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By Nitya Chakraborty With the U.S. President Donald Trump himself downgrading the status of the four nation Quadrilateral Security Dialogue (QUAD), why is Indian government spending so much of its precious time on QUAD meetings? It is sheer wastage of time for the Prime Minister Narendra Modi as also Foreign Minister Dr. S Jaishankar. US […]

The article Trump Has Downgraded Status Of QUAD-India Should Not Waste Time On It appeared first on Latest India news, analysis and reports on Newspack by India Press Agency).

The agreement was signed on Tuesday during the Quad foreign ministers’ meeting in New Delhi, where External Affairs Minister S Jaishankar and US Secretary of State Marco Rubio held talks covering economic security, energy resilience and Indo-Pacific cooperation. The pact gives both countries a structured route to work together across the full minerals value chain, from exploration and extraction to refining and downstream industrial use.

Critical minerals and rare earth elements have become central to the global contest over clean-energy technologies, semiconductors, electric vehicles, defence systems, communications equipment and advanced manufacturing. For New Delhi, the agreement offers access to US capital, technology partnerships and supply-chain coordination at a time when domestic demand for lithium, cobalt, nickel, graphite, copper and rare earth magnets is expected to rise sharply.

China’s dominance remains the main strategic driver behind the pact. Beijing controls a large share of global rare earth refining and magnet production, giving it leverage over industries that depend on high-performance permanent magnets, battery inputs and specialised industrial materials. Export restrictions and licensing measures imposed by China on select critical minerals have added urgency to efforts by major economies to diversify sourcing and processing.

New Delhi’s gains could come in several layers. The first is supply security. India’s electronics, defence, renewable energy and electric mobility sectors need stable access to minerals that are often mined in one region and processed in another. A bilateral framework with Washington can help identify viable projects, link producers with manufacturers and create longer-term offtake arrangements that reduce price and availability shocks.

The second benefit lies in processing. India has mineral-bearing resources, a large manufacturing base and expanding demand, but it lacks sufficient refining and separation capacity for several high-value critical minerals. Collaboration with US companies and research institutions could support technology transfer, pilot processing facilities, environmental safeguards and recycling systems. That is particularly important because rare earth value chains are not only about mining; separation, purification and magnet-making often determine strategic control.

A third advantage is financing. Critical mineral projects are capital-intensive, slow to develop and vulnerable to volatile prices. The new framework is expected to encourage coordinated investment, risk-sharing and policy support. For India, this could help attract patient capital into mineral exploration, processing parks, recycling ventures and specialised manufacturing units linked to batteries, magnets and electronics.

The agreement also fits into New Delhi’s wider industrial strategy. The government has launched auctions for critical mineral blocks, expanded exploration and moved to build partnerships with resource-rich countries. State-backed and private companies have shown growing interest in lithium, rare earths and battery materials, while domestic manufacturers are seeking greater localisation of supply chains for electric vehicles, solar power, wind equipment and semiconductors.

Washington’s interest is equally clear. The US wants trusted partners that can help build alternatives to China-centred mineral flows. India offers scale, engineering capacity, a large market and geopolitical alignment through the Quad. A stronger minerals partnership can also support US efforts to diversify clean-energy and defence supply chains beyond a narrow group of suppliers.

The pact follows earlier India-US cooperation in technology, defence, semiconductors and clean energy. It also complements Quad-level initiatives designed to strengthen supply chains and energy security across the Indo-Pacific. Australia brings mining strength, Japan brings advanced materials and manufacturing capacity, the US brings capital and technology, and India brings market scale and industrial ambition.

Challenges remain substantial. New mining and processing projects often face long approval timelines, environmental scrutiny, land acquisition issues and high upfront costs. Rare earth separation involves complex chemistry and waste management concerns. Recycling can reduce dependence on fresh extraction, but collection systems and processing capacity remain underdeveloped. India will also need a skilled workforce, stable regulation and clear pricing signals to convert diplomatic agreements into viable industrial projects.

Commerce and Industry Minister Piyush Goyal’s visit to Ottawa and Toronto, accompanied by more than 100 business leaders, has added momentum to negotiations on a Comprehensive Economic Partnership Agreement, which both sides see as central to expanding market access, investment flows and supply-chain cooperation. The visit comes after a prolonged freeze in trade discussions caused by the diplomatic dispute that followed the 2023 killing of Sikh separatist Hardeep Singh Nijjar in British Columbia.

New Delhi and Ottawa are now working to rebuild trust through a structured commercial agenda covering energy, agriculture, critical minerals, technology, education and services. The scale of the business delegation travelling with Goyal underlines the attempt to shift the relationship from political caution to transaction-led engagement. A “Team Canada” delegation is expected to visit India later this year, signalling that both governments want to sustain ministerial and private-sector contact beyond formal negotiating rounds.

Bilateral trade remains below potential for two large economies with complementary strengths. Merchandise trade between the two countries stood at about $13.6 billion in 2025, with Canada exporting vegetables, mineral fuels, oils and wood pulp, while imports from India were led by precious stones and metals, machinery, pharmaceuticals and manufactured goods. Services trade, especially in technology, education, professional services and mobility-linked sectors, has grown into a significant part of the relationship.

The $50 billion target is ambitious but not beyond reach if negotiations produce tariff concessions, regulatory clarity and stronger investment protections. India is seeking reliable access to critical minerals, energy products, gas, fuel and agricultural inputs from Canada. Ottawa, for its part, sees India as a large growth market at a time when Canada is trying to diversify trade beyond its heavy dependence on the United States.

Energy is likely to become one of the pillars of the renewed partnership. Canada’s reserves of uranium, potash, natural gas and critical minerals fit India’s requirements for food security, clean energy expansion and industrial supply chains. Civil nuclear cooperation has returned to the agenda, with uranium supply discussions forming part of the broader effort to rebuild strategic confidence. India’s rising electricity demand and its plan to expand non-fossil energy capacity give Canada a commercial opening in nuclear fuel and related technologies.

Agriculture and fertilisers also carry strategic weight. Canada is a major supplier of potash, while India’s farm sector requires stable input supplies to support food production. Pulses have long been part of the trade basket, though pricing, phytosanitary rules and domestic market sensitivities have at times complicated flows. A broader agreement could provide more predictable rules, but both sides will have to manage farmer concerns and safeguard mechanisms carefully.

Technology and education offer another route to deeper engagement. Canada remains a major destination for students from India, while companies in both countries are exploring artificial intelligence, clean technology, fintech, cybersecurity and advanced manufacturing. Talent mobility will remain a sensitive but crucial component of any expanded economic relationship, especially as Canada adjusts immigration settings and India pushes for easier access for skilled professionals.

The political backdrop remains complex. Relations deteriorated sharply in 2023 after Canada alleged involvement by agents linked to New Delhi in Nijjar’s killing, an accusation India rejected. Diplomatic expulsions and a pause in trade talks followed, slowing a relationship that had already been marked by differences over separatist activism, security concerns and diaspora politics. The decision to restart CEPA negotiations reflects a pragmatic calculation that economic interests can create stabilising channels even when political trust is still being repaired.

Business groups on both sides are pressing for faster progress. Exporters want lower tariffs, simpler customs procedures and clearer standards rules. Investors are seeking safeguards, dispute-resolution mechanisms and sectoral openings. Canada’s pension funds and institutional investors already have exposure to infrastructure, real estate and financial assets in India, while companies from India are looking at Canadian opportunities in technology, mining, manufacturing and services.

The Securities and Exchange Board of India is examining whether companies that list only debt securities should continue to face disclosure and compliance obligations similar to those imposed on equity-listed companies. The review is part of a wider effort to deepen the corporate bond market, where heavy reliance on private placements, limited retail participation and concentration among highly rated issuers have kept market breadth below policy ambitions.

Chairman Tuhin Kanta Pandey, who took charge of the regulator in March 2025, has signalled that the next phase of reform will focus on making the debt market more usable for issuers and investors without weakening safeguards. A pilot framework for tokenising corporate bonds is expected to be launched within six to nine months, testing whether distributed-ledger technology can improve settlement speed, servicing, traceability and operational efficiency.

The proposed easing would be especially relevant for entities that raise money only through listed non-convertible securities and do not have listed shares. Such issuers have argued that several provisions under the Listing Obligations and Disclosure Requirements framework were designed primarily for equity-market governance, where public shareholders carry voting and ownership rights. Debt investors, by contrast, are focused mainly on credit quality, repayment capacity, covenant compliance and timely interest payments.

The regulatory challenge is to separate investor-useful disclosure from procedural duplication. Quarterly financials, credit-rating updates, default alerts, asset-cover statements, related-party exposure and material event reporting remain central to creditor protection. Requirements linked more closely to equity ownership, board-shareholder dynamics and public float may be reviewed where they impose costs disproportionate to the risks faced by bondholders.

India’s corporate bond market has expanded sharply but remains structurally narrow. Net outstanding corporate bonds rose from ₹17.5 trillion in FY2014-15 to about ₹53.6 trillion in FY2024-25, with fresh issuances touching ₹9.9 trillion during 2024-25. The market accounts for roughly 15-16 per cent of GDP, far below deeper Asian and developed markets, and is still dominated by highly rated financial and public-sector issuers.

Private placements remain the preferred route for large borrowers, while public bond issues account for only a small share of overall issuance. That pattern has kept institutional investors at the centre of the market and limited participation by individuals, smaller corporates and lower-rated issuers. For policymakers, the issue is not simply market size but market diversity, liquidity and the ability of bonds to finance infrastructure, housing, manufacturing and growth-stage companies at competitive rates.

Tokenisation is being positioned as a market-infrastructure experiment rather than a replacement for existing bond-market systems. Under such a model, a bond can be represented as a digital token on a permissioned ledger, with ownership transfers, coupon payments and redemption records updated through automated processes. A well-designed pilot could reduce reconciliation gaps, improve audit trails and allow faster post-trade processing while preserving regulatory oversight.

Global fixed-income markets have been exploring tokenisation for similar reasons. Automation through smart contracts can simplify issuance, settlement and corporate actions, while better record keeping can lower operational risk. The benefits, however, depend on legal clarity, interoperability with depositories and clearing systems, cyber-security standards and clear rules on investor rights if technology vendors, wallets or ledger nodes fail.

Sebi’s review also comes amid wider changes in India’s debt-market architecture. Online bond platforms have been brought under regulation, the request-for-quote platform has gained traction, and policymakers are looking at ways to widen access to international debt products through regulated channels. Municipal bonds are also drawing greater attention as cities seek market funding for infrastructure, adding to the broader shift from bank-led financing towards market-based debt.

Investor protection will remain the central test for any relaxation. Lighter disclosure rules could encourage more issuers to list debt securities, but weaker transparency would raise borrowing costs and damage confidence if defaults rise or credit risks are obscured. The most likely outcome is a calibrated framework that retains credit-critical reporting while reducing requirements that do not materially affect bondholder decisions.

• 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.

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