE NEXT LTD

A successful first Global Dialogue on AI Governance should deliver practical, inclusive, and globally applicable outcomes for responsible AI deployment. It should move beyond broad principles and establish actionable guidance that helps governments, industry, and technical stakeholders deploy AI safely, responsibly, and with measurable public benefit. Success would mean building shared international principles for safe, trustworthy, and human-centered AI, while ensuring these principles are practical across both advanced and emerging economies. The Dialogue should recognize that AI governance must not be shaped only by frontier AI developers, but also by those deploying AI in real-world environments such as workplaces, public services, and industrial systems. A strong outcome would be the creation of practical governance frameworks for high-impact AI systems, especially those used in safety-critical environments where AI directly affects human well-being, worker safety, and operational risk. This should include clear guidance on transparency, accountability, human oversight, auditability, and responsible deployment. The Dialogue should also strengthen AI capacity-building so startups, SMEs, and developing economies can adopt responsible AI without being excluded by cost, infrastructure, or regulatory complexity. Another important outcome would be stronger representation from emerging markets in shaping global AI governance, ensuring international frameworks reflect practical deployment realities beyond large technology firms and advanced economies. According to the International Labour Organization (ILO), 5 to 6 workers die every minute globally due to work-related causes. Our solution has already proven to reduce workplace accidents by up to 80% through real-time hazard detection, proactive monitoring, and faster incident response. A successful Dialogue should recognize and support such practical AI systems that deliver immediate human and societal benefit.

• Safe, secure and trustworthy AI
• AI capacity-building
• Protection and promotion of human rights
• Transparency, accountability, and human oversight

We selected these priorities because The Brave Next Ltd develops Vision AI systems for industrial safety, compliance, and proactive risk prevention in real workplace environments. Our AI transforms existing CCTV infrastructure into intelligent monitoring systems that detect PPE violations, unsafe behavior, restricted-area access, emergency hazards, and operational risks in real time. Safe, secure, and trustworthy AI is essential because these systems operate in safety-critical environments where reliability, accuracy, and responsible deployment directly affect worker safety. AI capacity-building is equally important to ensure that responsible AI is not limited to large corporations or advanced economies. Practical and affordable AI governance must also support SMEs and emerging markets. We prioritize the protection and promotion of human rights because AI in workplace environments should improve worker safety, dignity, and well-being, not become a tool for harmful surveillance or unfair monitoring. Human-centered deployment is critical in industrial settings. Transparency, accountability, and human oversight are also central to our work. AI systems in safety-critical environments must support human supervisors, HSE teams, and operational decision-makers, not replace them. Effective governance should require explainable alerts, clear audit trails, accountable escalation processes, and human review of safety-related decisions. According to the International Labour Organization (ILO), 5 to 6 workers die every minute globally due to work-related causes. This highlights why responsible AI governance must prioritize practical, safety-focused AI systems that reduce harm, prevent accidents, and protect workers in real-world environments.

Yes. One important emerging issue is the governance of AI in physical and safety-critical environments, especially industrial workplaces. Much of today's AI governance discussion focuses on generative AI, but Vision AI is already being actively deployed in factories, construction sites, warehouses, logistics operations, and energy facilities where AI decisions can directly affect worker safety and operational risk. These systems require dedicated governance guidance beyond general AI principles. This includes worker privacy in camera-based environments, responsible monitoring boundaries, human review of AI alerts, incident traceability, audit logs, data retention standards, and minimum reliability thresholds for safety-critical AI systems. Another important cross-cutting issue is the need to distinguish between surveillance AI and safety AI. Governance frameworks should clearly define acceptable use boundaries so AI is used to protect workers, prevent harm, and improve safety rather than enable intrusive monitoring, misuse, or disproportionate control over workers. Affordability and inclusion are also major emerging issues. Responsible AI governance should be practical for startups, SMEs, and emerging economies, not only for large corporations or advanced markets. If governance frameworks are too costly or complex, many regions will be excluded from safe and beneficial AI adoption. A further cross-cutting issue is governance for low-cost, edge, and hybrid AI systems that operate on existing infrastructure such as CCTV. These systems are increasingly common in developing markets and require governance approaches that address privacy, security, resilience, and accountability in resource-constrained environments. Global AI governance should therefore include practical frameworks for applied AI in real-world environments, especially where AI directly impacts human safety, dignity, and working conditions.

In our sector, one of the largest governance gaps is the absence of practical AI standards for safety-critical industrial environments. While AI adoption is increasing across manufacturing, construction, logistics, and energy sectors in Pakistan, the GCC, and other emerging markets, governance frameworks have not evolved at the same pace. Most AI policy discussions still focus on frontier models and generative AI, while applied Vision AI in real workplaces remains under-addressed. This creates major challenges. There is still limited guidance on how AI should be deployed in industrial environments where worker safety, compliance, and operational risk are directly affected. Clear standards are needed for reliability thresholds, human oversight, auditability, incident traceability, and responsible use of AI-generated alerts in safety-critical settings. There is also a growing risk that AI monitoring may be misused for surveillance rather than worker protection if governance boundaries are not clearly defined. Affordability is another major challenge. Many governance and compliance frameworks are designed for large enterprises and advanced economies, making responsible AI adoption difficult for SMEs and emerging markets due to cost, infrastructure, and regulatory complexity. At the same time, this creates a significant opportunity. Practical governance frameworks for low-cost, human-centered, and safety-focused AI can unlock major impact in emerging markets. AI can reduce workplace accidents, improve compliance, and support safer industrial growth at scale. According to the International Labour Organization (ILO), 5 to 6 workers die every minute globally due to work-related causes. As Winner of the Shell WAFI Award, Top 30 Global Startup selected at Kazan Digital Week (KDW), and an incubated startup at NICAT and PITB Regional Incubation Wing, we have received strong national and international validation for practical industrial AI. We have deployed AI solutions across 100+ industrial sites and measured before-and-after operational impact, with our system proving to reduce workplace accidents by up to 80% through real-time hazard detection, proactive monitoring, and faster incident response. This demonstrates how responsible AI governance can deliver measurable, real-world impact at scale.

The AI Dialogue can play a critical role in advancing international cooperation by creating a practical and inclusive global platform for aligning how AI is governed, deployed, and evaluated across countries, sectors, and levels of development. It can help bridge the growing gap between high-level AI policy discussions and the realities of AI deployment in real-world environments. A key role of the Dialogue should be to establish shared global principles for safe, trustworthy, and human-centered AI, while also supporting practical implementation across different regulatory, economic, and technical contexts. International cooperation is most valuable when it enables interoperable governance approaches, common safety expectations, and shared accountability standards without limiting innovation. The Dialogue can also help ensure that AI governance is shaped not only by frontier AI developers and advanced economies, but also by emerging markets, SMEs, applied AI companies, and sectors deploying AI in practical environments such as workplaces, public services, manufacturing, logistics, and infrastructure. Another important role is enabling cross-border knowledge sharing on what responsible AI deployment looks like in practice. This includes sharing lessons, governance models, technical standards, risk controls, and measurable impact from real deployments across different regions and industries. The Dialogue should also strengthen international cooperation on AI capacity-building so that developing economies can adopt and govern AI responsibly without being excluded by cost, infrastructure, or regulatory complexity. For sectors like industrial safety, international cooperation is especially important because the risks AI addresses, such as worker harm, unsafe operations, and compliance failures, are global challenges. The Dialogue can help create globally relevant governance frameworks that support practical AI systems proven to improve safety, reduce accidents, and deliver measurable public benefit across regions.

The AI Dialogue should build upon existing international AI governance efforts such as the UN AI Advisory Body recommendations, UNESCO's Recommendation on the Ethics of AI, the OECD AI Principles, the G7 Hiroshima AI Process, and the ILO's work on worker protection, occupational safety, and the future of work. These frameworks already provide strong foundations on ethics, safety, human rights, and responsible innovation. The Dialogue should also connect with practical implementation ecosystems, including national innovation agencies, regulatory sandboxes, startup ecosystems, technical standards bodies, and industry-led AI safety initiatives. In emerging markets, this should include incubators, applied AI ecosystems, SME innovation networks, and public-private partnerships that are already deploying AI in real operational environments. The added value of the AI Dialogue should be its ability to connect these fragmented efforts into a more practical, globally inclusive, and implementation-focused governance platform. Many existing initiatives define high-level principles, but fewer address how AI should be governed in real-world deployment contexts, especially in sectors such as industrial safety, logistics, construction, manufacturing, and public infrastructure. The Dialogue can add value by translating broad AI governance principles into practical guidance for applied AI systems, particularly those operating in safety-critical and resource-constrained environments. It can also bridge the gap between advanced economies and emerging markets by ensuring governance frameworks are globally relevant, affordable, and implementable beyond large technology firms. Another important contribution would be creating stronger pathways between policymakers, technical communities, industry operators, and applied AI companies so governance is informed not only by theory, but also by operational experience, measurable outcomes, and practical deployment realities.

The AI Dialogue should be structured as a practical, multi-stakeholder process where governments, industry, academia, civil society, technical experts, and applied AI operators contribute distinct but complementary perspectives. Effective AI governance requires not only policy discussion, but also technical, operational, and social implementation input. Governments should contribute regulatory priorities, public-interest safeguards, and national implementation perspectives. Industry should contribute practical deployment experience, operational risks, and lessons from real-world AI systems. Academia and technical experts should support evidence-based standards, testing methods, and evaluation frameworks. Civil society should ensure human rights, inclusion, labor protection, and accountability remain central to the Dialogue. The Dialogue should also include stronger participation from startups, SMEs, and emerging-market innovators, as these groups are often underrepresented despite being critical to practical AI adoption and deployment. Their inclusion is essential to ensure governance frameworks remain globally relevant and implementable. In terms of structure, the AI Dialogue should combine high-level policy discussions with sector-specific working groups focused on practical deployment areas such as industrial AI, public services, education, healthcare, labor, and digital infrastructure. This would allow governance discussions to move beyond broad principles into applied and measurable recommendations. The format should include: plenary sessions for global priorities, thematic working groups for sector-specific governance, regional consultations for local context, and implementation roundtables that bring together policymakers, technical experts, and real-world AI operators. The Dialogue should also include written submissions, practical case studies, and deployment evidence from stakeholders already implementing AI systems. This would ensure the process is informed not only by policy and theory, but also by measurable operational experience, practical governance challenges, and real-world outcomes.

Several critical voices remain underrepresented in global AI governance discussions, particularly those involved in deploying AI in real-world environments outside large technology firms and advanced economies. Global AI governance is still largely shaped by frontier AI developers, major technology companies, and policy institutions in advanced markets, while many communities directly affected by AI deployment remain insufficiently represented. One of the most underrepresented groups is stakeholders from emerging markets, where AI is increasingly being deployed in resource-constrained and high-impact environments but governance realities differ significantly from those in advanced economies. Their inclusion is essential to ensure AI governance frameworks are globally relevant, practical, and not designed only for high-income markets. Startups, SMEs, and applied AI companies are also underrepresented, despite often being responsible for real-world deployment in sectors such as manufacturing, logistics, healthcare, agriculture, education, and public services. These actors bring practical experience on implementation constraints, operational risk, affordability, and responsible deployment in real conditions. Workers, labor communities, and occupational safety stakeholders are also insufficiently represented, particularly in discussions around workplace AI, automation, monitoring, and worker protection. Their participation is essential to ensure AI governance protects dignity, privacy, safety, and fair treatment. Other underrepresented groups include technical operators, system integrators, local regulators, industrial safety practitioners, and SMEs implementing AI on existing infrastructure in developing markets. These voices can be included through dedicated regional consultations, sector-specific working groups, multilingual participation pathways, and stronger representation from applied AI operators in formal consultations. The Dialogue should also include structured participation from startups, labor representatives, and practical deployment stakeholders so AI governance is informed not only by policy and frontier research, but also by operational realities and lived impact.

To foster meaningful and dynamic engagement, the AI Dialogue should move beyond traditional panel discussions and adopt more practical, interactive, and evidence-driven formats. AI governance is most effective when participants engage not only in policy discussion, but also in real use cases, operational trade-offs, and implementation realities. One effective format would be scenario-based policy labs, where stakeholders work through real AI governance cases such as workplace safety, public service delivery, algorithmic bias, and critical infrastructure monitoring. This would help test governance principles against practical deployment challenges. Sector-specific implementation roundtables would also be valuable. These focused sessions should bring together policymakers, technical experts, industry operators, civil society, and end users to discuss governance in areas such as industrial AI, labor, healthcare, education, logistics, and public infrastructure. Another strong format would be evidence-based case study sessions, where organizations present real AI deployments, including governance risks, safeguards, lessons learned, and measurable outcomes. This would ground the Dialogue in practical evidence rather than abstract theory. Regional and multilingual consultations are also essential to ensure stronger participation from emerging markets and underrepresented communities. These should be connected to the main process so local perspectives inform global outcomes. Interactive governance clinics could also add value, allowing startups, SMEs, and public-sector deployers to bring practical governance challenges and receive multidisciplinary feedback. Finally, the Dialogue should include written submissions, open consultation tracks, and cross-stakeholder working groups so engagement continues beyond live sessions and contributes to actionable, implementation-focused outcomes.

Effective AI governance should be supported by practical policies and operational mechanisms that translate high-level principles into real-world accountability. One strong example is the use of human-in-the-loop governance, where AI systems support decision-making but do not replace accountable human oversight. This is especially important in safety-critical environments where AI alerts should be reviewed and acted upon by responsible human operators. Another effective approach is audit-by-design, where AI systems are built with traceable logs, incident records, explainable outputs, and escalation histories. This improves accountability, enables compliance review, and supports responsible incident management. Risk-tiered governance is also a practical model. AI systems should be governed based on their potential impact. Safety-critical systems used in workplaces, infrastructure, healthcare, or public services should face stricter requirements for reliability, human oversight, transparency, and accountability than low-risk automation tools. Regulatory sandboxes are another effective mechanism, allowing innovators, regulators, and technical stakeholders to test AI systems in controlled environments before wider deployment. This helps balance innovation with oversight and reduces governance uncertainty. In practice, low-cost and inclusive AI governance models are also essential, especially in emerging markets. Governance frameworks should support responsible AI deployment on existing infrastructure, including edge and hybrid systems, rather than assuming only high-cost or cloud-native environments. At The Brave Next Ltd, we apply these principles in practice through human-supervised Vision AI for industrial safety. Our systems use real-time alerts, audit trails, incident traceability, and supervisor oversight to support worker protection and operational safety. Deployed across 100+ industrial sites, this approach has helped reduce workplace accidents by up to 80%, showing how practical governance mechanisms can deliver measurable real-world impact.