Capacity development Archives | Digital Watch Observatory

EU launches Cybersecurity Skills Coalition EDIC

The European Commission and participating member states have launched the Cybersecurity Skills Coalition European Digital Infrastructure Consortium to strengthen cybersecurity skills across the EU.

The consortium, known as CSC-EDIC, will support the implementation of the EU Cybersecurity Skills Academy, a flagship initiative launched by the Commission in 2023.

Announced during Digital Skills EU Days 2026, the consortium will be based in Athens. Greece, Cyprus, Austria, Croatia and Slovenia are founding members, while Czechia and Poland have joined as observers. Other member states will be able to join later.

The Commission said CSC-EDIC will develop and deliver tailored cybersecurity training programmes, measure cybersecurity skills gaps and serve as the secretariat for the Industry-Academia Network.

Working with ENISA, the consortium will also support cyber resilience in critical sectors, particularly the healthcare sector. Planned activities include an EU-wide attestation scheme for cybersecurity skills, career pathways and micro-credentials.

The initiative has received a €3.1 million grant from the Digital Europe Programme to support its initial governance, staffing and operations.

The Commission said the Cybersecurity Skills Academy has already secured 26 industry pledges, helping train more than 900,000 cybersecurity professionals. Ten partnerships have also been established through the Industry-Academia Network.

Why does it matter?

Europe’s cybersecurity workforce shortage affects the resilience of governments, businesses and critical sectors such as healthcare. CSC-EDIC gives member states a formal structure to pool resources, coordinate training and align skills development with EU cyber priorities. The initiative also shows how the EU is treating cybersecurity capacity as part of digital infrastructure, rather than solely as a labour-market issue.

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UN scientific panel publishes first global AI assessment ahead of Geneva governance dialogue

The United Nations’ Independent International Scientific Panel on Artificial Intelligence has published its first preliminary report, providing an evidence-based assessment of AI’s opportunities, risks, and societal impacts ahead of next week’s inaugural Global Dialogue on AI Governance in Geneva. Rather than prescribing specific policies, the report aims to inform international discussions by providing an independent scientific foundation for AI governance decision-making.

Established by the UN General Assembly in August 2025 following commitments made in the Global Digital Compact, the panel brings together 40 independent experts from academia, civil society, the private sector, and the technical community. It is the first permanent UN scientific body dedicated exclusively to assessing the development and societal implications of AI. The report will serve as a key input to the Global Dialogue on AI Governance, which takes place on 6–7 July alongside the World Summit on the Information Society (WSIS) Forum and the AI for Good Global Summit in Geneva.

The preliminary report examines AI through four broad dimensions:

Scientific and technological developments;
Opportunities for sustainable development;
Emerging risks;
Approaches to international governance.

Instead of advocating a particular regulatory model, the panel seeks to establish a shared evidence base that can support future policymaking and international cooperation on AI.

Rather than focusing solely on risks, the report examines AI’s growing role across sectors, including healthcare, education, agriculture, scientific research, and public administration. It describes AI as a general-purpose technology with the potential to accelerate innovation, improve productivity, and expand access to knowledge and public services. At the same time, the panel notes that these benefits remain unevenly distributed across countries and regions, with significant disparities in access to computing infrastructure, technical expertise, and digital resources.

The report estimates that more than one billion people now use AI-powered services each week, while frontier AI capabilities remain concentrated among a relatively small number of companies and countries. According to the panel, this concentration extends beyond AI models themselves to include computing infrastructure, specialised hardware, large-scale datasets, and technical talent, raising broader questions about equitable access to AI and the distribution of its benefits.

The panel also highlights the challenges facing developing countries, warning that many risk becoming primarily consumers rather than producers of AI technologies if investment in local infrastructure, research ecosystems, digital skills, and governance capacity does not keep pace with global developments. It identifies multilingual AI, locally relevant datasets, and stronger scientific capabilities as important factors in ensuring that AI systems better reflect diverse societies and languages rather than reinforcing existing global disparities.

Alongside these opportunities, the report identifies a range of emerging risks associated with increasingly capable AI systems. These include the use of AI for cyberattacks, fraud, disinformation, election interference, and other malicious activities, as well as broader concerns related to market concentration, transparency, and the growing dependence of many countries on a limited number of AI providers. The panel also notes that many governments currently lack the technical capacity to evaluate the most advanced frontier AI models independently.

Beyond security-related concerns, the report identifies environmental sustainability as an increasingly important governance issue. It notes that the rapid expansion of AI requires increasing amounts of computing power, electricity, water, and specialised hardware, and argues that future AI development should balance technological progress with efficient resource use and broader sustainable development objectives.

Speaking at the report’s launch, UN Secretary-General António Guterres said that the pace of AI development requires stronger international cooperation grounded in scientific evidence and inclusive dialogue.

Panel co-chair Maria Ressa described the publication as an independent scientific assessment designed to inform, rather than replace, intergovernmental decision-making. The report itself states that ‘effective AI governance requires international cooperation,’ while recognising that governance approaches will continue to reflect different national circumstances and policy priorities.

The publication marks the first major output of the Independent International Scientific Panel on AI since its establishment under the Global Digital Compact. Future reports are expected to provide regular scientific assessments of AI capabilities, impacts, and governance challenges as the technology continues to evolve.

Why does it matter?

As governments, international organisations, researchers, and industry representatives gather in Geneva next week for the inaugural Global Dialogue on AI Governance, the preliminary report is expected to provide an important reference point for discussions on the future of AI. By combining scientific evidence with a broad assessment of opportunities, risks, and governance considerations, it seeks to support a more informed international conversation on how AI can contribute to sustainable development, human rights, and shared global prosperity.

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Claude Science launches AI workbench for researchers

Claude Science has been launched as an AI workbench designed to streamline scientific research by bringing data analysis, coding and research tools into a single integrated environment. The platform is designed to help researchers analyse data, run multi-step workflows, and generate publication-ready outputs with full transparency.

The platform consolidates research tools such as databases, coding environments and analysis software, enabling scientists to work across disciplines without switching between applications. Outputs are fully auditable, with embedded code, workflow histories and documentation to support validation and reproducibility.

Claude Science also uses a multi-agent architecture comprising specialist agents and a reviewer agent that verifies calculations and citations. It can be deployed on local infrastructure or high-performance computing systems, allowing institutions to scale AI-assisted research while keeping sensitive data within their own environments.

Why does it matter?

Claude Science reflects a broader evolution of AI from a standalone assistant to an integrated research platform. By combining specialised AI agents, computational tools and transparent workflows in a single environment, it aims to simplify scientific research while improving reproducibility and collaboration.

The platform also raises broader questions about the future of AI in science. As researchers increasingly rely on AI to support data analysis and experimentation, ensuring transparency, validation and institutional control over sensitive research data will be essential to maintaining scientific integrity and trust.

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World Bank links Poland’s growth outlook to AI adoption

A World Bank Group report says faster AI adoption could significantly raise Poland’s economic output by 2035, but only if firms, workers and institutions can absorb the technology effectively.

The report estimates that Poland’s real GDP could be between 1.3% and 12.1% higher by 2035 than in a scenario where AI adoption remains at current levels. It also suggests that gains of 2% to 3% could appear within the next three years.

The estimates are based on a scenario in which AI adoption expands from 8.4% of Polish firms today to close to 45% by 2035. The report says adoption remains far below Denmark, where 42% of firms use AI.

Poland has several strengths, including 607,000 IT specialists, the largest pool in Central and Eastern Europe, and a high level of government AI readiness. However, only 50.4% of individuals have digital skills, compared with 60.4% across the EU.

The report says 48% of Polish workers are in highly AI-exposed occupations, below the EU average of 53%. It stresses that AI exposure does not automatically imply job losses, but can lead to either displacement or augmentation depending on skills, firm adoption and institutional support.

According to the World Bank, the main challenge is not only access to AI technology but also integrating it into business processes and enabling workers to move into higher-productivity roles.

The report calls for stronger labour-market monitoring, reskilling, support for firm-level AI adoption and policies that help Poland convert AI exposure into productivity gains.

Why does it matter?

The report frames AI adoption as a strategic economic issue, not only a technology upgrade. Poland already has strong digital foundations, including a large IT workforce, but low firm-level AI use could limit productivity gains if adoption does not accelerate. The findings also show that skills, labour mobility and institutional support will determine whether AI exposure leads to better jobs and higher productivity or deeper labour-market frictions.

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OpenAI launches GeneBench-Pro for AI biology research

OpenAI has introduced GeneBench-Pro, a research benchmark designed to assess whether AI agents can perform the complex, judgment-intensive analysis required in real-world computational biology.

Unlike conventional benchmarks that focus on factual recall or routine workflows, GeneBench-Pro is designed to measure what OpenAI calls ‘research taste‘, the sequence of judgement calls involved in scientific analysis, from interpreting ambiguous data and revising assumptions to deciding whether findings are robust enough to inform downstream research.

The benchmark comprises 129 problems spanning ten domains within computational biology, including statistical genetics, cancer genomics, clinical diagnostics, and pharmacogenomics. Each problem presents an AI agent with a realistic and deliberately messy dataset, brief experimental context, and a target to estimate.

To answer correctly, the model must explore the data iteratively, select an appropriate analytical approach, and supply a final answer without exploiting shortcuts or matching arbitrary author preferences. To prevent common benchmark shortcuts, every problem uses synthetically generated data whose underlying causal structure is fully known, allowing performance to be measured against a controlled ground truth.

OpenAI said its flagship model, GPT-5.6 Sol, achieved a pass rate of 28.7% at the highest reasoning setting, increasing to 31.5% in Pro mode. By comparison, the strongest model available when the original GeneBench was introduced scored below 5%.

External reviewers estimated that completing a typical GeneBench-Pro task would require 20 to 40 hours of expert work and cost thousands of dollars, whereas AI inference currently costs only a few dollars per run. OpenAI argues this suggests substantial economic potential even before models achieve expert-level performance.

OpenAI acknowledged that frontier models still solve fewer than one-third of the benchmark problems, often making partial progress but failing to complete the full chain of scientific reasoning expected from experienced researchers. To encourage independent evaluation, the company is open-sourcing ten representative tasks on Hugging Face and providing a 50-question subset to Artificial Analysis for third-party benchmarking.

Why does it matter?

GeneBench-Pro reflects a broader shift in AI evaluation from testing factual knowledge and coding ability to assessing whether models can support complex scientific reasoning. As computational biology increasingly becomes limited by data interpretation rather than data generation, reliable AI assistance in analytical workflows could accelerate research in areas such as genomics, drug discovery and precision medicine.

The benchmark also highlights the importance of rigorous evaluation methods for frontier AI. By using controlled synthetic datasets with known ground truth, GeneBench-Pro seeks to measure not only whether models reach the correct answer but also how well they make the sequence of judgements required in real-world scientific research.

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Google says AI power users drive UK productivity gains

Workplace AI adoption in the UK has more than doubled over the past year, reaching 73%, according to a new Google report. However, the benefits remain uneven, with a small group of advanced users seeing significantly stronger career outcomes than the wider workforce.

The report categorises workers into four groups: AI Spectators, who have not yet engaged with the technology; Experimenters, who use basic AI functions; Practitioners, who use AI regularly; and AI Trailblazers, who apply it in advanced and innovative ways.

Although AI Trailblazers account for just 15% of users, they report significantly better outcomes, including faster promotions, larger pay increases and substantial weekly time savings.

The report found that advanced users outperform other workers across several indicators, including promotions, performance reviews and salary growth. However, differences in adoption across age, gender and geography suggest that unequal access to AI skills could widen existing labour market disparities.

Google argues that closing this gap will require greater investment in AI literacy, organisational support and workplace culture. Initiatives such as national upskilling programmes and diagnostic tools are intended to help workers progress from basic experimentation to more advanced AI use, supporting broader productivity growth.

Why does it matter?

The findings suggest that simply adopting AI is not enough to generate widespread economic benefits. The greatest productivity and career gains are concentrated among workers who integrate AI deeply into their daily work, highlighting the importance of skills development and organisational support.

The report also points to a growing policy challenge. If access to advanced AI skills continues to vary across demographic groups and regions, AI could widen existing inequalities in the labour market. Expanding AI literacy and helping more workers move beyond basic use may therefore be as important as increasing adoption itself.

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UNESCO expands digital literacy training for educators

Around 10,000 literacy educators worldwide have completed a UNESCO Institute for Lifelong Learning digital skills course designed to strengthen the use of technology in literacy education.

The multilingual course was launched in December 2025 by the Secretariat of the Global Alliance for Literacy, in collaboration with Huawei. It is available in Arabic, English, French and Spanish.

The programme focuses on practical digital skills that educators can apply in literacy classrooms. It also encourages participants to use digital tools responsibly, evaluate online information critically and understand how technologies, including AI, shape learning and information use.

UNESCO said literacy today goes beyond reading and writing, requiring learners and educators to navigate digital environments and participate confidently in societies increasingly mediated by technology.

The course is delivered through 11 self-paced sessions and encourages educators to reflect on their teaching practice while developing new skills.

Participants from countries including Mexico, Pakistan and Togo reported stronger confidence in using digital tools, more learner-centred teaching approaches and greater use of collaboration and assessment technologies.

UNESCO said national and municipal adult education agencies, adult learning providers and UNESCO Learning Cities are helping expand the course across countries.

Why does it matter?

Digital literacy is becoming essential for both educators and learners, especially as AI and online platforms reshape access to information. Training literacy educators first can create a multiplier effect, helping adult learners and underserved communities build practical digital skills, critical thinking and confidence in online environments. The programme also shows how international education initiatives are moving beyond access to focus on effective and responsible use of technology.

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South Korea boosts AI funding to strengthen global competitiveness

South Korea’s National Assembly has approved a supplementary budget of 1.9067 trillion won for the AI sector, increasing the government’s original proposal by 61.8 billion won to strengthen the country’s global AI competitiveness. The Ministry of Science and ICT said the funding would be used to swiftly advance initiatives aimed at strengthening national AI competitiveness and positioning the country among the world’s top three AI leaders.

The funding is focused on three priorities: expanding AI computing infrastructure, advancing next-generation AI models and developing world-class talent. The largest allocation, 1.6341 trillion won, will be used to secure 10,000 advanced GPUs by the end of the year, alongside the leasing of a further 3,000 GPUs from the private sector to expand access.

A further 213.6 billion has been allocated to the proposed World Best LLM Project, under which five leading domestic AI teams will receive up to three years of support, including access to GPUs, high-quality datasets and specialist personnel. The Ministry will also launch the AI Pathfinder Project, offering grants of up to 2 billion won annually to attract leading international AI researchers.

Science and ICT Minister Yoo Sang-im said the funding comes at a pivotal moment as countries intensify competition for AI leadership. He said the government would pursue an all-out effort spanning advanced technology, talent development and AI adoption to establish South Korea among the world’s top three AI powers.

Why does it matter?

The supplementary budget demonstrates how governments are increasingly treating AI as strategic national infrastructure rather than simply an innovation policy issue. By investing simultaneously in computing capacity, foundation models and talent, South Korea is seeking to strengthen its long-term competitiveness in a global race increasingly defined by access to GPUs and skilled researchers.

The initiative also highlights that leadership in AI depends on more than financial investment alone. Competition for advanced chips and world-class talent has become increasingly intense, meaning the success of South Korea’s strategy will depend on how quickly it can translate funding into deployable infrastructure, cutting-edge research and commercial innovation.

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India launches WhatsApp chatbot for public health services

India’s Union Health Minister Jagat Prakash Nadda has launched Ayushman Sarathi, a WhatsApp chatbot developed by the National Health Authority to provide round-the-clock access to services under the Ayushman Bharat Pradhan Mantri Jan Arogya Yojana (PM-JAY), the country’s government-funded health insurance scheme.

Built on secure API integrations with the PM-JAY digital platform, the chatbot enables beneficiaries to check their eligibility, apply for and download Ayushman Cards, complete electronic Know Your Customer (eKYC) verification, link their Aadhaar identity, view treatment history, locate empanelled hospitals and access other PM-JAY services directly through WhatsApp.

Users can also register, track and withdraw grievances, request callbacks, view wallet balances, and submit feedback after hospital discharge. According to the Ministry of Health and Family Welfare, the chatbot is intended to improve accessibility, transparency and beneficiary engagement while reducing reliance on physical visits and call centres.

The chatbot also supports feedback collection and grievance management, providing health authorities with additional insight into service delivery while helping monitor PM-JAY implementation across India.

Why does it matter?

The launch reflects India’s continued expansion of digital public services by integrating government programmes into widely used consumer platforms. Delivering PM-JAY services through WhatsApp could make it easier for millions of beneficiaries to access healthcare information and administrative services without visiting government offices or contacting call centres.

The initiative also illustrates how digital public infrastructure is reshaping healthcare delivery. By combining secure digital identity, online service access and feedback mechanisms, the chatbot aims to improve efficiency, transparency and user engagement while supporting better monitoring of one of the world’s largest public health insurance programmes.

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Chief AI Officers to lead AI adoption across Australian government

Australian public service agencies are formalising the appointment of Chief AI Officers (CAIOs) to guide the safe, strategic and coordinated use of AI across government.

Under the APS AI Plan, all non-corporate Commonwealth entities must appoint a senior leader as Chief AI Officer by 30 June 2026. Corporate Commonwealth entities and Commonwealth companies are strongly encouraged to make similar appointments.

The role is intended to help agencies adopt and use AI, particularly generative AI, as the technology reshapes government operations, public service delivery and internal processes.

Chief AI Officers will complement, rather than replace, AI Accountable Officials. While Accountable Officials focus on governance, compliance and risk management, CAIOs will lead strategic adoption, organisational transformation and AI capability building.

The government said CAIOs should provide strategic leadership rather than focus primarily on technical implementation. Their responsibilities include identifying high-value AI use cases, building staff capability, championing responsible adoption and ensuring AI is deployed safely and effectively.

CAIOs will work across technology, data, policy, cybersecurity, privacy and human resources functions, while collaborating with counterparts across the Australian Public Service and the Department of Finance’s AI Delivery and Enablement team.

Chief AI Officers will also collaborate across the Australian Public Service, including with other CAIOs and the AI Delivery and Enablement function in the Department of Finance.

The government said AI should be viewed as a general-purpose capability rather than a conventional technology upgrade, reflecting its potential to transform multiple areas of public-sector work.

The CAIO role is intended to help agencies move from experimentation to more systematic and responsible adoption. It is also designed to support a whole-of-organisation view of AI risks and opportunities.

The AI Delivery and Enablement team has developed an information pack to support agencies in appointing CAIOs, along with a blog for newly appointed leaders.

A wide range of agencies have already appointed Chief AI Officers. The published list includes major departments, regulators, integrity bodies, health and research agencies, cultural institutions, security agencies and service delivery organisations.

A wide range of organisations have already appointed CAIOs, including major government departments, regulators, law enforcement bodies, research organisations and service delivery agencies such as the Department of Finance, Home Affairs, Treasury, the Australian Federal Police, Services Australia and the Australian Electoral Commission.

The appointments of Chief AI Officers reflect a broader effort to coordinate AI adoption across government while maintaining attention to safety, privacy, cybersecurity, governance and public value.

Why does it matter?

Australia’s initiative reflects a broader shift from experimental AI projects to coordinated, organisation-wide adoption across the public sector. By establishing dedicated AI leadership roles, the government is seeking to embed strategic oversight while ensuring that innovation is balanced with governance, privacy, cybersecurity and public accountability.

The creation of Chief AI Officers also highlights the growing recognition that AI adoption is an organisational transformation challenge rather than solely a technical one. As governments integrate AI into public services, dedicated leadership is becoming increasingly important to coordinate implementation, build capability and ensure AI delivers public value responsibly.

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