Amul, the world’s largest dairy cooperative, has launched what it calls Amul AI: a platform built on five decades of cooperative data, designed to give every farmer in its network round-the-clock, personalised guidance in their own language.

Amul was launched just ahead of India’s AI Impact Summit 2026 and backed by the Ministry of Electronics and Information Technology (MeitY) with the EkStep Foundation. It is a test case for whether AI – the kind being debated in boardrooms and policy forums globally – can actually reach the last mile.

Meet Sarlaben: The AI dairy farming assistant

Sarlaben draws from one of India’s most comprehensive agricultural data repositories. It’s accessible via the Amul Farmer mobile app – already downloaded by over 10 lakh (one million) users on Android and iOS – as well as through voice calls for farmers using feature phones or landlines.

The system is integrated with Amul’s Automatic Milk Collection System (AMCS) and the Pashudhan application, allowing it to offer personalised, cattle-specific guidance.

What makes Amul AI substantially different from most agricultural chatbots is the scale of its training data. The platform was built on a digital backbone managing over 200 crore (two billion) milk procurement transactions annually, veterinary treatment records from more than 1,200 doctors covering nearly 3 crore (30 million) cattle, approximately 70 lakh (seven million) artificial inseminations conducted each year, ISRO satellite imagery for fodder production mapping, and a cattle census conducted every five years.

Every animal in the system carries a unique ID, with individual records of feed intake, disease history and milking status. “Amul AI is about taking dependable, verified information directly to the farmer – instantly and in a language they are comfortable with,” said Jayen Mehta, Managing Director of the Gujarat Cooperative Milk Marketing Federation (GCMMF), which markets the Amul brand.

He said how, by using decades of structured data and integrating it with their operational systems, the platform will help farmers make timely decisions that improve animal productivity and income.

India’s productivity paradox

India is the world’s largest producer of milk, generating 347.87 million tonnes in 2024-25 according to the Department of Animal Husbandry and Dairying – more than double the US’s 102.70 million tonnes. And yet despite leading in volume, India’s per-animal milk yield remains among the lowest globally.

The reasons are structural. India’s dairy sector is characterised by small herd sizes, low-quality feed, limited access to veterinary care in rural areas, and widespread lack of awareness about modern breeding and husbandry practices. Amul’s network spans more than 18,600 villages in Gujarat, where farmers supply over 350 lakh litres (35 million litres) of milk daily.

But information asymmetry has long been a bottleneck – a farmer facing a sick animal at midnight in a remote village has few places to turn; the gap Amul AI is designed to close.

Available initially in Gujarati – the primary language of the cooperative’s farmer base – the platform is built on the government’s Bhashini multilingual framework and could, in principle, be extended to 20 Indian languages, reaching Amul’s presence in 20,000 villages in 20 states.

The cooperative model

The technology story here is inseparable from the institutional one. Amul’s cooperative structure – built over five decades under the original White Revolution – created the data infrastructure that makes Amul AI possible.

Most private agri-tech startups are working backwards: collecting data first, building products second. Amul already had the data. What was needed was a way to make it actionable at the farmer level.

Experts tracking the dairy-tech space see this as significant. Sreeshankar Nair, Founder of Brainwired, a dairy-tech startup, identifies three specific challenges that Amul AI could meaningfully address: farmer awareness, access to quality veterinary guidance, and connectivity to grazing and feed resources.

“If AI can integrate local dialects of Indian languages, India can have White Revolution 2.0,” Nair said, pointing to the transformative potential of vernacular AI in a sector where not every farmer speaks the same dialect.

Saswata Narayan Biswas, Director of the Institute of Rural Management, Anand (IRMA) – the institution closely associated with Amul’s founding ethos – frames it as an AI embedded in a cooperative framework. It becomes “not a technology upgrade, but an instrument of inclusive rural transformation.”

For Biswas, the specific abilities Amul AI brings – predictive disease detection, oestrus tracking, optimised feed formulation, localised weather risk advisories – are abilities Amul had been building for years. AI accelerates and democratises them.

Scale and the test ahead

The launch has drawn backing from the highest levels of government. Gujarat Chief Minister Bhupendra Patel launched the platform and confirmed it will be showcased at the AI Impact Summit 2026. The cooperative has acknowledged MeitY and the EkStep Foundation – an open digital infrastructure nonprofit – as partners in building the AI layer.

Farmers not affiliated with Amul can also access general dairying and animal husbandry information through the app. At its current scale, Amul AI already covers more cattle – nearly 3 crore (30 million) – than most national veterinary databases anywhere in the world.

The harder question, as with most AI deployments at a population scale, is whether the tool will serve those who need it most. The farmers most likely to benefit first – those already comfortable with smartphones, already plugged into Amul’s digital system – may not be the ones with the greatest information deficit.

The rollout of Bhashini-enabled dialect support, the adoption rate among feature-phone users relying on voice calls, and whether AI-driven advisories translate into measurable yield improvements will be the metrics that determine whether this is genuinely White Revolution 2.0.

Amul has built an AI system grounded in half a century of real cooperative transactions, real animals, and real farmers. Such an infrastructure is, arguably, the most credible foundation for AI dairy farming at scale. Whether it fulfils its promise will depend on execution – and on whether Sarlaben’s voice can reach in the last few miles; those that have always been the hardest to cross.

See also: Hitachi bets on industrial expertise to win the physical AI race

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Public sector organisations often hold large archives of historical data that do not inform forward-looking decisions. A partnership between the University of Hertfordshire and regional NHS health bodies addresses this issue by applying machine learning to operational planning. The project analyses healthcare demand to assist managers with decisions regarding staffing, patient care, and resources.

Most AI initiatives in healthcare focus on individual diagnostics or patient-level interventions. The project team notes that this tool targets system-wide operational management instead. This distinction matters for leaders evaluating where to deploy automated analysis within their own infrastructure.

The model uses five years of historical data to build its projections. It integrates metrics such as admissions, treatments, re-admissions, bed capacity, and infrastructure pressures. The system also accounts for workforce availability and local demographic factors including age, gender, ethnicity, and deprivation.

Iosif Mporas, Professor of Signal Processing and Machine Learning at the University of Hertfordshire, leads the project. The team includes two full-time postdoctoral researchers and will continue development through 2026.

“By working together with the NHS, we are creating tools that can forecast what will happen if no action is taken and quantify the impact of a changing regional demographic on NHS resources,” said Professor Mporas.

Using AI for forecasting in healthcare operations

The model produces forecasts showing how healthcare demand is likely to change. It models the impact of these changes in the short-, medium-, and long-term. This capability allows leadership to move beyond reactive management.

Charlotte Mullins, Strategic Programme Manager for NHS Herts and West Essex, commented: “The strategic modelling of demand can affect everything from patient outcomes including the increased number of patients living with chronic conditions.

“Used properly, this tool could enable NHS leaders to take more proactive decisions and enable delivery of the 10-year plan articulated within the Central East Integrated Care Board as our strategy document.” 

The University of Hertfordshire Integrated Care System partnership funds the work, which began last year. Testing of the AI model tailored for healthcare operations is currently underway in hospital settings. The project roadmap includes extending the model to community services and care homes.

This expansion aligns with structural changes in the region. The Hertfordshire and West Essex Integrated Care Board serves 1.6 million residents and is preparing to merge with two neighbouring boards. This merger will create the Central East Integrated Care Board. The next phase of development will incorporate data from this wider population to improve the predictive accuracy of the model.

The initiative demonstrates how legacy data can drive cost efficiencies and shows that predictive models can inform “do nothing” assessments and resource allocation in complex service environments like the NHS. The project highlights the necessity of integrating varied data sources – from workforce numbers to population health trends – to create a unified view for decision-making.

See also: Agentic AI in healthcare: How Life Sciences marketing could achieve $450B in value by 2028

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For CIOs, it’s part of a broader move away from fragmented and project-specific AI pilots toward a more platform engineering approach. By standardising on Red Hat’s infrastructure, the MOD aims to decouple its AI capabilities from underlying hardware, allowing algorithms to be developed once and deployed anywhere—whether on-premise, in the cloud, or on disconnected field devices.

Red Hat industrialises the AI lifecycle for the MOD

The agreement focuses on the Defence Digital Foundry, the MOD’s central software delivery hub. The Foundry will now provide a consistent MLOps environment to all service branches, including the Royal Navy, British Army, and Royal Air Force.

At the core of this initiative is Red Hat AI, a suite that includes Red Hat OpenShift AI. This platform addresses a familiar bottleneck in enterprise AI: the “inference gap” between data science teams and operational infrastructure.

The new agreement will allow MOD developers to collaborate on a single platform, choosing the most appropriate AI models and hardware accelerators for their specific mission requirements without being locked into a single vendor’s ecosystem.

This standardisation is vital for “enabling AI at scale,” according to Red Hat. By unifying disparate efforts, the MOD intends to reduce the duplication that often plagues large government IT programs. The platform supports optimised inference, ensuring that AI models can run efficiently on restricted hardware footprints often found in military environments.

Mivy James, CTO at the UK MOD, said: “Easing access to Red Hat platforms becomes all the more important for the UK Ministry of Defence in the era of AI, where rapid adoption, replicating good practice, and the ability to scale are critical to strategic advantage.”

Bridging legacy and autonomous systems

A major hurdle for defence modernisation is the coexistence of legacy virtualised workloads with modern, containerised AI applications. The agreement includes Red Hat OpenShift Virtualization, which provides a “well-lit migration path” for existing systems. This allows the MOD to manage traditional virtual machines alongside new neural networks on the same control plane to reduce operational complexity and cost.

The MOD deal also incorporates Red Hat Ansible Automation Platform to drive enterprise-wide AI automation. In an AI context, automation is the enforcement mechanism for governance. It ensures that as models are retrained and redeployed, the underlying configuration management, security orchestration, and service provisioning remain compliant with rigorous defence standards.

Security and ecosystem alignment

Deploying AI in defence naturally requires a “consistent security footprint” that can withstand sophisticated cyber threats.

The Red Hat platform enables DevSecOps practices, integrating security gates directly into the software supply chain. This is particularly relevant for maintaining a trusted software pedigree when integrating code from approved third-party providers, who can now align their deliverables with the MOD’s standardised Red Hat environment.

Joanna Hodgson, Regional Manager for the UK and Ireland at Red Hat, commented: “Red Hat offers flexibility and scalability to deploy any application or any AI model on their choice of hardware – whether on premise, in any cloud, or at the edge – helping the UK Ministry of Defence to harness the latest technologies, including AI.”

The deployment shows that AI maturity is moving beyond the model itself to the infrastructure that supports it. Success in high-stakes environments like defence depends less on individual algorithm performance and more on the ability to reliably deliver, update, and govern those models at scale.

See also: Chinese hyperscalers and industry-specific agentic AI

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The contract represents a broader shift in how public sector bodies approach automation. Rather than layering AI tools over legacy infrastructure, HMRC is replacing the underlying architecture to support machine learning and automated decision-making natively.

The AI-powered modernisation effort focuses on the Enterprise Tax Management Platform (ETMP), the technological backbone responsible for managing over £800 billion in annual tax revenue and which currently supports over 45 tax regimes. By migrating this infrastructure to a managed cloud environment via RISE with SAP, HMRC aims to simplify a complex technology landscape that tens of thousands of staff rely on daily.

Effective machine learning requires unified data sets, which are often impossible to maintain across fragmented on-premise legacy systems. As part of the deployment, HMRC will implement SAP Business Technology Platform and AI capabilities. These tools are designed to surface insights faster and automate processes across tax administration.

SAP Sovereign Cloud meets local AI adoption requirements

Deploying AI in such highly-regulated sectors requires strict data governance. HMRC will host these new capabilities on SAP’s UK Sovereign Cloud. This ensures that while the tax authority adopts commercial AI tools, it adheres to localised requirements regarding data residency, security, and compliance.

“Large-scale public systems like those delivered by HMRC must operate reliably at national scale while adapting to changing demands,” said Leila Romane, Managing Director UKI at SAP.

“By modernising one of the UK’s most important platforms and hosting it on a UK sovereign cloud, we are helping to strengthen the resilience, security, and sustainability of critical national infrastructure.”

Using AI to modernise tax infrastructure

The modernisation ultimately aims to reduce friction in taxpayer interactions. SAP and HMRC will work together to define new AI capabilities specifically aimed at improving taxpayer experiences and enhancing decision-making.

For enterprise leaders, the lesson here is the link between data accessibility and operational value. The collaboration provides HMRC employees with better access to analytical data and an improved user interface. This structure supports greater confidence in real-time analysis and reporting; allowing for more responsive and transparent experiences for taxpayers.

The SAP project illustrates that AI adoption is an infrastructure challenge as much as a software one. HMRC’s approach involves securing a sovereign cloud foundation before attempting to scale automation. For executives, this underscores the need to address technical debt and data sovereignty to enable effective AI implementation in areas as regulated as tax and finance.

See also: Accenture: Insurers betting big on AI

Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is part of TechEx and is co-located with other leading technology events including the Cyber Security & Cloud Expo. Click here for more information.

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For both public and private sector technology leaders, the integration of LLMs into customer-facing platforms often stalls at the proof-of-concept stage. The UK’s Department for Science, Innovation, and Technology (DSIT) aims to bypass this common hurdle by operationalising its February 2025 Memorandum of Understanding with Anthropic.

The joint project, announced today, prioritises the deployment of agentic AI systems that are designed to actively guide users through processes rather than simply retrieving static information.

The decision to move beyond standard chatbot interfaces addresses a friction point in digital service delivery: the gap between information availability and user action. While government portals are data-rich, navigating them requires specific domain knowledge that many citizens lack.

By employing an agentic system powered by Claude, the initiative seeks to provide tailored support that maintains context across multiple interactions. This approach mirrors the trajectory of private sector customer experience, where the value proposition is increasingly defined by the ability to execute tasks and route complex queries rather than just deflect support tickets.

The case for agentic AI assistants in government

The initial pilot focuses on employment, a high-volume domain where efficiency gains directly impact economic outcomes. The system is tasked with helping users find work, access training, and understand available support mechanisms. For the government, the operational logic involves an intelligent routing system that can assess individual circumstances and direct users to the correct service.

This focus on employment services also serves as a stress test for context retention capabilities. Unlike simple transactional queries, job seeking is an ongoing process. The system’s ability to “remember” previous interactions allows users to pause and resume their journey without re-entering data; a functional requirement that is essential for high-friction workflows. For enterprise architects, this government implementation serves as a case study in managing stateful AI interactions within a secure environment.

Implementing generative AI within a statutory framework necessitates a risk-averse deployment strategy. The project adheres to a “Scan, Pilot, Scale” framework, a deliberate methodology that forces iterative testing before wider rollout. This phased approach allows the department to validate safety protocols and efficacy in a controlled setting, minimising the potential for compliance failures that have plagued other public sector AI launches.

Data sovereignty and user trust form the backbone of this governance model. Anthropic has stipulated that users will retain full control over their data, including the ability to opt out or dictate what the system remembers. By ensuring all personal information handling aligns with UK data protection laws, the initiative aims to preempt privacy concerns that typically stall adoption.

Furthermore, the collaboration involves the UK AI Safety Institute to test and evaluate the models, ensuring that the safeguards developed inform the eventual deployment.

Avoiding dependency on external AI providers like Anthropic

Perhaps the most instructive aspect of this partnership for enterprise leaders is the focus on knowledge transfer. Rather than a traditional outsourced delivery model, Anthropic engineers will work alongside civil servants and software developers at the Government Digital Service.

The explicit goal of this co-working arrangement is to build internal AI expertise that ensures the UK government can independently maintain the system once the initial engagement concludes. This addresses the issue of vendor lock-in, where public bodies become reliant on external providers for core infrastructure. By prioritising skills transfer during the build phase, the government is treating AI competence as a core operational asset rather than a procured commodity.

This development is part of a broader trend of sovereign AI engagement, with Anthropic expanding its public sector footprint through similar education pilots in Iceland and Rwanda. It also reflects a deepening investment in the UK market, where the company’s London office is expanding its policy and applied AI functions.

Pip White, Head of UK, Ireland, and Northern Europe at Anthropic, said: “This partnership with the UK government is central to our mission. It demonstrates how frontier AI can be deployed safely for the public benefit, setting the standard for how governments integrate AI into the services their citizens depend on.”

For executives observing this rollout, it once again makes clear that successful AI integration is less about the underlying model and more about the governance, data architecture, and internal capability built around it. The transition from answering questions to guiding outcomes represents the next phase of digital maturity.

See also: How Formula E uses Google Cloud AI to meet net zero targets

Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is part of TechEx and is co-located with other leading technology events including the Cyber Security & Cloud Expo. Click here for more information.

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According to reporting by Reuters, the Gates Foundation and OpenAI are backing a new initiative, Horizon1000, that aims to introduce AI tools into primary healthcare clinics across several African countries. The project will begin in Rwanda and is intended to reach 1,000 clinics and surrounding communities by 2028, supported by a combined $50 million investment.

The timing is not accidental as global development assistance for health fell by just under 27% last year compared to 2024, the Gates Foundation estimates, following cuts that began in the United States and spread to other major donors such as Britain and Germany. Those reductions have coincided with the first rise in preventable child deaths this century, adding pressure to health systems already stretched thin.

Rather than focusing on advanced diagnostics or research, Horizon1000 is framed around everyday tasks that consume time in under-resourced clinics. AI tools under the programme are expected to assist with patient intake, triage, record keeping, appointment scheduling, and access to medical guidance, particularly in settings where one doctor may serve tens of thousands of people.

Gates Foundation and OpenAI focus on AI support in healthcare

“In poorer countries with enormous health worker shortages and lack of health systems infrastructure, AI can be a gamechanger in expanding access to quality care,” Bill Gates wrote in a blog post announcing the initiative. Speaking to Reuters at the World Economic Forum in Davos, Gates said the technology could help health systems recover after aid cuts slowed progress.

“Our commitment is that that revolution will at least happen in the poor countries as quickly as it happens in the rich countries,” he said.

The focus, according to both partners, is on supporting healthcare workers rather than replacing them. OpenAI is expected to provide technical expertise and AI systems, while the Gates Foundation will work with African governments and health authorities to oversee deployment and alignment with national guidelines.

Rwanda was chosen as the first pilot country in part because of its existing digital health efforts. The country established an AI health hub in Kigali last year and has positioned itself as a testbed for health technology projects. Paula Ingabire, Rwanda’s minister of information and communications technology and innovation, said the goal is to reduce administrative burdens while expanding access.

“It is about using AI responsibly to reduce the burden on healthcare workers, to improve the quality of care, and to reach more patients,” Ingabire said in a video statement released alongside the launch.

Under Horizon1000, AI tools may also be used before patients reach clinics. Gates told Reuters the systems could support pregnant women and HIV patients with guidance ahead of visits, especially when language barriers exist between patients and providers.

What the AI tools are expected to handle

Once patients arrive, AI could help link records, reduce paperwork, and speed up routine processes.

“A typical visit, we think, can be about twice as fast and much better quality,” Gates said.

Those expectations highlight both the promise and the limits of the approach. While AI may help streamline workflows, its impact depends on reliable data, stable power and connectivity, trained staff, and clear oversight. Many previous digital health pilots in low-income settings have struggled to scale beyond initial trials once funding or external support tapered off.

Horizon1000’s designers say they are trying to avoid that pattern by working closely with local governments and health leaders rather than deploying one-size-fits-all systems. Tools are meant to be adapted to local clinical rules, languages, and care models. Even so, questions remain about long-term maintenance, data governance, and who bears responsibility if systems fail or produce errors.

The initiative also reflects a broader shift in how AI is being positioned in global health. Instead of headline-grabbing claims about medical breakthroughs, the emphasis here is on narrow, operational use cases that address staffing gaps and administrative overload. In that sense, AI is being treated less as a cure for weak health systems and more as a temporary support amid declining resources.

OpenAI’s involvement comes as the company expands its presence in healthcare, following earlier work on health-related applications. At the same time, it faces growing scrutiny over how its systems are trained, deployed, and governed, especially in sensitive sectors like medicine.

A test of AI’s limits in healthcare systems

For African health systems, the stakes are practical rather than symbolic. Sub-Saharan Africa faces an estimated shortage of nearly six million healthcare workers, a gap that training alone cannot close in the near term. If AI tools can help clinicians see more patients, reduce errors, or manage workloads more effectively, they may offer some relief. If they add complexity or require constant outside support, they risk becoming another layer of dependency.

Horizon1000 sits at that intersection. As aid budgets tighten and healthcare demands rise, the project offers a test of whether AI can play a useful, limited role in primary care without overstating its reach. The outcome will depend less on the technology itself than on how well it fits into the systems meant to use it.

See also: SAP and Fresenius to build sovereign AI backbone for healthcare

Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is part of TechEx and is co-located with other leading technology events, click here for more information.

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The report’s central theme is that AI will become the operating system for modern rail, not as a single, centralised collection of models and algorithms, but as layers of prediction, optimisation, and automated monitoring found in infrastructure, rolling stock, maintenance yards, and stations (pp.18-23). This technology will guide human focus within daily work schedules rather than replace human activity entirely.

Maintenance to become predictive and data-driven

Traditional rail maintenance relies on fixed schedules and manual inspections, a reactive and labour-intensive practice. The whitepaper cites Network Rail’s reliance on engineers walking the track to spot defects (p.18). AI will shift the industry to predictive maintenance, analysing data from sensors to forecast failures before they cause significant disruption.

This involves a combination of sensors and imaging, including high-definition cameras, LiDAR scanners, and vibration monitors. These provide machine-learning systems with data that can flag degradation in track, signalling, and electrical assets ahead of failure (pp.18-19).

These monitoring programs can generate alerts months in advance, reducing emergency call-outs. The timeframe for predicting asset failure varies by asset type. Network Rail’s intelligent infrastructure efforts should transition from “find and fix” to “predict and prevent.”

Network Rail emphasises data-led maintenance and tools designed to consolidate asset information, while European R&D programs (like Europe’s Rail and its predecessor, Shift2Rail) fund projects like DAYDREAMS, similarly aimed at prescriptive asset management. Prediction at scale requires a common approach to achieve transformation.

Traffic control and energy efficiency

Operational optimisation, beyond predictive maintenance, offers significant returns. AI systems use live and historical operating data—train positions, speeds, weather forecasts—to anticipate disruption and adjust traffic flow. Digital twin and AI-based traffic management trials in Europe, alongside research and testing of AI-assisted driving and positioning, could increase overall network capacity without laying more track (p.20).

Algorithms also advise drivers on optimal acceleration and braking, potentially saving 10-15% in energy. Considering route variations, traction, and timetable constraints, energy savings compound quickly across a large network.

Safety monitoring and CCTV

Visible AI applications focus on safety and security. Obstacle detection uses thermal cameras and machine learning to identify hazards beyond human visibility. AI also monitors level crossings and analyses CCTV footage to spot unattended items and suspicious activity (pp.20-21). For example, AI and LiDAR are used for crowd monitoring at London Waterloo as part of a suite of safety tools.

Passenger flows and journey optimisation

AI can forecast demand using ticket sales, events, and mobile signals, allowing operators to adjust the number of carriages and reduce overcrowding, the report states. Passenger counting is a high-impact, low-drama application: better data supports better timetables and clearer customer information.

Cybersecurity issues

As operational technology converges with IT, cybersecurity becomes a critical operational issue. Legacy systems, lacking replacement plans, pose a risk, as does integrating modern analytics with older infrastructure. This creates conditions attractive to attackers.

The future of AI in rail involves sensors performing in extreme environments, models trusted and tested by operators, and governance that treats cyber resilience as inseparable from physical safety. The report’s message is that AI will arrive regardless. The question is whether railways proactively adopt and control it or inherit it as un-managed complexity.

(Image source: “Train Junction” by jcgoble3 is licensed under CC BY-SA 2.0.)

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In Chifeng, a city in northern China, a renewable-powered factory offers a clear example. The site produces hydrogen and ammonia using electricity generated entirely from nearby wind and solar farms. Unlike traditional plants connected to the wider grid, this facility runs on its own closed system. That setup brings a problem as well as a benefit: renewable power is clean, but it rises and falls with the weather.

To keep production stable, the factory relies on an AI-driven control system built by its owner, Envision. Rather than following fixed schedules, the software continuously adjusts output based on changes in wind and sunlight. As reported by Reuters, Zhang Jian, Envision’s chief engineer for hydrogen energy, compared the system to a conductor, coordinating electricity supply and industrial demand in real time.

When wind speeds increase, production ramps up automatically to take full advantage of the available power. When conditions weaken, electricity use is quickly reduced to avoid strain. Zhang said the system allows the plant to operate at high efficiency despite the volatility of renewable energy.

Projects like this are central to China’s plans for hydrogen and ammonia, fuels seen as important for cutting emissions in sectors such as steelmaking and shipping. They also point to a broader strategy: using AI to manage complexity as the country adds more renewable power to its grid.

Researchers argue that AI could play a significant role in meeting China’s climate goals. Zheng Saina, an associate professor at Southeast University in Nanjing who studies low-carbon transitions, said AI can support tasks ranging from emissions tracking to forecasting electricity supply and demand. At the same time, she cautioned that AI itself is driving rapid growth in power consumption, particularly through energy-hungry data centres.

China now installs more wind and solar capacity than any other country, but absorbing that power efficiently remains a challenge. According to Cory Combs, associate director at Beijing-based research firm Trivium China, AI is increasingly seen as a way to make the grid more flexible and responsive.

That thinking was formalised in September, when Beijing introduced an “AI+ energy” strategy. The plan calls for deeper links between AI systems and the energy sector, including the development of multiple large AI models focused on grid operations, power generation, and industrial use. By 2027, the government aims to roll out dozens of pilot projects and test AI across more than 100 use cases. Within another three years, officials want China to reach what they describe as a world-leading level of AI integration in energy.

Combs said the focus is on highly specialised tools designed for specific jobs, such as managing wind farms, nuclear plants, or grid balancing, rather than general-purpose AI. This approach contrasts with the United States, where much of the investment has gone into building advanced large-language models, according to Hu Guangzhou, a professor at the China Europe International Business School in Shanghai.

One area where AI could have immediate impact is demand forecasting. Fang Lurui, an assistant professor at Xi’an Jiaotong-Liverpool University, said power grids must match supply and demand at every moment to avoid outages. Accurate forecasts of renewable output and electricity use allow operators to plan ahead, storing energy in batteries when needed and reducing reliance on coal-fired backup plants.

Some cities are already experimenting. Shanghai has launched a citywide virtual power plant that links dozens of operators — including data centres, building systems, and electric vehicle chargers — into a single coordinated network. During a trial last August, the system reduced peak demand by more than 160 megawatts, roughly equivalent to the output of a small coal plant.

Combs said such systems matter because modern power generation is increasingly scattered and intermittent. “You need something very robust that is able to be predictive and account for new information very quickly,” he said.

Beyond the grid, China is also looking to apply AI to its national carbon market, which covers more than 3,000 companies in emissions-heavy industries such as power, steel, cement, and aluminium. These sectors together produce over 60% of the country’s carbon emissions. Chen Zhibin, a senior manager at Berlin-based think tank adelphi, said AI could help regulators verify emissions data, refine the allocation of free allowances, and give companies clearer insight into their production costs.

Still, the risks are growing alongside the opportunities. Studies suggest that by 2030, China’s AI data centres could consume more than 1,000 terawatt-hours of electricity each year — roughly the same as Japan’s current annual usage. Lifecycle emissions from the AI sector are projected to rise sharply and peak well after China’s 2030 emissions target.

Xiong Qiyang, a doctoral researcher at Renmin University of China who worked on one such study, said the results reflect the reality that coal still dominates China’s power mix. He warned that rapid AI expansion could complicate national climate goals if energy sources do not shift quickly enough.

In response, regulators have begun tightening rules. A 2024 action plan requires data centres to improve energy efficiency and increase their use of renewable power by 10% each year. Other initiatives encourage new facilities to be built in western regions, where wind and solar resources are more abundant.

Operators on the east coast are also testing new ideas. Near Shanghai, an underwater data centre is set to open, using seawater for cooling to cut energy and water use. The developer, Hailanyun, said the facility will draw most of its power from an offshore wind farm and could be replicated if the project proves viable.

Despite the growing energy demands of AI, Xiong argued that its overall impact on emissions could still be positive if applied carefully. Used to optimise heavy industry, power systems, and carbon markets, he said, AI may remain an essential part of China’s effort to cut emissions — even as it creates new pressures that policymakers must manage.

(Photo by Matthew Henry)

See also: Can China’s chip stacking strategy really challenge Nvidia’s AI dominance?

Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is part of TechEx and is co-located with other leading technology events, click here for more information.

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EU AI Cloud is built to support organisations using SAP’s data centres, other European providers, or on-premise topologies.

Strengthening AI sovereignty in Europe

SAP is also working with Cohere to bring new agent-style and multimodal AI tools to customers through Cohere North. Models will be available through SAP Business Technology Platform (SAP BTP), giving industries with strict data residency needs a way to build AI systems. The two companies say the goal is to help enterprises retain control of performance.

As Cohere’s team put it, their work with SAP is meant to keep advanced AI accessible to organisations that cannot move data outside Europe.

SAP is building EU AI Cloud with several European and global partners. Models and applications from Cohere, Mistral AI, OpenAI, and others are integrated into SAP BTP. Companies can access partner XaaS tools and choose where to run them.

The aim is to give enterprises and public sector groups access to modern AI tools while staying in European standards for security, data protection, and sovereignty.

Deployment choices tied to different security needs

EU AI Cloud works through SAP Sovereign Cloud, which lets customers pick the level of control they want in the stack. AI models run on SAP’s cloud infrastructure and SAP BTP in European data centres, keeping operations separate from US hyperscalers.

Here are the deployment options:

• SAP Sovereign Cloud on SAP Cloud Infrastructure (EU)

SAP’s IaaS is based on open-source tools and runs inside SAP’s European data centre network. Data stays in the EU to support compliance with regional data protection rules.

• SAP Sovereign Cloud On-Site

Infrastructure is managed by SAP but housed in a customer’s chosen data centre. This offers the highest level of control over data, operations, and legal requirements and keeps access to SAP’s cloud architecture.

• Selected hyperscalers by market

Some customers may still run SAP commercial SaaS on global cloud providers. When they do, they can add sovereignty elements based on regional needs.

• Delos Cloud

A sovereign cloud service in Germany designed for the public sector.

EU AI Cloud gives organisations in Europe more choice in how they run AI and cloud workloads with varying levels of control of their data. The mix of deployment options, partner models, and sovereign design aims to support companies that face strict rules around privacy, storage, and operations.

(Photo by Antoine Schibler)

See also: Adversarial learning breakthrough enables real-time AI security

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The update was shared at the Cloud & AI Innovation Summit in Jakarta, where business and government leaders met to discuss how Indonesia can advance its AI ambitions. Speakers included Mike Chan, who leads Azure AI Apps & Agents in Asia, and Dharma Simorangkir, President Director of Microsoft Indonesia. Their message was that local capacity is only useful if organisations put it to work.

During the event, Dharma said the new services “open the door for every organisation to innovate in Indonesia, for Indonesia,” calling on teams in sectors to build solutions that tackle national needs.

A shift toward building, not just adopting

Many Indonesian enterprises are moving beyond basic AI trials and are now designing tools that solve problems unique to their operations.

Microsoft describes these kinds of organisations as Frontier Firms – teams that treat AI as a core part of how they work rather than an optional add-on. The firms tend to focus on building applications that make tasks easier for customers, improve internal processes, or modernise old workflows.

To support this shift, the Indonesia Central region now hosts a range of Azure services that help teams design and deploy software. They include tools for building data-connected applications, services for storing and managing structured data, and a set of AI-ready virtual machines that can train and run advanced models. Machines built for heavy computing work allow teams to keep data inside the country while working with complex AI workloads.

The region now supports Microsoft 365 Copilot, bringing AI features to common work tools. Developers also have access to GitHub Copilot, which suggests code and speeds up software development. These services form a connected stack that helps teams move past small pilots and into production, where reliability and cost control matter more.

Early Microsoft cloud projects emerging in Indonesia

The expansion of the region follows steady demand since its launch in May 2025. Companies in mining, travel, and digital services are already using local cloud infrastructure to refresh legacy systems and meet stricter data governance needs.

Petrosea and Vale Indonesia are among the firms using the region to support technical upgrades and secure local data storage. Digital-first players are also experimenting with more direct AI engagement. One example is tiket.com, which built its own AI travel assistant using the Azure OpenAI Service. The assistant lets customers interact with the platform in everyday language, from checking flight updates to adding extra services after a booking.

“Our advancements in artificial intelligence are designed to deliver the best possible experience for our customers,” said Irvan Bastian Arief, PhD, Vice President of Technology GRAND, Data & AI at tiket.com.

The company sees conversational AI as a way to make travel planning simpler while reducing friction in customer support.

Bringing scattered data into one system

A major theme at the summit was the need to get data in order before adopting AI at scale. To support this, Microsoft introduced Microsoft Fabric to the Indonesian market. Fabric is a single environment that brings together data engineering, integration, warehousing, analytics, and business intelligence. It includes Copilot features that help teams prepare data and build insights without juggling multiple tools.

For many organisations, data sits in different internal systems and cloud providers. Fabric gives teams one place to bring these sources together, which may help improve governance, speed up reporting, and control costs. The platform is designed for teams that want structure without building their own data foundation from scratch.

Preparing Indonesia’s workforce for practical AI with Microsoft tools

The day’s focus was not limited to infrastructure. Microsoft also highlighted its AI training program, Microsoft Elevate, which is now entering its second year. The programme has already reached more than 1.2 million learners and aims to certify 500,000 people in AI skills by 2026. The next phase will focus on hands-on use, encouraging participants to apply AI in real settings rather than only learning concepts in theory.

Training covers a wide range of groups – teachers, nonprofit workers, community leaders, and people looking to improve their digital skills. Participants learn through tools like Microsoft Copilot, Learning Accelerator, Minecraft Education, and modules designed to explain how AI can support practical tasks.

During the summit, Dharma said that cloud and AI “are the backbone of national competitiveness” and stressed that infrastructure only matters if people are prepared to use it.

Building a long-term ecosystem

Such efforts sit in a broader commitment of US$1.7 billion that Microsoft has pledged for Indonesia from 2024 to 2028. The investment spans infrastructure, partner support, and talent development. The company is also preparing to host GitHub Universe Jakarta on 3 December 2025, a developer-focused gathering meant to encourage collaboration among software teams, startups, and researchers.

Indonesia is aiming to position itself as a centre for secure and inclusive AI development in the region. With the expansion of the Indonesia Central cloud region, new data and AI tools, and growing attention on workforce training, the country is taking steps to build the foundations needed for long-term digital growth. Companies now have the option to build AI systems closer to home, developers have more resources, and workers have more pathways to gain practical skills.

The coming years will show how these pieces fit together as organisations move from experimentation to long-term use.

(Photo by Simon Ray)

See also: Microsoft, NVIDIA, and Anthropic forge AI compute alliance

Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is part of TechEx and is co-located with other leading technology events, click here for more information.

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In an exclusive interview, Matar Al Hemeiri, Chief Executive of Digital Dubai Government Establishment, revealed how Dubai’s approach to AI government efficiency differs fundamentally from both its regional competitors and established Asian tech hubs—and why the emirate believes its model of rapid deployment paired with binding ethical frameworks offers a blueprint other governments will eventually follow.

The DubaiAI advantage: 180 services, one virtual assistant

While neighbouring Abu Dhabi announced a $4.8 billion investment to become the world’s first fully AI-powered government by 2027, Dubai has taken a different path. “Abu Dhabi’s investment is focused on building an end-to-end AI-powered government infrastructure,” Al Hemeiri explained. “Dubai’s model is to embed AI ethics, interoperability, and explainability into a scalable governance framework.”

The results are already visible. DubaiAI, the citywide AI-powered virtual assistant, now provides information on more than 180 public services—a figure that represents one of the most comprehensive government AI chatbot deployments globally. The system handles 60% of routine government inquiries while cutting operational costs by 35%.

But Al Hemeiri pushed back against the narrative that AI automation inevitably means job losses. “Automation frees our workforce from repetitive, informational tasks,” he said. “Employees are being reskilled and redeployed into higher-value roles such as AI oversight, service design, and strategic policy work.”

The timing couldn’t be more critical. Dubai’s population growth has created an “immense spike in demand for government services,” according to Al Hemeiri, making AI-driven efficiency not just a competitive advantage but an operational necessity.

Speed as strategy: From pilot to deployment in months

What sets Dubai apart in AI government efficiency isn’t just what it builds—it’s how quickly it deploys. “In Dubai, once an AI initiative is announced, it is swiftly activated, moving from pilot to deployment within months, far faster than the global norm,” Al Hemeiri emphasised.

The numbers back this claim. In 2025, over 96% of government entities had adopted at least one AI solution, and 60% of surveyed users preferred AI-supported services. 

Dubai benchmarks itself against leading smart cities like Singapore, Berlin, Helsinki, and Tallinn, but argues its integration of AI ethics directly into procurement and deployment provides a decisive edge.

“Our competitive edge lies in the speed with which Dubai operationalises its ethics,” Al Hemeiri said, addressing a common criticism that AI governance frameworks are purely theoretical. “The AI Policy is not a theoretical framework; it is a binding set of principles and technical requirements applied to every AI deployment across government.”

This approach builds on the Ethical AI Toolkit launched in 2019, making Dubai one of the few cities globally where ethical compliance is embedded from procurement to performance evaluation.

Beyond chatbots: Healthcare, energy, and predictive services

While DubaiAI captures headlines, Al Hemeiri pointed to less-publicised implementations delivering measurable impact. AI models are now detecting chronic conditions such as diabetes at earlier stages, while predictive algorithms improve auditing systems within the Dubai Health Authority. 

In energy infrastructure, smart grids powered by real-time AI forecasting tools are optimising consumption and reducing environmental impact. The most ambitious project currently in development is Dubai’s predictive public services platform, which will use integrated data and AI to anticipate citizen needs—from automated license renewals to preventive healthcare notifications. 

“We have begun efforts on building this project, with full rollout targeted for the early 2030s,” Al Hemeiri revealed. Elements of this vision are already being tested through AI-enabled urban planning tools and citywide digital twins that simulate policy outcomes before implementation.

Data sovereignty: A hybrid model between China and GDPR

Dubai’s approach to data governance offers a middle path between China’s strict localisation requirements and the EU’s GDPR framework. “Dubai’s model offers a hybrid—anonymised citizen data remains within Dubai’s jurisdiction under robust sovereignty laws, but can be securely shared across entities with the user’s consent for government services, through the nation’s official digital identity platform: UAE PASS,” Al Hemeiri explained.

A key differentiator is Dubai’s embrace of synthetic data frameworks. “They allow us to develop and test AI systems at scale while preserving privacy and maintaining compliance with Dubai’s data sovereignty requirements,” he said. This approach enables faster innovation cycles while addressing privacy concerns that have hampered AI development in other jurisdictions.

The startup sandbox: Real integration, not just regulatory relief

Dubai positions itself as a testing ground for AI startups, but Al Hemeiri argued the emirate offers more than regulatory flexibility. “Dubai’s AI sandboxes combine regulatory flexibility with direct access to government datasets and real-world testing environments,” he said.

One healthcare diagnostics startup piloted within Dubai’s sandbox has already integrated its AI triage tool into Dubai Health Authority services. 

“Because our ecosystem operates as an interconnected digital operating system, startups in our sandboxes can test solutions that seamlessly integrate with other city services, from mobility innovations like the Dubai Loop and eVTOL air taxis to healthcare AI diagnostics,” Al Hemeiri explained.

Converting global attention into economic returns

Dubai AI Week 2025 attracted participants from 100 countries and partnerships with Meta, Google, Microsoft, and OpenAI. But Al Hemeiri insisted the emirate is focused on converting attention into tangible outcomes. 

“We have established post-event working groups with each of these partners to identify and accelerate joint projects,” he said, citing AI upskilling programs, R&D collaborations, and pilot deployments in healthcare, mobility, and urban planning.

These partnerships feed directly into Dubai’s D33 Economic Agenda, which aims to generate AED 100 billion annually from digital innovation. The State of AI Report projects AI could contribute over AED 235 billion to Dubai’s economy by 2030—a figure that represents nearly 20% of the emirate’s targeted economic expansion.

Quiet wins and future risks

When pressed about initiatives that deliver value without media fanfare, Al Hemeiri highlighted the UN Citiverse Challenge, co-led by Digital Dubai and global partners, which brings together innovators to design AI-powered solutions for inclusive public services and sustainability. 

He also pointed to Dubai Future Foundation’s autonomous delivery robot, already being piloted on Dubai streets to improve last-mile delivery efficiency while reducing congestion and emissions.

On risks, Al Hemeiri was direct: “The greatest risk is scaling without sufficient oversight.” Dubai mitigates this through continuous system audits and a requirement for explainability in all public sector AI. 

Al Hemeiri added that ensuring ROI “is crucial for us when deciding to build an AI use case. We calculate this when planning a project, and only move ahead once we are convinced we will be able to attain the expected ROI for the city.”

The five-year test

Asked what would constitute failure five years from now, Al Hemeiri said that it “would mean fragmented AI adoption without improving citizen trust, efficiency, or quality of life.”

Success, conversely, would be “when AI-powered public services are seamless, anticipatory, and inclusive, easing the lives of citizens and residents, and naturally becoming a blueprint replicated by other governments globally.”

It’s an ambitious vision—one that positions Dubai not just as a fast follower in AI government efficiency, but as a potential model for how cities can deploy transformative technology at speed without sacrificing ethical oversight or public trust.

Whether that model proves replicable beyond Dubai’s unique governance structure and resources remains the central question. But with 96% of government entities already adopting AI solutions and deployment timelines measured in months rather than years, Dubai is testing that hypothesis in real-time—and betting that in the race to build AI-powered governments, velocity matters as much as vision.

(Photo by David Rodrigo)

See also: UAE to teach its children AI

Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is part of TechEx and is co-located with other leading technology events, click here for more information.

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The post Exclusive: Dubai’s Digital Government chief says speed trumps spending in AI efficiency race appeared first on AI News.

“Korea’s leadership in technology and manufacturing positions it at the heart of the AI industrial revolution — where accelerated computing infrastructure becomes as vital as power grids and broadband,” said Jensen Huang, founder and CEO of NVIDIA. “Just as Korea’s physical factories have inspired the world with sophisticated ships, cars, chips and electronics, the nation can now produce intelligence as a new export that will drive global transformation.”

“Now that AI has gone beyond mere innovation and become the foundation of future industries, South Korea stands at the threshold of transformation,” said Bae Kyung-hoon, Korea Deputy Prime Minister, and Minister of Science and Information and Communication Technologies.

The government plans to deploy up to 50,000 new NVIDIA GPUs to support sovereign AI programs for businesses and research groups. The first phase includes 13,000 NVIDIA Blackwell and other GPUs through providers such as NAVER Cloud, NHN Cloud and Kakao. The expansion includes efforts to build a National AI Computing Center. Startups, researchers and other organisations will be able to use this sovereign infrastructure to train models and build new applications.

NVIDIA is also working with Samsung, SK Telecom, ETRI, KT, LGU+ and Yonsei University on AI-RAN and 6G network research. The work focuses on shifting some computing tasks from devices to network base stations, which may reduce battery drain and lower computing costs across sovereign AI services.

Major companies build sovereign AI factories

Large corporations in Korea are investing in advanced AI infrastructure for chip production, network operations and digital manufacturing tools that support the country’s sovereign computing goals.

NVIDIA and Samsung plan to build a new AI factory that connects chip manufacturing with accelerated computing. The system will run more than 50,000 NVIDIA GPUs and support data-driven production methods, including predictive maintenance and process improvements across chip fabs.

“We are at the dawn of the AI industrial revolution — a new era that will redefine how the world designs, builds and manufactures,” said Jensen Huang. Jay Y. Lee, executive chairman of Samsung Electronics, added, “From Samsung’s DRAM for NVIDIA’s game-changing graphics card in 1995 to our new AI factory, we are thrilled to continue our longstanding journey with NVIDIA in leading this transformation.”

Samsung plans to use NVIDIA CUDA-X libraries, along with software from Synopsys, Cadence and Siemens, to speed circuit design and manufacturing workflows. It will also use NVIDIA Omniverse to create digital twins of factories and equipment for real-time simulation, testing and logistics planning — all supporting wider sovereign AI adoption.

NVIDIA’s cuLitho library is being integrated into Samsung’s computational lithography tools. The collaboration has led to major gains in performance, supporting faster scaling in chip production.

Samsung is also developing large language models that run across hundreds of millions of Samsung devices, supporting translation and other reasoning tasks. The company plans to expand into robotics using NVIDIA Isaac Sim, NVIDIA Cosmos and the Jetson Thor edge platform, which may strengthen its position in sovereign AI systems.

SK Group expands AI capacity

SK Group is building an AI factory that will include more than 50,000 NVIDIA GPUs, with completion expected by late 2027. The facility will support SK subsidiaries and outside clients through GPU-as-a-service offerings that align with South Korea’s sovereign AI strategy. NVIDIA and SK are also working together on next-generation high-bandwidth memory for GPUs.

“SK Group is working with NVIDIA to make AI the engine of a profound transformation that will enable industries across Korea to transcend traditional limits of scale, speed and precision,” said Chey Tae-Won, chairman of SK Group.

SK Telecom plans to build an industrial AI cloud using NVIDIA RTX PRO 6000 Blackwell Server Edition GPUs. The platform will support semiconductor manufacturing, digital twins and internal AI agents.

SK hynix is using NVIDIA PhysicsNeMo tools to support chip design simulations, aiming to improve accuracy and speed. It is also testing NVIDIA Blackwell GPUs with Synopsys software and building autonomous fab digital twins.

To support workers, SKT is developing a foundation model called A.X., built with NVIDIA NIM microservices and NVIDIA AI Enterprise. The model will power internal agents to assist thousands of employees across chip development and operations.

Hyundai Motor Group plans new AI factory

NVIDIA and Hyundai Motor Group are expanding their partnership to support autonomous driving, factory automation and robotics. Hyundai plans to build an AI factory using NVIDIA Blackwell GPUs for integrated training, simulation and deployment.

“AI is revolutionising every facet of every industry, and in transportation alone — from vehicle design and manufacturing to robotics and autonomous driving — NVIDIA’s AI and computing platforms are transforming how the world moves,” said Jensen Huang.

The companies expect joint investment of about $3 billion to grow national physical AI capabilities. The plan includes an NVIDIA AI Technology Center, Hyundai’s Physical AI Application Center and new data centres. These programs aim to help train a new generation of AI talent.

Hyundai will use NVIDIA Omniverse Enterprise to build digital twins of factories, supporting virtual testing, robot integration and predictive maintenance. It will also use NVIDIA DRIVE AGX Thor for in-vehicle AI systems, including driver assistance and infotainment features.

Growth of sovereign AI models

NAVER Cloud plans to deploy more than 60,000 GPUs for sovereign and physical AI work. The company will build industry-targeted models for areas such as shipbuilding and public safety.

The Ministry of Science and ICT is also leading a Sovereign AI Foundation Models project using NVIDIA NeMo and open Nemotron datasets. Partners include LG AI Research, NC AI, SK Telecom and Upstage. These models will support language and reasoning tasks.

LG is working with NVIDIA on physical AI research and will support startups and researchers using its EXAONE models, including healthcare applications.

Quantum and scientific research

KISTI plans to use NVIDIA accelerated computing in its sixth national supercomputer, HANGANG. The institute will support NVQLink, an open architecture for connecting quantum processors with GPU clusters. It will also develop scientific foundation models and explore physics-informed AI tools using NVIDIA PhysicsNeMo.

NVIDIA and local partners are forming a startup alliance through the NVIDIA Inception program. Members will gain access to accelerated computing resources from cloud partners like SK Telecom, along with support from venture firms. NVIDIA also plans to take part in the N-Up AI startup incubation program from the Ministry of SMEs and Startups.

(Photo by Nvidia)

See also: Migrating AI from Nvidia to Huawei: Opportunities and trade-offs

Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is part of TechEx and is co-located with other leading technology events, click here for more information.

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