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The renewed memorandum of understanding was signed on 25 May by Rear Admiral Saif bin Nasser Al Rahbi, Commander of the Royal Navy of Oman and Chairman of the Maritime Security Committee, and G. V. Srinivas, India’s Ambassador to the Sultanate of Oman. Oman was represented by the Maritime Security Centre, underlining the agreement’s operational focus on maritime safety, information-sharing and cooperation at sea.

The renewal keeps in place a mechanism that has gained importance as shipping routes through the Arabian Sea, Gulf of Oman and Strait of Hormuz face heightened risks from regional conflict, trafficking networks, drone threats, piracy-linked activity and disruptions to commercial navigation. For both countries, maritime security is tied not only to defence cooperation but also to energy flows, trade resilience and the protection of expatriate and commercial links across the Gulf.

The agreement aims to sustain joint cooperation and facilitate the exchange of expertise in maritime security, with the stated objective of contributing to the safety and security of maritime navigation. Its scope fits into a wider pattern of bilateral defence engagement involving naval interaction, coast guard cooperation, training, port calls, high-level meetings and operational contact between maritime agencies.

Oman’s location gives the pact strategic weight. The Sultanate sits close to the Strait of Hormuz, through which a significant share of the world’s seaborne oil trade passes, and faces the Arabian Sea, a route connecting the Gulf with East Africa, South Asia and the wider Indo-Pacific. India’s energy imports, diaspora links and commercial shipping make stability in these waters a central security interest.

The renewed document also reflects a continuity in defence ties that have developed through structured mechanisms over two decades. India and Oman have maintained regular military cooperation, including joint committee meetings, staff-level exchanges and service-to-service engagement. Earlier maritime arrangements between the two sides covered cooperation against crimes at sea, including piracy, armed robbery, arms trafficking, drug smuggling, illegal immigration and people-smuggling by sea.

Coast guard engagement has become a practical arm of that relationship. The Indian Coast Guard and Royal Oman Police Coast Guard have used high-level meetings to discuss capacity-building, cross-ship visits, the Sea Rider programme, pollution-reporting links and cooperation against transnational illegal activity at sea. Such measures are aimed at creating working-level familiarity before crises emerge, particularly in areas where law-enforcement, search-and-rescue and environmental response overlap.

The agreement comes as maritime security has moved higher on the regional agenda. Attacks on merchant vessels in and around the Red Sea, tensions linked to the Israel-Gaza war, risks around Iran-linked flashpoints and the vulnerability of energy shipping have pushed Gulf and Indian Ocean states to strengthen coordination. Oman has also positioned itself as a diplomatic and maritime actor with an interest in keeping sea lanes open while maintaining dialogue with multiple regional powers.

For India, Oman remains one of its closest defence partners in the Gulf. Access, trust and geography make the relationship important for deployments, evacuation contingencies, anti-piracy coordination and wider maritime domain awareness. Oman has historically provided India with a reliable western Indian Ocean partner, while Muscat benefits from deeper technical, training and operational interaction with a major naval power.

The security renewal also sits alongside a growing economic relationship. Bilateral trade has crossed the $10 billion mark, and the two countries signed a comprehensive economic partnership agreement in December 2025 to expand market access for goods and services. The trade pact gave exports from India zero-duty access across most Omani tariff lines, while New Delhi agreed tariff reductions covering a large share of imports from Oman by value.

That economic backdrop makes maritime security more than a defence concern. Smooth shipping flows affect energy supplies, fertilisers, minerals, food products, pharmaceuticals, textiles and engineering goods. Oman’s ports and logistics infrastructure are also relevant to India’s westward trade links, particularly as supply chains adjust to geopolitical tensions and route disruptions.

The renewed memorandum is expected to support more sustained institutional contact between the two sides rather than a one-off diplomatic gesture. Its emphasis on expertise-sharing suggests cooperation may cover surveillance practices, response protocols, training modules, information exchange and coordination against illegal activities at sea. It also offers a framework for strengthening maritime navigation safety at a time when commercial vessels increasingly require layered protection across contested waters.

Global enterprise AI expenditure is expected to reach about $940 billion in 2026 and climb to roughly $2.1 trillion by 2029, reflecting a shift from experimental deployments to large-scale use across manufacturing, finance, logistics, health care, energy and public services. China is emerging as one of the fastest-expanding markets within that cycle, helped by heavy infrastructure spending, state-backed industrial policy and a corporate race to build domestic alternatives to foreign chips, models and cloud platforms.

The acceleration is no longer confined to model development. China’s AI buildout is moving across the full technology stack, from data centres and accelerators to enterprise software, robotics and agentic systems capable of handling complex workflow tasks. The first phase of the investment wave was dominated by computing power, foundational models and large infrastructure projects. The next phase is being shaped by applied AI, intelligent services and automation across production lines, offices and consumer platforms.

That transition is putting pressure on global rivals. US technology groups still lead in advanced chips, frontier models and hyperscale cloud capacity, but China’s market has advantages in deployment speed, manufacturing depth and policy coordination. Companies including Alibaba, Tencent, Baidu, Huawei and ByteDance are expanding AI services while also trying to reduce dependence on restricted US semiconductor supplies. Alibaba has unveiled a new AI chip through its T-Head unit and is directing tens of billions of dollars into cloud and AI infrastructure over several years. Huawei’s Ascend processors have become central to China’s domestic accelerator strategy as export controls limit access to top-tier Nvidia products.

Infrastructure remains the foundation of the race. Worldwide AI infrastructure spending is on course to exceed $1 trillion by 2029, with accelerated servers accounting for the overwhelming share. China remains the second-largest AI infrastructure market after the US, even though export restrictions have created supply constraints for high-end accelerators. Those restrictions have encouraged domestic chip design, local server ecosystems and more efficient deployment models for inference, where many commercial AI applications run after models have been trained.

China’s data centre expansion is also moving into more specialised formats. AI workloads demand dense computing power, stable electricity supply and advanced cooling, all of which are raising costs and straining grids. Projects near Shanghai and other technology hubs are testing alternative energy and cooling systems, including offshore and renewable-powered facilities. These efforts reflect a broader attempt to manage the energy burden of AI while supporting national ambitions in cloud services, industrial software and smart manufacturing.

Robotics is becoming a central pillar of the spending wave. China already has the world’s largest industrial robot base and is pushing into embodied intelligence, where AI systems are integrated into machines that operate in factories, warehouses, homes and public spaces. Forecasts for China’s robotics market point to rapid expansion over the next five years, with demand coming from electric vehicles, electronics, logistics and elder care. The combination of AI models, sensors, batteries and manufacturing scale gives domestic firms a route to compete internationally beyond software alone.

Policy support is reinforcing corporate investment. Beijing’s “AI Plus” strategy, issued in 2025, aims to embed artificial intelligence across the economy by expanding access to data, compute and talent while promoting open-source ecosystems and industrial adoption. Local governments have added funding programmes, innovation parks and procurement support, creating competition among cities such as Beijing, Shanghai, Shenzhen and Hangzhou. That model can accelerate deployment, though it also raises the risk of duplicated capacity, uneven standards and projects driven more by subsidy incentives than commercial demand.

The competitive picture remains mixed. China leads in AI publication volume, citations, patent output and industrial robot installations, while the US continues to produce more frontier models and higher-impact patents. The performance gap between leading US and Chinese models has narrowed, aided by open-source releases and engineering efficiency from Chinese labs. DeepSeek’s breakthrough earlier in the cycle demonstrated that capable systems could be produced with fewer resources than previously assumed, intensifying debate over whether raw spending alone will determine leadership.

The product, built around a personal AI assistant called Mona, is being positioned as a “local-first” alternative to cloud-based generative AI tools. Rather than routing most tasks through remote servers, MonoClaw runs large language models on a user’s own Apple hardware, with the company saying the approach is designed to keep sensitive memory, session history and credentials under the user’s direct control.

Sentimento Technologies Limited, the Hong Kong engineering studio behind the product, describes MonoClaw as the city’s first managed local AI runtime environment for professional users. Its launch comes as businesses move from experimental chatbot use towards agentic AI systems that can monitor workflows, draft responses, prepare reminders and act on user-approved instructions across multiple workplace channels.

Mona is designed to work as a digital secretary across email, WhatsApp, Telegram and calendar-linked tasks. The system can identify action items in messages, prepare reply drafts, generate morning briefings, track pending approvals, surface relevant documents before meetings and flag deadlines. The company says the product ships with 160 pre-bundled professional skills, covering both business administration and personal task management.

Billy Zuo, co-founder and chief executive of Sentimento Technologies, said the shift from manual software operation to autonomous AI execution marked a wider change in workplace computing. He said many businesses adopting cloud AI still face concerns over data leakage and unpredictable subscription costs, arguing that a local-first model gives companies greater control over sensitive information and spending.

MonoClaw’s pricing reflects that positioning. The permanent software licence is priced at HK$28,888, with an early-bird offer of HK$25,888. Users must also provide compatible Apple hardware, such as a Mac mini M4 or iMac M4, with the Mac mini starting at about HK$6,099. The company handles installation, workflow configuration and validation, with payment structured through a 40 per cent deposit and 60 per cent balance after final delivery and verification.

That model differs from many AI software products that rely on monthly subscriptions, metered token use or cloud-service plans. Sentimento is pitching MonoClaw as a capital investment rather than an operating expense, a message likely to appeal to smaller firms that want clearer budgeting and lower recurring software exposure. The trade-off is a higher upfront cost and a hardware dependency that may limit adoption among users who prefer device-neutral or fully cloud-hosted systems.

Privacy and auditability are central to the product’s commercial pitch. MonoClaw uses local contextual memory, local runtime profiles, secret redaction and explicit approval gates for higher-risk actions. Mona is designed to request confirmation through messaging channels before executing sensitive commands, data changes or external actions. That “human-in-the-loop” structure reflects a broader concern in the AI sector: agents that are powerful enough to automate work also need firm boundaries, logs and user control.

Hong Kong’s technology ecosystem has been moving in the same direction. AI agents, local foundation models and governed automation have become more prominent in policy, research and enterprise discussions, with public and academic initiatives focusing on authorisation, traceability and risk controls. MonoClaw enters this environment as a commercial product for office users rather than a research platform, but its design mirrors the same debate over whether AI agents can be useful without creating new privacy and operational risks.

The launch also underscores a changing competitive field for productivity software. Major global platforms are embedding AI assistants into office suites, browsers, customer-service tools and enterprise resource systems. MonoClaw’s advantage, if it gains traction, will lie in its local deployment, Hong Kong-specific setup and promise of cost predictability. Its challenge will be proving that a local Mac-based assistant can match the reliability, integrations and performance of cloud-backed rivals while remaining simple enough for non-technical users.

Energy and infrastructure concerns add another layer to the product’s appeal. Cloud AI workloads have intensified scrutiny of data-centre electricity demand, cooling requirements and network dependence. MonoClaw argues that routine tasks such as inbox triage, drafting, reminders and meeting preparation can be handled on Apple Silicon at desk level, reducing reliance on remote inference for every interaction. The system can still use filtered cloud subprocesses for selected workloads, meaning its privacy and cost benefits will depend on how users configure and supervise each workflow.

This initiative is a collaborative effort involving the University of Chinese Academy of Sciences (UCAS), the National Space Science Center of the Chinese Academy of Sciences, and the Binzhou Weiqiao UCAS High Technology Research Institute. The successful launch marks a historic “zero-to-one” breakthrough, representing the first time private sci-tech forces from Binzhou and indeed Shandong province have reached space. It also stands as China’s first in-space experiment to study the solidification of lightweight high-entropy alloys under the dual-field coupling of “microgravity and rotating magnetic fields.”

As a national-level “space laboratory,” the manned space station hosts world-class research facilities and serves as a core platform for disruptive innovation in new materials. This successful deployment not only highlights the institute’s cutting-edge research capabilities but also signifies a deep integration between corporate scientific research and national aerospace engineering. Looking ahead, the institute will continue its deep dive into frontier fields such as space materials and lightweight alloys. By strengthening collaborative innovation across industry, academia, and research, they aim to empower the upgrading of the new materials industry with technological innovation, contributing both wisdom and strength to the development of China’s manned space program and the cultivation of new quality productive forces.
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• At the launch, KPMG also unveiled its Trusted AI Assurance offering that is aligned with relevant international standards and frameworks to mitigate risk and build trust in AI deployment. This gives businesses a clear, credible pathway to scale AI confidently as Singapore reinforces its position as a trusted node in the global community.

SINGAPORE – Media OutReach Newswire – 25 May 2026 – As Singapore deepens its commitment to becoming a world-leading AI hub, the question of how organisations build AI that is trusted — by customers, regulators and international partners — has become as consequential as how fast they build it. Today, KPMG took a significant step in answering that question with the launch of its Trusted Artificial Intelligence Centre of Excellence (AI CoE). Supported by the Singapore Economic Development Board (EDB), the AI CoE is a dedicated capability hub designed to help organisations move beyond AI experimentation and embed AI as a trusted, enterprise-ready asset.

At the same event, KPMG also unveiled its Trusted AI Assurance — a structured, business-focused, evidence-based approach that gives Singapore businesses a rigorous multi-faceted assessment of their AI deployment and a clear pathway to scale confidently. Today’s launch — bringing together government, enterprise and the professional services sector — signals a pivotal shift in the national AI conversation: from speed to scale of adoption where trust is the foundational bedrock of deploying AI.

The Trusted AI CoE was officially launched by Ms Jasmin Lau, Minister of State, Ministry of Digital Development and Information & Ministry of Education, alongside Mr Jermaine Loy, Managing Director of the Singapore Economic Development Board (EDB), and Ms Lee Sze Yeng, Managing Partner of KPMG in Singapore.

KPMG’s 2025 Global CEO Outlook shows that more than seven in ten CEOs now rank AI as a top investment priority — yet for many, the gap between ambition and sustained enterprise impact remains wide. Governance, data readiness and workforce capability are the defining constraints of this next phase of adoption. Singapore-based businesses face a further dimension: as they expand regionally and globally, they need their AI systems to be trusted not just at home but across multiple jurisdictions — each with its own regulatory expectations and stakeholder standards.

Lee Sze Yeng, Managing Partner at KPMG in Singapore, said: “Across every sector, we are seeing the same pattern: organisations that moved fast on AI are now asking harder questions — where is the real value, are our people genuinely ready to work alongside AI, and can we stand behind the decisions our systems make? These are not technical questions. They are leadership questions. And for Singapore businesses with ambitions beyond our shores, there is an added dimension: the trust that matters to customers and regulators in the markets you are entering may be defined differently from what is required here. Through the KPMG Singapore Trusted AI Centre of Excellence, we are partnering with businesses to rigorously assess where they stand, close the gaps that matter, and build AI that is trusted not just locally but in the markets most critical to their growth. Trusted AI is not a constraint on ambition. Done well, it is the foundation for it.”

Jermaine Loy, Managing Director, EDB, said: “KPMG’s Trusted AI Centre of Excellence here in Singapore will enable businesses across diverse sectors – including financial services, healthcare, logistics and manufacturing – to scale use of AI with confidence, supported by robust governance frameworks and assurance capabilities. At the same time, this strengthens Singapore’s growing AI ecosystem by building enterprise capabilities and workforce readiness for AI adoption. We welcome KPMG Singapore’s efforts to work with EDB and the relevant agencies in advancing Singapore’s position as a globally trusted hub for AI deployment and innovation.”

Trusted AI Assurance — Giving Singapore Businesses a Foundation to Scale AI

For Singapore businesses, the ambition to scale AI is rarely the obstacle. What is harder — and more consequential — is the question of whether the AI they are building and deploying can genuinely be stood behind: by their boards, by their customers, employees and by the regulators and partners they will encounter as they grow beyond Singapore’s shores.

KPMG’s Trusted AI Assurance offering (the “Trusted AI Assurance”) was developed in response to the need to address the trust deficit with AI in the business community to boost adoption. It is not a certification exercise or a compliance checklist. It is a rigorous, evidence-based independent assessment that gives leadership teams an honest picture of where they stand — and a clear, practical path to where they need to be.

What makes it meaningful is its specificity. The Trusted AI Assurance is calibrated to each organisation’s sector, operating context and growth ambitions — because a financial institution navigating cross-border data regulation faces a fundamentally different set of questions from a manufacturer embedding AI into operational workflows, or a healthcare provider deploying AI-assisted diagnostics. Generic benchmarks serve neither well.

The assessment examines two things that are often conflated but must be considered separately: whether an organisation has the governance, culture and leadership practices to deploy AI responsibly; and whether the AI solutions it has built or acquired are themselves trustworthy — documented, risk-assessed, well-integrated and performing as intended. An organisation can have the right intentions and still be running AI that falls short of what its stakeholders — or another country’s regulators — would accept.

Across four domains, the Trusted AI Assurance examines what genuinely matters:

AI Governance — Whether the frameworks, operating models, policies and procedures that govern AI are real and embedded, not aspirational documents that sit untouched between audits.

AI Systems — The AI systems themselves: how they are documented, how risks are inventoried, how models are integrated into business workflows, and whether the data foundations — including Retrieval-Augmented Generation, pipelines and datasets — are robust enough to be relied upon.

AI Regulatory Compliance — How prepared an organisation is for the regulatory environments it currently operates in, and those it intends to enter. For Singapore businesses with expansion ambitions, this is often where the most important gaps surface: the Trusted AI Assurance can identify whether a specific AI system meets the requirements for deployment in a given market — such as the European Union — or what adjustments would make it so.

AI Security — The strategies and processes in place to protect AI systems, manage privacy, and ensure the organisation can detect, respond to and recover from threats as they evolve.

The stakes are sharpest for businesses in highly regulated sectors. Financial institutions, healthcare providers and infrastructure operators have long been accustomed to navigating complex compliance environments — and Singapore’s AI governance frameworks are among the most developed in the region. But as the EU AI Act moves into full enforcement, a new and specific set of expectations is taking effect for any organisation deploying AI within European markets, or handling the data of European citizens. What satisfies regulators here does not automatically satisfy regulators there.

The reality is one of regulatory diversity: different jurisdictions are building their AI governance regimes at different speeds, with different emphases, and with different evidentiary requirements. A Singapore company that has passed relevant local standards may still find that its AI systems require different documentation, greater explainability, or additional human oversight controls to operate with compliance and trust in a European context. Discovering that gap in the middle of a market entry, a cross-border partnership or a regulatory review is a risk that can be avoided.

The Trusted AI Assurance maps these jurisdictional differences, giving organisations a clear read of where they stand against the specific requirements of markets they are entering — before those requirements become obstacles. It is aligned with globally recognised standards including the EU AI Act, the NIST AI Risk Management Framework, ISO 42001 and Singapore’s Model AI Governance Framework, ensuring that what organisations build here is credible and defensible everywhere.

The Trusted AI Assurance is coupled with consultancy guidance — from KPMG or partnered stakeholders — ensuring every assessment leads to a practical path forward. The intent is to give Singapore businesses the clarity and confidence to grow their AI ambitions without having to rebuild trust from scratch in new markets they enter into regionally or globally.

A Framework for Singapore’s AI Ambition: The Four Doors

Beyond the Trusted AI Assurance, organisations can leverage the AI CoE to assess their broader AI strategies through KPMG’s Four Doors framework — four strategic pillars that help Singapore businesses move from isolated pilots to trusted, sustained AI at scale. Together, they address what it takes for Singapore to be not just an AI-capable nation, but a globally trusted one: where the AI solutions developed and deployed here meet the expectations of regulators, partners and customers worldwide.

Value — From Activity to Impact: Establishing the clear metrics and sector-specific solutions needed to distinguish AI activity from measurable business value, across Financial Services, Government, Healthcare, Real Estate, Infrastructure and Manufacturing.

Trust — Trusted-by-Design AI: Built on a human-centric foundation encompassing ten ethical pillars — including Fairness, Transparency, Accountability, Privacy and Sustainability — the Trust pillar puts responsible governance at the centre of AI strategy, rather than treating it as a compliance afterthought.

People — Genuine AI Fluency: Covering sustainable AI workforce strategy, job redesign, leadership development and change management — from executive ignition sessions that build board-level commitment to the behavioural shifts needed to embed AI into everyday working practices.

Data and Technology — Scalable Foundations: Establishing the trusted data and technology infrastructure that enterprise AI requires, supported by KPMG’s proprietary platforms and a market-ready suite of AI tools, with rigorous attention to data quality, system integration, product development and enterprise-wide AI deployment.

For Singapore’s business community, the Four Doors offer a structured path to becoming AI-trusted — not just AI-active. As global regulatory standards tighten and international partners raise governance expectations, that distinction will increasingly determine which Singapore enterprises are positioned to lead in the markets they enter.

Building AI Capability in Singapore

The Trusted AI CoE (the “CoE”) is designed to function as a capability hub to support Singapore’s position as a trusted node in the global community — co-creating knowledge, accelerating innovation and strengthening Singapore’s AI ecosystem across the business community, academia and the public sector.

At its core is a dedicated innovation team of AI/Software Engineers, Solution Architects, Data Analysts, Product Managers, Tech Business Analysts and Designers — 100 per cent Singapore-based and locally hired. This specialist nucleus is complemented by KPMG’s 3,600-strong Singapore workforce and over 275,000 professionals globally, giving the CoE both deep technical capability and multi-disciplinary reach across Audit, Advisory and Tax.

Solutions co-developed through the CoE are pressure-tested with KPMG as default “Client Zero” where appropriate — rigorously iterated across the firm’s own operations before being brought to clients. Initial focus sectors include Financial Services, Infrastructure and Logistics, Manufacturing, Government, Healthcare and Real Estate, alongside functional domains such as Finance, Governance Risk and Compliance, Customer Services and Operations. Strategic partnerships with technology companies, academic institutions and government agencies further support Singapore’s ambition to serve as a trusted and well-governed regional AI hub.

KPMG’s Commitment to Singapore

KPMG is the world’s first professional services firm to attain ISO/IEC 42001 certification for AI Management Systems — the globally recognised standard for responsible AI governance. This underpins every element of today’s launch, reflecting KPMG’s commitment to practising what it advocates: embedding the disciplines of trusted AI governance into its own operations before bringing them to clients.

KPMG continues to invest in Singapore’s AI talent pipeline through international mobility programmes, internal rotations and capability development across data, digital and leadership disciplines — directly aligned with Singapore’s national goal of growing the skilled workforce its AI ambitions require.

ANNEX: Bringing the KPMG Singapore Trusted AI Centre of Excellence to Life

To bring the KPMG Trusted AI Centre of Excellence (AI CoE) to life, guests at the launch were invited to explore four interactive stops (“windows”) showcasing how trusted AI can move from strategy into real-world enterprise and national impact.

FIRST WINDOW – AI Philosophy

The first window introduced guests to KPMG’s Four Doors framework and Trusted AI Assurance – an evidence-based approach designed to help organisations move from AI experimentation to AI that is genuinely trusted by their boards, their customers, and the regulators and partners they encounter as they grow beyond our shores.

Through an interactive scenario set in the financial services sector, the demonstration illustrated how organisations are assessed across four strategic pillars: Value, Trust, People, and Data and Technology. The walkthrough explored whether businesses have the governance structures, workforce readiness, data foundations and operational oversight needed to support trusted, scalable AI deployment.

1. Value — Whether the organisation has clearly defined what its AI investments are meant to deliver, and whether it has the metrics to distinguish genuine business impact from mere AI activity
2. Trust — Whether the organisation’s AI is built on a responsible, human-centric foundation — covering governance frameworks, ethical guardrails, risk management, security and accountability for AI-driven decisions
3. People — Whether the workforce, from board to frontline, has the genuine AI fluency needed to work alongside AI systems, including the leadership commitment, job redesign and capability development required for adoption to take hold
4. Data and Technology — Whether the data assets, infrastructure and system integration practices are robust enough to support trusted, scalable AI deployment

The experience also demonstrated how AI systems themselves are assessed – including areas such as system documentation, risk inventories, model integration and data platform robustness – highlighting the importance of building AI that is genuinely trusted.

1. System Card — Whether each AI solution is properly documented, including how it performs and how it is used in practice
2. Risk Inventory — Whether AI systems across the enterprise have been identified and their risks catalogued
3. Model Integration — Whether adequate processes and controls govern how AI models are integrated into business workflows
4. Data Platform — Whether the data foundations underpinning AI outputs — including Retrieval-Augmented Generation (RAG) pipelines and datasets — are fit for purpose

A second stage of the scenario then explored the reality of regulatory diversity across jurisdictions. While an organisation may be assessed as “trusted” in Singapore, areas that require attention may emerge when expanding into international markets such as Europe under the EU AI Act.

The demonstration underscored the growing importance of regulatory readiness for Singapore businesses with international ambitions, and how the AI CoE is designed to help organisations expand with confidence through trusted AI governance.

SECOND WINDOW – AI Journey

The second window focused on KPMG’s “Client Zero” approach, where suitable AI solutions are developed and applied within its own operations under real-world conditions, and in some cases co-developed with clients depending on their specific needs. This model reflects a simple philosophy: credibility in guiding responsible AI adoption must be earned by applying the same discipline internally. This approach is particularly relevant in Singapore’s context, where trust and reliability are central to how AI is being adopted across sectors.

KPMG’s “Client Zero” approach provides a structured pathway for scaling AI responsibly. Solutions are first embedded into live workflows – allowing organisations to address practical considerations such as human oversight, explainability, data handling and accountability – before broader deployment. This mirrors the journey many enterprises in Singapore are now facing, as they transition from pilots to AI that can be relied on consistently in regulated, cross-border environments. This shift is important, as trust at scale is built not through experimentation alone, but through systems that are reliable, auditable, and able to operate consistently across different contexts.

At this window, MOS will see this pathway brought to life with AI applied across real business workflows, through three solutions: KPMG’s Digital Gateway (DG) GenAI, KPMG Clara Intelligence and Kiara. These solutions are already implemented in real-time across KPMG’s various functions.

The first demonstration illustrates how the audit and tax professions are beginning to deploy AI agents within their workflows to enhance how work is performed. Rather than treating AI as standalone tools, these capabilities are being embedded into business processes to support analysis, decision-making and execution in a controlled and accountable manner. DG GenAI, KPMG’s Generative AI capability embedded within our Digital Gateway platform, will be used as a demonstration of how this is operationalised in practice — showing how AI capabilities can be integrated into everyday workflows, with appropriate governance, data security and user controls in place.

The second demonstration presents KPMG Clara Intelligence, an intelligent AI-powered platform which acts as a centralised platform. KPMG Clara Intelligence showcases the future of audit, where AI is used to support data-driven risk assessment, better insights and analysis and decision-making on a single platform. This reduces reliance on manual processes and enables auditors to focus on higher-value judgement, improving both efficiency and audit quality. MOS will see how this reflects a broader shift in the profession — towards AI-assisted workflows that require new skills and deeper analytical capabilities.

This window also highlighted how AI is reshaping the accountancy and professional services sector. As AI becomes embedded into core workflows, roles within the profession are evolving — routine, manual tasks are increasingly automated, allowing professionals to focus on higher‑value judgement, analysis and advisory work. At the same time, this shift requires a broader base of AI fluency across the workforce, not just among specialists, as professionals will need to work alongside AI systems in their day‑to‑day roles.

These changes are driving a wider rethinking of skills development across the sector, including how institutions and employers prepare current and future talent. Efforts are underway to align training and education pathways with these evolving needs, ensuring that individuals entering the profession — and those already in it — are equipped to operate effectively in an AI‑enabled environment. This reflects a broader transition towards a workforce that combines strong domain expertise with AI capabilities, in line with Singapore’s ambition to develop an AI‑bilingual workforce.

The third solution, Kiara, is KPMG’s GenAI recruitment assistant, illustrating how AI can enhance the candidate engagement and streamline scheduling of interviews. This highlights how AI is not only transforming business processes, but also how people work and interact with technology.

Overall, the window demonstrated how these solutions reflect the same principles KPMG advises its clients to adopt: strong data foundations, embedded governance, and human oversight at key decision points – illustrating the practical steps organisations take to deploy AI responsibly at scale, underpinning the rigour required for Singapore to continue its progress as a trusted hub for AI.

THIRD WINDOW – AI Vision

The third window focused on how AI does not merely change the tools organisations use, but changes the nature of work itself – which roles exist, how decisions are made, and what it means to be genuinely capable in a professional environment.

As organisations navigate AI-driven workforce transformation, the showcase highlighted the same set of questions organisations consistently grapple with: which roles are genuinely enhanced by AI, and which risk being hollowed out? Where should organisations invest in reskilling, and on what timeline? How are accountability and professional standards maintained when AI is embedded into decisions that used to rest entirely with a person?

The insight that emerges across sectors is consistent: organisations that treat AI workforce transformation as a technology deployment tend to underinvest in the human dimensions – and that is where adoption stalls, quality erodes or trust breaks down. The organisations that get it right treat it as a leadership and organisational design challenge first, with technology as the enabler.

The window also demonstrated how KPMG Mystro™, an AI-enabled workforce intelligence platform, is used to map how work is done today at the task and decision level, and model where AI can responsibly augment human roles across functions such as finance, audit and operations.

Drawing on examples from finance, audit and operations, the showcase illustrated professionals working alongside AI in ways that redirect their expertise toward work requiring human judgement. More broadly, it reinforced the importance of approaching AI transformation not simply as a technology deployment exercise, but as a broader leadership and organisational design challenge.

Singapore’s commitment to building an AI-bilingual workforce – professionals who are not merely aware of AI but genuinely capable of working alongside it, questioning its outputs and being accountable for the decisions it informs – rests on organisations taking the human dimension of transformation as seriously as the technology itself. The insights at this window speak directly to that agenda: what it takes to build not just AI capability, but the kind of AI-confident workforce that sustains Singapore’s competitiveness over the long term.

FOURTH WINDOW – AI Ecosystem

The fourth window focused on how trusted AI adoption cannot be the preserve of large enterprises alone. Singapore’s economic resilience depends on its SME community — the businesses that form the backbone of the economy and that stand to gain enormously from AI, but that often lack the resources, expertise or confidence to navigate adoption responsibly.

Referencing the DBS Spark GenAI programme and DBS SME AI Playbook – developed through a collaboration between DBS and KPMG, and supported by SkillsFuture Singapore – the showcase highlighted practical pathways designed to help SMEs build awareness and accelerate their AI adoption through tailored pathways depending on their AI maturity.

1. Start — For businesses at the beginning of their AI journey: practical orientation on what AI can realistically deliver, where to begin, and what foundational steps to take before committing to more significant investment
2. Accelerate — For businesses that have taken initial steps and are ready to move further: guidance on identifying higher-value use cases, building internal capability and managing the operational changes that come with deeper AI adoption
3. Scale — For businesses ready to embed AI more systematically: the governance, integration and workforce considerations that responsible scaling requires

The window also highlighted the broader support ecosystem available to SMEs, including a broader ecosystem of over 16,000 solution providers globally via IMDA’s Open Innovation Platform, alongside the SME AI Playbook which offers practical use cases, success stories from real businesses, FAQs and an AI readiness diagnostic tool.

Economy-wide AI adoption – the kind that drives meaningful productivity gains across the full breadth of Singapore’s business community – only happens if SMEs are part of the story. The DBS SME AI Playbook is a direct response to that reality: a practical, accessible entry point into AI adoption calibrated to the real constraints and real ambitions of smaller businesses.

The collaboration between DBS and KPMG reflects how Singapore’s AI ecosystem works at its best: financial institutions, professional services firms, government platforms and businesses working together to make trusted AI adoption accessible to every part of the economy, not just its largest players.

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About KPMG in Singapore

KPMG in Singapore is part of a global organization of independent professional services firms providing Audit, Tax and Advisory services. We operate in 138 countries and territories with more than 276,000 partners and employees working in member firms around the world. Each KPMG firm is a legally distinct and separate entity and describes itself as such. KPMG International Limited is a private English company limited by guarantee. KPMG International Limited and its related entities do not provide services to clients.

For more details about our structure, please visit kpmg.com/governance.
For more information, visit kpmg.com.sg
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Huang is expected to meet Taiwan Semiconductor Manufacturing Company chairman and chief executive C. C. Wei as Nvidia prepares a second-half production ramp for Vera Rubin, the next-generation platform that combines new Vera CPUs, Rubin GPUs, advanced memory and high-speed networking for rack-scale AI systems. The visit has drawn close attention because Nvidia’s product cycle is now tied more tightly than ever to Taiwan’s foundries, packaging houses, server assemblers and component suppliers.

Nvidia reported revenue of $81.6bn for the quarter ended April 26, up 85 per cent from a year earlier, with data-centre revenue reaching $75.2bn, up 92 per cent. The company also authorised an additional $80bn in share repurchases and raised its quarterly dividend to 25 cents a share from 1 cent, underscoring the scale of cash generation from the AI infrastructure boom.

Demand is being driven by cloud providers, AI model developers, enterprise customers and sovereign AI projects, all seeking clusters capable of training and running larger generative and agentic AI systems. Nvidia has described the buildout of “AI factories” as one of the largest infrastructure expansions in technology history, with its platform spanning chips, networking, systems software and reference designs for data centres.

Vera Rubin is expected to follow the Blackwell and Grace Blackwell generation, which is still being shipped in high volumes. That overlap is creating pressure across Taiwan’s supply chain because new capacity must support existing commitments while preparing for a platform with different requirements in advanced packaging, high-bandwidth memory integration, board assembly, cooling and system validation.

Huang has described Vera Rubin as potentially the biggest product ramp in Nvidia’s history and one of the largest technology manufacturing efforts Taiwan has handled. Industry estimates around the platform point to millions of parts across each system and the involvement of well over 100 Taiwan-based ecosystem partners, from chip fabrication and advanced packaging to printed circuit boards, power systems, connectors, thermal modules and final server integration.

TSMC remains central to the effort because Nvidia depends on its leading-edge process technology and advanced packaging capacity. The challenge is not limited to wafers. CoWoS and other packaging technologies have become strategic bottlenecks as AI accelerators require multiple logic dies, high-bandwidth memory stacks and dense interconnects. Any delay in packaging expansion can ripple through server production schedules, cloud deployment plans and Nvidia’s revenue timing.

Taiwan’s broader technology sector has become a critical artery for the global AI trade. TSMC, Foxconn, Quanta, Wistron, Wiwynn, Inventec and a dense network of specialist suppliers are competing to add capacity while dealing with constraints in land, power, water, skilled labour and equipment availability. The concentration gives Nvidia speed and engineering depth, but also exposes the company to geopolitical and operational risks.

China remains the clearest commercial setback. Huang has said US export controls have pushed Nvidia’s share of China’s advanced AI chip market to zero, a striking reversal for a company that once held a dominant position there. The restrictions have strengthened the opening for Huawei and other domestic suppliers, while leaving Nvidia dependent on whether Washington and Beijing allow compliant chips to be sold without triggering national-security objections.

That loss has not slowed Nvidia’s global momentum, but it has changed the composition of growth. Revenue is now increasingly concentrated in markets where hyperscale cloud companies, enterprise AI developers and governments are building large AI data centres outside China. For investors, the question is whether demand can keep absorbing each successive architecture at the scale implied by Nvidia’s guidance.

Nvidia forecast revenue of about $91bn for the current quarter, above market expectations, signalling that orders for AI infrastructure remain strong despite concerns about overcapacity, power shortages and the pace at which customers can monetise AI services. Operating expenses rose 49 per cent to $7.75bn, reflecting the growing cost of research, supply commitments and ecosystem investments needed to sustain the company’s lead.