Singapore is sharpening its AI strategy with a stronger emphasis on accelerating business adoption, expanding AI skills and strengthening governance, as the country seeks to translate AI capabilities into broader economic impact.
Speaking at IBM Think on Tour Singapore, Minister Josephine Teo said the updated approach builds on the National AI Strategy by narrowing the gap between widespread individual AI use and slower enterprise adoption.
Initiatives include the National AI Impact Programme, AI training for digital leaders, support for AI ‘bilinguals’ who combine domain expertise with AI knowledge, and expanded compute resources for organisations developing tailored AI applications.
Singapore is also working to make AI adoption more inclusive by helping professionals and SMEs access AI tools, skills and computing resources. Partnerships with technology companies will support organisations in identifying practical use cases and integrating AI into their operations.
Alongside expanding AI adoption, Singapore is strengthening governance frameworks to address risks associated with increasingly capable agentic AI systems. The country is also investing in quantum technologies as part of its broader ambition to position itself as a global AI innovation hub.
Why does it matter?
Singapore’s updated strategy reflects a broader shift in national AI policies from developing technical capabilities to accelerating adoption across the economy. Increasingly, governments are focusing not only on creating AI technologies but also on ensuring that businesses, workers and public institutions have the skills, infrastructure and support needed to use them effectively.
The strategy also demonstrates how AI competitiveness is becoming closely linked with governance and workforce development. By combining investment in AI adoption, talent and risk management, Singapore aims to strengthen its position as a regional AI hub while preparing for more advanced technologies such as agentic AI and quantum computing.
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The 19th session of the United Nations Expert Mechanism on the Rights of Indigenous Peoples (EMRIP), organised by the Office of the United Nations High Commissioner for Human Rights (OHCHR) in Geneva, highlighted progress under the International Decade of Indigenous Languages (2022–2032), with discussions placing renewed emphasis on the opportunities and governance challenges posed by digital technologies and AI.
A report presented during the session noted that digital technologies and AI can support language revitalisation while also raising questions about Indigenous Peoples’ rights to maintain ownership and control over their languages, cultural heritage and traditional knowledge. EMRIP member Janine Gama Bara urged caution to ensure these technologies are deployed in ways that respect Indigenous rights.
Participants highlighted several initiatives, including UNESCO’s Global Roadmap for Multilingualism in the Digital Era, launched in November 2025, partnerships with Unicode and the Internet Corporation for Assigned Names and Numbers (ICANN), and the newly launched New Commons Incubator for Indigenous Languages and Cultures.
Participants also pointed to persistent challenges in language transmission and called for sustained funding and stronger partnerships between governments, Indigenous Peoples and UN agencies. The session carries added significance as 2027 marks both the midpoint of the International Decade and the twentieth anniversary of the UN Declaration on the Rights of Indigenous Peoples, with recommendations from the meeting expected to inform the next phase of the Decade’s implementation.
Why does it matter?
The International Decade of Indigenous Languages was established to address the accelerating loss of Indigenous languages worldwide, and EMRIP provides an important mechanism for Indigenous representatives to shape international policy responses. The progress reported, from national action plans to digital preservation initiatives, suggests that implementation is increasingly moving from awareness-raising towards practical measures that support language revitalisation.
The discussion on AI highlights a broader challenge in digital governance. Technologies capable of preserving and promoting Indigenous languages can also expose communities to risks if linguistic and cultural knowledge is collected, reused or commercialised without appropriate safeguards. As governments and technology developers increasingly rely on AI, ensuring Indigenous ownership, consent and governance over language data is likely to become an increasingly important policy issue.
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The Spanish Data Protection Agency (AEPD) has published guidance examining how data quality relates to the GDPR’s accuracy principle when personal data is processed using AI. The document argues that, although closely linked, data quality is broader than the GDPR’s concept of accuracy because it also applies to non-personal data and encompasses requirements beyond the regulation.
According to the guide, properties such as veracity and currentness should only be required where they are genuinely necessary for the intended purpose.
The AEPD also stresses that non-personal data feeding into processes involving personal data must meet appropriate quality standards whenever it could influence the outcome. Assessing quality should not stop at the input stage: without objective metrics to evaluate the quality of outputs, the guide argues, organisations cannot determine whether a system is fulfilling its intended purpose.
The guidance has particular significance for AI datasets, stating that data which does not meet the required quality standards cannot be considered necessary for processing. Access to such data may therefore only be justified for the purpose of assessing its quality.
Why does it matter?
The guidance addresses a practical challenge that has become increasingly important as AI adoption expands. Organisations need large volumes of data to develop effective systems, while data protection law requires that personal data be limited to what is genuinely necessary. By distinguishing the broader concept of data quality from the GDPR’s narrower accuracy principle, the AEPD provides organisations with a more practical framework for deciding how much veracity, precision or currentness their data actually requires for a given purpose.
The emphasis on evaluating outputs as well as inputs also reflects a broader evolution in AI governance. Rather than treating data protection as a one-off compliance exercise at the point of data collection, the guidance encourages organisations to embed data quality assessment, accountability and multidisciplinary oversight throughout an AI system’s entire life cycle.
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South Korea has unveiled the ‘K-AI Package’, a development cooperation strategy that combines AI infrastructure, digital capacity development and development finance to support AI adoption in developing countries while expanding international opportunities for Korean technology companies.
The strategy was presented on 20 July during a ministerial meeting on global economic affairs chaired by Deputy Prime Minister and Minister of Economy and Finance Koo Yun Cheol.
The initiative combines AI solutions, AI data centres and renewable energy facilities with infrastructure projects financed through the Economic Development Cooperation Fund. It covers seven sectors, including water management, healthcare, energy, transport, agriculture, culture and education, with projects ranging from AI-supported dam management and medical diagnosis systems to smart farming, renewable energy technologies and AI education programmes.
South Korea plans to integrate the package with existing development cooperation mechanisms, including the Knowledge Sharing Program, trust funds, development finance and joint financing with multilateral development banks.
Alongside infrastructure projects, the government will support partner countries in developing AI legislation, standards and institutions while investing in technical education, workforce training and local capacity development.
From the second half of 2026, South Korea will identify partner countries’ priorities and propose tailored K-AI projects before moving to implementation. AI will also become a priority under the Knowledge Sharing Program in 2027, while a proposed global AI hub will support technology adoption and skills development in emerging economies.
Why does it matter?
The K-AI Package illustrates how development cooperation is becoming an increasingly important instrument of AI diplomacy. Rather than focusing solely on technology exports, South Korea is combining infrastructure, policy advice, skills development and financing to help partner countries build AI capacity.
The initiative also reflects growing international competition to shape the global AI ecosystem through development partnerships. By linking development assistance with industrial strategy, South Korea aims to expand opportunities for its AI industry while strengthening long-term digital cooperation with emerging economies.
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Microsoft and French AI company Mistral have expanded their strategic partnership to strengthen sovereign AI deployment in Europe, giving enterprises and regulated industries greater control over how frontier AI is deployed and managed.
A central element of the partnership is a multibillion-dollar investment in European AI infrastructure. Microsoft will leverage Mistral’s expanding GPU capacity, powered by thousands of NVIDIA Vera Rubin GPUs, to support AI training, inference and large-scale deployment while increasing capacity for Microsoft’s cloud and AI services.
The companies said the investment supports Europe’s AI ecosystem and aligns with Microsoft’s European Digital Commitments and Sovereign Cloud strategy.
The partnership also brings Mistral’s Medium 3.5 and OCR 4 models to Microsoft Foundry, while Medium 3.5 becomes available through Microsoft Copilot Studio. Organisations will be able to develop AI applications using Mistral’s multilingual models and deploy them consistently across Azure, Azure Local and fully disconnected environments without redesigning workloads.
According to the companies, the partnership is intended to support highly regulated sectors including healthcare, manufacturing, financial services and critical infrastructure, where sovereignty, resilience, privacy and operational continuity are key requirements.
Alongside joint customer engagement, Microsoft and Mistral will also provide proof-of-concept funding, Azure credits and technical workshops to accelerate enterprise AI adoption.
Why does it matter?
The partnership reflects a broader shift in enterprise AI from simply providing access to advanced models towards giving organisations greater control over where AI systems run, how data is handled and how regulatory requirements are met. Flexible deployment options are becoming increasingly important for governments and highly regulated industries.
The announcement also reinforces Europe’s ambition to build sovereign AI capabilities by combining domestic AI models, regional compute infrastructure and trusted cloud services. Rather than viewing sovereignty as an alternative to global technology partnerships, the collaboration illustrates how international companies and European AI developers are increasingly working together to strengthen regional digital resilience.
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UNESCO and Egypt’s Ministry of Education and Technical Education have launched a national Artificial Intelligence Competency Framework for Teachers, marking a major step in integrating AI into teacher training and education reform.
Based on UNESCO’s global framework and adapted to Egypt’s national priorities, the initiative makes Egypt one of the first countries to introduce a nationally tailored AI competency framework for teachers.
Developed through collaboration between the Ministry of Education and Technical Education, the Ministry of Communications and Information Technology and UNESCO, the framework aims to equip teachers with the knowledge, practical skills and ethical understanding needed to integrate AI into teaching and learning.
Its development was informed by consultations involving government institutions, universities, teacher training organisations, civil society, development partners and the private sector to ensure it reflects Egypt’s educational priorities and supports the Sustainable Development Goals.
The framework promotes a human-centred approach to AI, emphasising ethics, inclusion, critical thinking and human agency. UNESCO stresses that AI should enhance rather than replace teachers, helping to personalise learning, improve classroom experiences and increase efficiency while supporting responsible use.
The initiative aligns with UNESCO’s broader guidance, including the Beijing Consensus on Artificial Intelligence and Education, the Recommendation on the Ethics of Artificial Intelligence and the Santiago Consensus on Teachers.
During the implementation phase, UNESCO and the Egyptian government will introduce capacity-building programmes, develop Arabic-language AI learning resources, train AI master trainers and establish teacher training hubs.
The framework is also intended to serve as a national policy reference for integrating AI into teacher education, professional development and wider education reform.
Why does it matter?
The initiative illustrates how AI governance is increasingly moving from high-level principles to practical implementation in national education systems. Rather than focusing solely on AI technologies, the framework emphasises preparing teachers to use AI responsibly, ethically and effectively in the classroom.
It also demonstrates how international AI governance frameworks can be adapted to national contexts. By combining teacher training, policy development and locally relevant learning resources, Egypt is creating institutional capacity to support the long-term integration of AI into education while maintaining a human-centred approach.
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For much of the past decade, discussions on AI governance have focused on algorithms. Policymakers, regulators, and researchers have debated ethics, transparency, bias, accountability, and human oversight, seeking to ensure that AI systems remain trustworthy and aligned with fundamental rights.
These questions remain essential, but they are no longer sufficient to explain the direction of AI policy.
A new reality is emerging. Increasingly, governments are recognising that the ability to govern AI depends not only on regulating algorithms but also on securing the infrastructure that makes them possible. Data centres, advanced semiconductors, cloud computing, electricity grids, submarine cables, digital public infrastructure, and skilled workforces have become as strategically important as the AI models themselves.
The shift has been gradual rather than abrupt, yet it is now evident across national strategies, international organisations, and multilateral discussions. AI governance is expanding beyond software governance to become infrastructure governance.
AI runs on infrastructure, not algorithms alone
The rapid adoption of generative AI has often obscured a simple reality that every AI system rests on a vast physical and institutional foundation.
Large language models require enormous computing power, specialised chips, cloud infrastructure, reliable electricity, high-speed connectivity, trusted datasets, secure digital environments, and engineers capable of developing and maintaining increasingly complex systems. None of these components can be built overnight, and few can be developed without substantial public and private investment.
Image via Magnific
As a result, AI capability is increasingly determined by access to infrastructure, not just software.
Countries may have ambitious AI strategies, talented researchers, or innovative start-ups, but without advanced computing capacity, modern data centres, resilient cloud infrastructure, and skilled human capital, those ambitions become difficult to realise. Governments are therefore beginning to treat AI infrastructure as a strategic national asset rather than simply a commercial resource.
Industrial policy is becoming a pillar of AI policy
This shift is increasingly reflected in public policy.
The European Union has complemented its landmark AI Act with broader industrial initiatives, including the European Chips Act, the AI Factories initiative under EuroHPC, and, most recently, plans to develop AI Gigafactories capable of supporting the next generation of AI models. The recently adopted Digital Omnibus on AI also strengthens the role of the European Commission’s AI Office while simplifying implementation aspects, illustrating that regulation, institutional capacity, and infrastructure investment are evolving together.
The United States has pursued a different approach, combining export controls on advanced semiconductors with large-scale investment in domestic chip manufacturing and AI infrastructure. China continues to expand its national computing centres, cloud capacity, and state-backed AI ecosystems as part of its long-term industrial strategy.
Although these approaches differ politically and economically, they increasingly share the view that governing AI requires building the infrastructure that enables it.
AI policy, in other words, is beginning to resemble industrial policy.
Infrastructure has become a development issue
The infrastructure shift is equally visible in developing countries, where the conversation is moving beyond access to AI applications towards the ability to build AI capacity locally.
Throughout the World Summit on the Information Society (WSIS) Forum 2026, discussions repeatedly highlighted that meaningful digital transformation depends on foundational infrastructure. African policymakers, researchers, and technical experts argued that the continent suffers not primarily from a shortage of digital ambition, but from limited access to computing capacity.
Speakers pointed out that Africa has only a tiny share of global AI compute and data centre capacity, forcing many researchers and innovators to rely on infrastructure located elsewhere. The challenge, they argued, is no longer simply connectivity but compute sovereignty, the ability to build, train, and deploy AI systems locally.
The same message emerged during discussions on digital sovereignty. Rather than focusing solely on access to foreign AI models, participants argued that exporting raw data while importing AI services mirrors older economic patterns in which countries exported raw materials while importing higher-value finished products. Building local AI capability, therefore, requires investment in data centres, energy systems, cloud infrastructure, skills development, and trusted data ecosystems.
These discussions suggest that AI infrastructure is becoming an increasingly important component of development policy.
Infrastructure is also becoming geopolitics
The growing strategic importance of AI infrastructure extends far beyond economic development.
Competition over semiconductor manufacturing, cloud services, critical minerals, advanced computing facilities, and energy has become a defining feature of international relations. Governments increasingly view these assets through the lens of economic security, technological resilience, and geopolitical influence.
Submarine cables illustrate this evolution particularly well. Once regarded primarily as telecommunications infrastructure, they are now recognised as essential to AI systems that depend on massive volumes of cross-border data traffic. Recent international efforts to strengthen submarine cable resilience reflect how infrastructure once considered largely invisible has become central to digital governance.
Image via Magnific
Electricity is undergoing a similar transformation. As AI models become increasingly computationally intensive, access to affordable and reliable energy is becoming a key factor determining where data centres can be built and where AI innovation can flourish. This has encouraged governments to align AI strategies with broader industrial, energy, and climate policies.
In this environment, AI governance increasingly overlaps with trade policy, investment screening, industrial strategy, critical infrastructure protection, and national security.
A broader understanding of AI governance
This evolution does not diminish the importance of ethical AI principles or risk-based regulation. Questions surrounding transparency, accountability, bias, safety, and human rights remain fundamental.
Rather, it broadens the understanding of what AI governance entails.
Traditional AI governance focuses on how AI systems should be designed, deployed, and used responsibly. Infrastructure governance raises a different set of questions, such as who has access to the computing power needed to develop advanced AI? Who controls the cloud infrastructure that underpins AI services? Where are critical datasets stored? Which countries have the physical, financial, and institutional capacity to participate meaningfully in the AI economy?
image via Magnific
These questions ultimately shape who can innovate, compete, and benefit from AI.
As a result, debates that once focused primarily on algorithms increasingly encompass semiconductors, cloud infrastructure, digital public infrastructure, data centres, energy systems, connectivity, and workforce development.
From governing AI to enabling AI
The next phase of AI governance is likely to be defined less by entirely new regulatory principles than by long-term investment decisions.
Building trustworthy AI ecosystems will require governments to develop resilient digital infrastructure, strengthen education and digital skills, encourage research, modernise energy systems, expand computing capacity, and foster international cooperation on shared digital resources.
The countries that succeed may not simply be those that write the most comprehensive AI regulations. They may instead be those that create the conditions that allow responsible AI to flourish in the first place.
Image via Magnific
The global conversation about AI has not moved beyond governance. It has moved deeper into its foundations.
In this sense, the future of AI governance may increasingly depend not only on how societies regulate AI, but also on how they build the infrastructure that makes AI possible.
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The European Commission has adopted the Electrification Action Plan and proposed changes to the EU electricity market rules aimed at accelerating the digitalisation of Europe’s electricity system and preparing it to support growing AI infrastructure.
The measures are intended to reduce energy system costs, improve grid efficiency and accelerate the deployment of smart technologies while supporting a cleaner, more affordable and resilient electricity market.
A central element of the proposal is the accelerated rollout of smart meters, with a target of covering at least 50% of final electricity customers by 2030 and 75% by 2033. The devices will provide near real-time consumption data, enabling consumers to shift electricity use to cheaper and cleaner periods while helping network operators manage congestion and integrate renewable energy more efficiently.
The proposal also seeks to improve the secure exchange of electricity grid data between transmission and distribution system operators. Better access to harmonised, high-quality data is expected to strengthen grid planning, support renewable energy integration, encourage demand-side flexibility and enable new digital services.
The Commission also proposes a voluntary European framework for the secure reuse of grid data to support research and AI-powered energy management.
The initiative complements the EU’s broader Digital Union agenda, including the Data Act, the AI Act, the proposed Cloud and AI Development Act and the AI Continent Action Plan.
According to the Commission, smarter electricity networks will provide the infrastructure needed to support AI factories, AI gigafactories and sustainable data centres while strengthening Europe’s energy security, competitiveness and strategic autonomy.
Why does it matter?
The proposal reflects a growing recognition that electricity infrastructure and digital infrastructure are becoming increasingly interconnected. AI systems, cloud computing and data centres require abundant, reliable and flexible electricity supplies, making modernised energy networks a prerequisite for Europe’s digital competitiveness.
By linking smart grids, secure data sharing, AI-enabled energy management and electricity market reform, the Commission is also broadening the concept of digital governance. Rather than treating energy and digital policy separately, the EU is increasingly developing them as mutually reinforcing pillars of its industrial, climate and technological strategy.
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The Civil Liberties Union for Europe (Liberties) has published research arguing that general-purpose AI systems should not be relied upon for personalised voting advice, after assessing ChatGPT and Gemini during Hungary’s 2026 parliamentary election campaign.
According to Liberties, the research found that the AI systems frequently failed to match users with the appropriate political party, produced inconsistent responses to identical prompts and often continued offering political guidance despite initially stating that they could not provide voting recommendations. The researchers also found that the systems did not explain a clear methodology for political matching or produce reproducible results.
The report argues that general-purpose AI systems differ from established voting advice applications, which typically operate under dedicated public oversight and transparent methodologies. It found that the AI systems gave little consideration to strategic voting, coalition dynamics or local electoral factors when generating recommendations.
Liberties said the findings expose governance gaps between the EU AI Act and the Digital Services Act, calling for stronger safeguards for AI systems that provide political information. The organisation recommends that AI providers direct users to trusted election information sources rather than offering personalised voting advice.
Why does it matter?
The report highlights the growing role of general-purpose AI systems as intermediaries for political information, even though they were not designed or regulated to function as voting advice services. Inaccurate, inconsistent or non-transparent recommendations could undermine informed electoral decision-making if users place undue trust in AI-generated guidance.
The findings also contribute to the broader debate over AI governance in democratic processes. As generative AI becomes more widely used during elections, policymakers may need to clarify how existing digital regulations apply to AI systems that influence political information without fitting neatly into established regulatory categories.
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India’s Ministry of Finance has outlined a series of measures to strengthen the country’s fintech ecosystem, combining financial regulation, cybersecurity, digital payments oversight and consumer protection to support innovation while managing emerging risks.
According to the ministry, the Reserve Bank of India (RBI) has introduced a framework for Self-Regulatory Organisations in the fintech sector to promote ethical conduct, market integrity, transparency and accountability. The RBI has also implemented Digital Payment Security Controls for banks and expanded its Regulatory Sandbox to enable the testing of innovative financial products and services in a controlled environment.
The ministry also highlighted wider efforts to strengthen digital security. The Ministry of Electronics and Information Technology introduced the Digital Personal Data Protection Act, 2023, and the Digital Personal Data Protection Rules, 2025, while the National Payments Corporation of India uses AI and machine learning to help banks detect fraudulent Unified Payments Interface (UPI) transactions. Meanwhile, the Ministry of Home Affairs has expanded consumer protection through the National Cybercrime Reporting Portal, the 1930 helpline and public awareness programmes.
The measures were outlined in a written reply to Parliament, with the Ministry of Finance describing them as part of an ongoing effort to strengthen oversight, support fintech innovation and improve consumer protection across India’s digital financial ecosystem.
Why does it matter?
The update illustrates how fintech regulation is evolving beyond financial supervision to encompass cybersecurity, data protection, fraud prevention and digital trust. Rather than relying on a single regulatory instrument, India is building a layered governance framework designed to support innovation while improving resilience and consumer confidence.
The measures also demonstrate the increasingly coordinated roles of financial regulators, government ministries and industry bodies in overseeing digital finance. AI-based fraud detection, regulatory sandboxes and stronger reporting mechanisms are becoming complementary tools for managing the risks associated with rapidly expanding digital payment ecosystems.
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