How countries are harnessing AI: Case studies across key sectors

A Kazinform News Agency correspondent examined global examples of AI adoption, exploring key successes, common challenges, and solutions used in other countries.

Medicine and healthcare

Israel is actively using artificial intelligence in healthcare for diagnostics, personalized treatment, emergency response, and drug development. For example, the startup Aidoc has implemented AI algorithms in the country’s largest hospitals to instantly detect strokes, hemorrhages, and fractures in CT scans.

Personalized medicine is also advancing rapidly, with a joint project by Technion and Maccabi reducing incorrect antibiotic prescriptions for urinary tract infections by 35% and helping combat drug resistance.

AI is used in emergency services such as United Hatzalah, where the system predicts potential emergency call locations with up to 85% accuracy, reducing response times.

However, the use of medical data requires strict protection, as data breaches can compromise patient privacy. The high cost of AI solutions and the need for integration with existing systems make adoption difficult for smaller clinics. There is also a risk that doctors may over-rely on AI, lowering their own diagnostic vigilance.

To address these risks, several countries have introduced certification standards for medical AI systems, mandatory algorithm verification procedures, and protocols for joint decision-making between doctors and AI. Open medical datasets are being developed to improve model accuracy and objectivity. Training healthcare professionals to work with AI helps them interpret results correctly and treat AI as a support tool rather than a replacement for clinical expertise.

Education

Finland is actively integrating artificial intelligence into education, combining a national strategy with innovations at the school and university level. For example, the ViLLE platform, developed by the Turku Institute for Learning Analytics, uses adaptive feedback methods. It analyzes students’ answers, strengths, weaknesses, and other indicators to pinpoint where they need additional support and where they are ready for new tasks.

However, algorithms can carry biases, for instance, misjudging work due to writing style or cultural context. Excessive reliance on AI in learning can weaken students’ critical thinking and independent research skills. In schools with limited resources, AI adoption may also deepen educational inequality between regions and social groups.

To reduce these risks, many countries are creating ethical standards for AI use in education, including mandatory checks for transparency and fairness. Hybrid models are emerging where AI supports teachers rather than replacing them. Teachers are trained to work with AI tools so they can effectively oversee the learning process and correct system errors.

Transport and logistics

The Netherlands is making extensive use of AI to improve the efficiency and sustainability of its transport infrastructure. At the Port of Rotterdam, the AI system predicts vessel arrival times with high accuracy, drawing on data like past arrival trends, ship types, routes, and speed. This has cut average waiting times by 20%, boosting planning efficiency for terminals, shipping agents, and vessel operators.

In the USA, AI supports logistics at the industry service level. For example, Uber Freight uses machine learning algorithms to reduce empty truck trips. About 35% of trucks typically travel without cargo, but this figure has been lowered by 10-15%.

Despite significant progress, predictive systems rely heavily on high-quality, real-time data, but many ports and logistics hubs still operate with fragmented or outdated information flows. Over-reliance on automated decision-making also carries operational risks if algorithms misinterpret unusual conditions, such as extreme weather or geopolitical disruptions.

Cybersecurity

Artificial intelligence is increasingly being deployed to detect, prevent, and respond to cyber threats in real time. In the USA, Microsoft’s Security Copilot uses generative AI to assist security analysts in investigating incidents, correlating threat intelligence, and recommending containment measures.

In the financial sector, HSBC applies AI models to monitor millions of daily transactions, detecting suspicious activity and blocking fraudulent payments within seconds.

However, AI-based defenses can themselves be targeted, for example, attackers may use adversarial techniques to feed misleading data, causing the system to miss threats or trigger false alarms. Models trained on biased or incomplete datasets might fail to detect novel attack patterns, while over-reliance on automated decisions can delay human intervention during complex incidents. Moreover, malicious actors are also adopting AI to automate phishing campaigns, develop polymorphic malware, and scan networks for vulnerabilities at scale.

Energy and environment

Forecasting wind energy output is critical in Denmark, where half of electricity comes from renewables and some days rely on wind for up to 50% of consumption. AI-powered models significantly improve forecast accuracy, allowing the grid to absorb volatile production more efficiently.

In Australia, the startup Neara analyzes power grid infrastructure with extreme weather conditions in mind, helping utilities choose optimal repair strategies and improve supply reliability.

However, while AI supports environmental and energy goals, its own development can harm the environment. Training large-scale generative models with billions of parameters consumes vast amounts of electricity, increasing CO₂ emissions and straining power systems. Cooling these servers also requires millions of liters of water.

To mitigate AI’s environmental impact, countries are developing greener data center solutions. In cooling, liquid and immersion systems are increasingly used, saving up to 50% of energy and greatly reducing water consumption. Data centers are also transitioning to renewable power sources. For example in Brazil, they connect to a grid that is nearly 90% powered by hydroelectric plants.

Agriculture

In the Netherlands, greenhouse farms use computer vision systems to monitor plant health and automatically adjust irrigation, lighting, and temperature. In the USA, farmers use platforms like John Deere’s See & Spray, which rely on AI to identify weeds and spray herbicides only where needed, reducing chemical use severalfold. In Australia, drones with AI analytics monitor livestock health and pasture conditions.

These technologies also come with challenges. The high cost of equipment and the complexity of integration make it difficult for smaller farms to adopt AI solutions. Algorithms can make mistakes, such as misdiagnosing plant diseases or soil conditions, leading to losses. Reliance on cloud services and sensors increases vulnerability to cyberattacks, which could disrupt entire farming operations.

To address these issues, governments and companies are introducing training programs for farmers, subsidies for technology purchases, and create AI platforms adapted to local conditions. Hybrid systems that can operate without a constant internet connection are also being developed, reducing the risk of outages and attacks.

Earlier, Kazinform News Agency reported that Google and NASA trial AI system for diagnosing medical issues in space.