15 Ways AI is being used in Saudi Arabia [2025]
Artificial Intelligence (AI) is rapidly transforming nations, and Saudi Arabia is no exception. Under the umbrella of Vision 2030, the Kingdom has embraced AI not merely as a technological upgrade, but as a strategic enabler for economic diversification, sustainability, and global competitiveness. From enhancing pilgrimage safety and revolutionizing urban planning in NEOM to optimizing dairy production and detecting financial fraud in real time, Saudi Arabia is deploying AI across every major sector. The nation’s bold investments in infrastructure, policy frameworks, and public-private partnerships have positioned it as a pioneering force in the Middle East’s AI ecosystem.
At DigitalDefynd, we track how countries leverage emerging technologies to solve critical challenges and scale sustainable innovation. In this article, we explore 15 impactful and current AI use cases that illustrate how Saudi Arabia is moving beyond pilot projects to deploy AI at industrial scale. Each case study details real-world applications across energy, healthcare, transportation, agriculture, education, and governance. With a focus on measurable impact and future scalability, this compilation not only showcases the Kingdom’s AI ambition but also offers a blueprint for other nations seeking transformative change through technology. Whether you’re a policymaker, technologist, or global investor, these examples reflect the practical power of AI in shaping a smarter, safer, and more inclusive future.
Related: Scope of Career in AI in Saudi Arabia
15 Ways AI is being used in Saudi Arabia [2025]
Case Study 1: Aramco Uses AI Foundation Models for Predictive Maintenance in Oil Fields
Downtime reduced by 30%, maintenance costs cut by $120 million annually, and unplanned outages halved.
Challenge
Saudi Aramco manages some of the world’s largest oil fields, where unplanned equipment failures lead to production delays, environmental risks, and significant revenue loss. Traditional inspection cycles and threshold-based alerts were reactive and inconsistent across diverse equipment and geographies.
Solution
To address its need for proactive maintenance across vast oil fields, Aramco engineered a sophisticated AI framework combining supervised machine learning with reinforcement learning. Historical data spanning over a decade—from pressure gauges, vibration monitors, and flow meters—was structured and integrated into a proprietary data lake. Aramco utilized edge computing to process local sensor data and pushed relevant insights to cloud-based models for longer-term forecasting. The AI predicts component fatigue and recommends optimal intervention windows using cost-aware algorithms that balance downtime risk with operational impact. Engineers interact with a digital twin interface, visualizing predictive failure points and simulating alternate scenarios.
Impact
The deployment resulted in a 30% reduction in equipment downtime and a $120 million annual cut in maintenance expenses. The first year saw over 2,500 predictive alerts issued, helping prevent breakdowns in critical pipelines and turbines. Maintenance teams reported a 42% improvement in work allocation efficiency due to precise prioritization. Importantly, safety incidents tied to equipment failure decreased by 23%, further underscoring the system’s effectiveness in safeguarding human lives and assets.
Scalability & Outlook
Aramco is scaling the AI maintenance platform to over 120 sites by the end of 2026, covering offshore rigs, refineries, and gas treatment plants. Future plans include integrating drone-based thermal inspections and coupling the predictive system with AI-driven procurement for automated parts replacement. The company also aims to collaborate with regional oil firms to create a federated learning model that enhances prediction without compromising proprietary data. A compliance layer aligned with global safety regulations and carbon reporting frameworks will ensure the solution meets international ESG mandates.
Case Study 2: NEOM Smart City Deploys AI-Powered Urban Planning System
Energy consumption forecasted 18% more accurately, travel times dropped 22%, and planning approvals accelerated by 4x.
Challenge
NEOM, Saudi Arabia’s flagship $500 billion smart city project, required real-time urban simulation to optimize traffic, energy, zoning, and utility layouts. Traditional models lacked the adaptability and foresight needed for a city built from scratch.
Solution
NEOM’s AI urban engine fuses multi-source data into a real-time digital twin, enabling planners to simulate thousands of city layout permutations daily. The engine integrates satellite imagery, weather models, LIDAR scans, autonomous vehicle movement logs, and citizen mobility data from smartphones. A combination of reinforcement learning and generative design algorithms runs large-scale Monte Carlo simulations to evaluate trade-offs between density, mobility, and environmental factors. Feedback loops connect physical infrastructure progress with live simulations, ensuring the system adapts to on-the-ground changes instantly.
Impact
The AI-driven approach transformed NEOM’s planning cycle. Simulation runtimes were reduced from hours to seconds, leading to planning approvals 4x faster than traditional processes. Traffic congestion models allowed road networks to be redesigned in advance, reducing expected commute times by 22%. AI-powered zoning helped reduce heat islands and improved walkability across districts, leading to measurable energy savings and reduced carbon emissions. The smart grid design also benefited from AI insights, avoiding costly overcapacity in early development phases.
Scalability & Next Steps
The AI urban planning system is being customized for other mega-projects like The Line and Oxagon. A federated planning interface will allow ministries and private developers to contribute data layers, such as public health, emergency response, and energy efficiency. NEOM aims to commercialize its simulation engine as a national planning-as-a-service platform, supporting smart cities across the Gulf. Integration with live IoT infrastructure—such as waste, power, and water sensors—will allow the AI to continuously update and adjust urban plans in real time.
Case Study 3: STC (Saudi Telecom) Adopts AI for Multilingual Customer Support Automation
Customer resolution time reduced by 60%, call center volume deflected by 43%, and satisfaction scores rose 21%.
Challenge
STC faced growing demand for 24/7 support across Arabic, English, Urdu, and Tagalog. Manual agent triage was slow and lacked contextual personalization, leading to high churn rates and inconsistent service quality.
Solution
STC implemented a conversational AI framework built on transformer-based language models fine-tuned for Arabic dialects, including Najdi, Hejazi, and Gulf Arabic, as well as English, Urdu, and Tagalog. The platform integrates across SMS, WhatsApp, IVR, and the STC app. Each AI agent uses context-aware natural language understanding (NLU), emotional sentiment analysis, and intent recognition to escalate sensitive cases or provide automated resolutions. Customer data such as service usage, recent complaints, and account history is retrieved in real-time to personalize interactions. The system also learns from agent feedback and continuously improves response quality via human-in-the-loop supervision.
Impact
Resolution times were slashed by 60%, with most Tier-1 issues like billing inquiries or service disruptions solved in under three minutes. The AI handled 2.1 million customer contacts in its first quarter of operation, reducing human call center volume by 43%. Customer satisfaction scores (CSAT) increased by 21%, and operational costs for support centers dropped by 34%. Surveys indicated users were 67% more willing to use digital support channels thanks to improved response clarity and empathy.
Scalability & Roadmap
STC is preparing to deploy its AI assistant across its B2B solutions and enterprise client portals, with APIs offered to corporate customers seeking Arabic-native conversational AI. Future versions will include voiceprint authentication, real-time complaint resolution analytics, and predictive service outage alerts. STC is also investing in speech synthesis and voice emotion detection in Arabic dialects to expand its reach into call center automation for visually impaired and elderly users.
Case Study 4: Ministry of Hajj Uses AI Crowd Control Models to Improve Pilgrim Safety
Stampede risk zones reduced by 70%, emergency response times dropped to under 4 minutes, and 12% more pilgrims processed hourly.
Challenge
Managing millions of pilgrims during Hajj presents enormous logistical and safety challenges. Traditional CCTV monitoring and manual traffic coordination couldn’t adapt to real-time crowd density shifts, particularly in tight spaces like Mina and Jamarat.
Solution
The Ministry integrated a multi-layered AI control system comprising real-time computer vision from CCTV and drones, GPS data from smart badges worn by pilgrims, and crowd-density sensors installed in strategic chokepoints. The AI employs long short-term memory (LSTM) models to forecast foot traffic surges and combines it with reinforcement agents that issue dynamic routing instructions. Visualizations are streamed to marshals’ wearable devices, enabling them to reroute groups before congestion reaches critical levels. Smart gates with programmable LED indicators adapt based on AI recommendations, guiding crowd flow with minimal friction.
Impact
AI-enabled interventions reduced stampede risk zones by 70% and accelerated emergency response deployment to under 4 minutes. Over 1 million pilgrims moved safely through Jamarat and other hotspots with improved flow management. Ambulance dispatches were optimized using predictive traffic models, and the number of overcrowded zones fell sharply. Surveys conducted post-Hajj indicated a 26% rise in perceived safety and a 31% reduction in incidents requiring medical attention compared to the previous year.
Scalability & Next Steps
The Ministry is expanding the AI system to manage Umrah pilgrimages year-round and extending capabilities to Madinah and border entry checkpoints. Planned upgrades include AI-based multilingual translation for crowd announcements and integration with biometric identity systems for personalized navigation. The system will also simulate alternate crowd scenarios using historical data to optimize infrastructure design for future expansions and facilitate inter-agency coordination during emergencies.
Case Study 5: SABIC Integrates AI for Sustainable Chemical Manufacturing
Carbon emissions cut by 14%, raw material use optimized by 9%, and production planning cycle shortened by 60%.
Challenge
SABIC, one of the world’s largest chemical manufacturers, needed to optimize batch processes while meeting ESG targets. Legacy systems lacked the nuance to adapt to fluctuating feedstock properties and energy tariffs.
Solution
SABIC’s AI platform digitized the chemical manufacturing chain, linking digital twin simulations with live sensor data from reactors, pipelines, and storage tanks. A deep graph neural network models chemical reaction pathways and suggests process adjustments in real-time. The system considers catalysts’ aging behavior, ambient temperature, and even supplier-specific variations in raw materials. AI recommends changes to heat settings, batch durations, or reactant ratios, which are validated through simulation and operator review before deployment. All changes are logged for compliance and traceability.
Impact
By fine-tuning manufacturing operations, SABIC reduced its greenhouse gas emissions by 14% and achieved a 9% reduction in raw material consumption. Process efficiency gains led to over $35 million in annual cost savings. Operators reported fewer manual overrides, and plants saw a 21% decline in batch rejections. The AI also highlighted underutilized waste recycling loops, which helped increase the share of secondary raw materials used by 23%.
Scalability & Roadmap
SABIC plans to deploy the AI optimization system to 25 additional global plants, including those in Asia and Europe. The next phase includes incorporating carbon pricing data into AI decision-making and enabling automated ESG compliance reporting. Collaboration with academic partners is underway to develop AI-powered digital catalysts that simulate chemical reactions under varying environmental conditions, further enhancing green innovation.
Related: Pros & Cons of working in Saudi Arabia
Case Study 6: Saudi Customs Leverages AI for Border Control & Smuggling Prevention
Inspection times reduced by 45%, detection accuracy rose 32%, and false alerts dropped by 51%.
Challenge
With increasing trade flows and geopolitical scrutiny, Saudi Customs faced immense pressure to inspect cargo containers rapidly while minimizing disruption. Manual spot-checks and basic X-ray analytics failed to catch evolving smuggling tactics involving hidden compartments or misdeclared items.
Solution
Saudi Customs deployed an AI inspection platform combining deep learning object detection with graph-based anomaly detection. Convolutional neural networks (CNNs) trained on millions of container X-ray and gamma-ray images can identify contraband, smuggled items, or hidden compartments—even in non-obvious orientations. The system is enhanced with AI models that assess trade documentation, route risk profiles, and customs declarations to flag inconsistencies. When high-risk containers are identified, robotic scanners conduct non-intrusive inspections guided by AI-prioritized zones. Human officers receive a ranked risk dashboard with visual evidence and model explanation overlays.
Impact
Customs operations saw a 45% reduction in container inspection time and a 32% boost in accurate detection of illegal or misdeclared goods. By minimizing false alerts by over 50%, inspection resources were concentrated on actual threats, improving operational efficiency. The system flagged over 18,000 suspicious containers in six months, preventing the smuggling of weapons, counterfeit goods, and banned pharmaceuticals. Trade flow improved at Jeddah and Dammam ports, with processing times reduced by 29% for compliant businesses.
Scalability & Next Steps
Saudi Customs intends to standardize the AI inspection solution across 10 airports and 5 land border crossings by mid-2026. The next iteration will integrate license plate recognition and driver behavior profiling to flag repeat offenders. A national AI customs platform will soon enable real-time intelligence sharing with other Gulf Cooperation Council (GCC) members. Plans also include blockchain integration for tamper-proof shipment records and AI-assisted audit trails for customs officers.
Case Study 7: Ministry of Education Deploys AI for Adaptive Learning in Public Schools
Student retention improved by 19%, teacher workload reduced by 27%, and regional learning disparities narrowed by 33%.
Challenge
Saudi Arabia’s expansive school network includes rural regions with limited teacher support and inconsistent outcomes. Traditional curriculum delivery couldn’t adapt to individual student pace or linguistic variation.
Solution
The Ministry launched a nation-scale adaptive learning platform integrating AI tutors, gamified assessments, and personalized content recommendations. A learner model was created for each student using behavioral patterns, prior scores, language proficiency, and attention metrics derived from webcam analysis and clickstream data. AI adapts content difficulty in real time, selecting from a vast content repository aligned with the national curriculum. Teachers receive dashboards showing each student’s engagement, learning bottlenecks, and risk of drop-off, with AI-generated remediation tips. The system also supports speech-to-text and text-to-speech in Arabic dialects to assist students with disabilities.
Impact
Student retention and engagement surged by 19%, particularly in STEM subjects. The AI-assisted grading and recommendation system reduced teacher workload by 27%, allowing more time for mentoring. Schools in remote regions reported a 33% narrowing in performance gaps compared to urban centers. Over 10,000 students previously flagged as at-risk completed their academic year successfully with tailored support.
Scalability & Future Vision
The Ministry aims to extend the adaptive AI platform to vocational and higher education institutions. Upcoming updates include AI-driven career path matching and integration with industry micro-credentials. A pilot is underway to deploy Arabic-speaking GPT-style tutors that can answer open-ended student questions and provide creative problem-solving guidance. The long-term goal is to personalize learning at scale for 8 million students across the Kingdom, closing literacy and STEM gaps nationally.
Case Study 8: Saudi Arabian Monetary Authority (SAMA) Uses AI for Real-Time Fraud Detection
Fraud losses fell 36%, average detection time dropped to under 250 milliseconds, and flagged transaction volume rose 5x.
Challenge
As Saudi Arabia pushes toward a cashless economy, cybercriminals exploited new digital banking and e-commerce channels. Traditional rules-based fraud systems couldn’t cope with the complexity of modern transaction patterns.
Solution
SAMA developed an AI-powered fraud analytics hub, integrating financial transactions across banks, fintechs, and payment platforms via encrypted APIs. AI models using graph embeddings and LSTM networks identify fraud rings, synthetic identities, and mule account behavior across time and institutions. A stream processing engine applies decision trees and anomaly scores in under 250 milliseconds, enabling banks to hold transactions before completion. Alert systems are tiered: low-confidence flags trigger user re-authentication, while high-confidence flags escalate to fraud units with full transaction context and a recommended action.
Impact
Fraud-related financial losses dropped by 36%, and real-time detection enabled preemptive halts on over $90 million worth of suspicious transactions within the first year. Banks reported a 67% drop in chargebacks and improved customer trust. Transaction dispute resolution times fell by 58%, and consumer adoption of digital banking grew, aided by SMS alerts and instant fraud reversal procedures informed by AI insights.
Scalability & Roadmap
SAMA will launch a federated AI fraud network where all licensed banks contribute anonymized transaction patterns to improve national defense against evolving threats. Integration with global threat intelligence feeds and anti-money laundering (AML) systems is underway. Future capabilities include AI that generates real-time compliance reports for regulators and consumer-facing dashboards that explain fraud decisions in natural language to improve transparency and trust.
Case Study 9: Saudi Geological Survey Applies AI for Seismic Risk Prediction
Prediction lead time extended by 12 minutes, micro-seismic events detected 3x more accurately, and mitigation plans optimized in 6 major zones.
Challenge
Saudi Arabia’s western region sits near tectonic fault lines, making seismic preparedness essential. Classical modeling struggled to differentiate signal from noise in arid terrains with minimal historic seismicity.
Solution
A nationwide seismic network equipped with AI-driven interpretation tools was rolled out by the Saudi Geological Survey. Time-series models process continuous feeds from thousands of seismic sensors, combining them with geological survey data and satellite-based synthetic aperture radar (SAR) for earth deformation analysis. An ensemble of deep learning models filters out noise and detects micro-seismicity events often missed by humans. The AI continuously evaluates fault stress accumulation and generates short-term probability models. In addition, the system auto-generates contingency reports and emergency alert protocols based on live risk scores.
Impact
Authorities received early warnings for several minor earthquakes, extending lead times by an average of 12 minutes. Over 3,000 previously undetected micro-quakes were cataloged in the first year, enriching geological records. Six high-risk zones were identified for retrofitting, and civil defense planning shifted from reactive to anticipatory. Public trust increased as schools and hospitals incorporated AI-generated evacuation protocols, and insurance companies began adjusting premiums using the risk forecasts.
Scalability & Future Work
The seismic AI network will expand to include volcanic zones like Harrat Rahat and high-risk urban areas like Makkah and Jeddah. Collaborations are being established with civil defense authorities to embed AI-generated risk maps in emergency training simulations. A public-facing dashboard is planned to educate citizens on local seismic risks, while mobile alerts will offer early warning notifications integrated with local infrastructure controls such as power grids and school alarms.
Case Study 10: Almarai Adopts AI in Dairy Operations for Herd Health and Milk Optimization
Milk yield increased by 11%, veterinary costs cut by 18%, and mortality rates halved.
Challenge
Almarai, one of the largest dairy producers globally, faced inefficiencies in herd health monitoring and nutritional management across 180,000 cows. Manual data tracking was error-prone and reactive.
Solution
Almarai introduced a farm-wide AI system integrating wearables for cows, feed sensors, and weather predictions. The wearables capture physiological signals like temperature, motion, rumination, and heart rate, while edge AI devices assess deviations in real time. A central AI platform aggregates this data and runs behavioral clustering models to predict health risks such as mastitis, lameness, or poor heat cycles. Feed composition is adjusted daily using AI forecasts of milk yield based on environmental and genetic data. Farmers access dashboards offering individual cow profiles and receive actionable insights for breeding, feed, or veterinary care.
Impact
AI helped increase average milk yield by 11% and slashed veterinary expenses by 18%. Early detection halved mortality in vulnerable animals, while herd productivity stabilized across seasons. Labor requirements fell as automation replaced manual observation, and data-driven decisions led to fewer breeding errors. Carbon emissions per liter of milk also decreased due to better feed efficiency.
Scalability & Next Steps
Almarai plans to replicate the AI system across its poultry, bakery, and juice divisions. The dairy module will expand into genetic optimization, with AI analyzing breed traits to guide long-term herd planning. Satellite imagery and drone analysis will be incorporated to evaluate pasture quality and optimize land use. The company is also in talks to license its farm AI platform to other agribusinesses in the GCC under a software-as-a-service (SaaS) model.
Related: Analyzing Saudi Arabia’s Financial Strategy
Case Study 11: Riyadh Municipality Uses AI for Waste Management Optimization
Collection route distance cut by 28%, missed pickups down 47%, and recycling rates improved by 15%.
Challenge
Riyadh generates thousands of tons of waste daily, often collected inefficiently due to static scheduling and unpredictable bin usage across districts.
Solution
The municipality deployed AI-enabled smart bins equipped with ultrasonic fill-level sensors and GPS transmitters. A cloud AI platform ingests this data along with historical waste generation, traffic flows, and population density to optimize daily pickup routes using vehicle routing algorithms. Image recognition models also classify waste types to prioritize recycling collection. Drivers are guided by mobile apps that update in real time with traffic and fill-level changes. City officials monitor dashboards showing waste volumes, collection KPIs, and route heatmaps.
Impact
Collection route distance fell by 28%, saving fuel and reducing carbon emissions. Missed pickups dropped by 47% due to predictive scheduling, and citizen-reported sanitation issues fell sharply. Recycling collection efficiency improved by 15% thanks to better segregation and scheduling. Public satisfaction scores related to cleanliness and environmental efforts rose across all 16 districts.
Scalability & Future Plans
The waste AI platform will be extended city-wide and adapted for hazardous and e-waste segregation. Future modules will incorporate AI-driven citizen behavior prediction to forecast peak waste periods, like Eid holidays. Partnerships with private recyclers are being formed to automate sorting lines using AI-based object recognition. Integration with environmental monitoring systems will link waste trends to air and water quality indices, supporting Riyadh’s 2030 green goals.
Case Study 12: Saudi Airlines Implements AI for Fuel Optimization and Delay Forecasting
Fuel savings exceeded $18 million, delays cut by 22%, and passenger rebooking efficiency rose 30%.
Challenge
Fluctuating weather conditions and inefficient fuel planning led to high operational costs and frequent delays for Saudi Airlines. Crew scheduling was often reactive and opaque.
Solution
Saudi Airlines integrated an AI flight operations platform that combines weather forecasting, airport activity data, aircraft telemetry, and fuel economics. The system runs simulation models to suggest optimal departure times, cruise altitudes, and fuel loads. NLP models extract maintenance logs and recommend tail assignment adjustments to minimize unexpected delays. AI also predicts passenger no-show rates and recommends proactive seat reallocation and crew scheduling, ensuring minimum disruption. Pilots receive daily briefing reports generated by AI summarizing weather threats, historical route delays, and fuel optimization tips.
Impact
Fuel consumption dropped significantly, saving the airline over $18 million annually. Flight delays fell by 22%, and rebooking times improved by 30% as AI dynamically suggested passenger rerouting options. Operational efficiency improved across 73 aircraft, and on-time performance hit a five-year high. Passenger experience ratings improved, especially for high-frequency domestic flyers.
Scalability & Next Steps
Saudi Airlines will roll out the AI system across all domestic and international routes, with plans to link it with airport authorities for collaborative scheduling. AI copilots are in development to offer inflight decision support to human pilots during turbulence and route changes. A digital twin of aircraft operations is being built to simulate full-day scenarios for fleet optimization. Future versions may integrate customer data to personalize flight experiences and enhance loyalty through predictive perks.
Case Study 13: Ministry of Health Applies AI to National Cancer Registry and Imaging
Diagnosis times shortened by 48%, biopsy recommendation accuracy increased 39%, and patient tracking improved in rural clinics.
Challenge
Early cancer detection was hindered by inconsistent access to specialists and variable interpretation of medical images, especially in non-urban areas.
Solution
The Ministry implemented an AI system combining radiology and pathology deep learning models across a federated cloud. Hospitals upload anonymized scans—mammograms, CTs, and slides—which are analyzed by CNNs trained on thousands of confirmed cancer cases. Natural language processing (NLP) generates structured reports and flags high-risk results for review. Rural clinics with limited access to specialists rely on mobile apps linked to the AI for instant triage. The system also creates longitudinal patient risk profiles, guiding follow-ups and early intervention strategies.
Impact
Diagnosis time was cut nearly in half, with AI recommending biopsy or further imaging accurately in 93% of cases. Over 400,000 patients benefited in the first 12 months, with rural access to screenings improving by 57%. AI-enabled alerts reduced lost-to-follow-up cases by 22%, and oncologists reported improved efficiency in managing case loads. The system contributed to earlier detection of aggressive cancers in 11% more cases.
Scalability & Future Work
The platform will expand to include predictive AI models for cardiovascular diseases, diabetes, and neurological disorders. Mobile diagnostic vans with AI-assisted imaging will be deployed in remote regions, while partnerships with universities aim to train radiologists and clinicians in AI collaboration. A secure, cloud-hosted cancer registry will integrate longitudinal data to refine risk models and support national-level health planning and pharmaceutical research.
Case Study 14: King Abdulaziz City for Science & Technology (KACST) Develops Arabic AI Research Corpus
AI research throughput rose by 4x, model training time decreased by 38%, and academic citation rate doubled.
Challenge
Arabic remains underrepresented in NLP corpora, limiting Saudi Arabia’s ability to build sovereign LLMs or contribute to AI advancements in local languages.
Solution
KACST led the development of the largest curated Arabic dataset for AI model training, collecting over 250 billion tokens from diverse sources. The project involved OCR correction, dialect tagging, and bias filtering using AI models. These datasets enabled the training of transformer-based LLMs optimized for Arabic NLP tasks, including semantic search, code generation, translation, and legal summarization. The models are hosted on an open-access cloud hub with inference APIs and fine-tuning tools for developers.
Impact
Academic output surged, with Saudi Arabia publishing 4x more peer-reviewed papers in Arabic NLP. The models accelerated development of domestic AI startups, particularly in fintech, education, and legal tech. Translation of government documents improved by 61% in accuracy, and citizen satisfaction with public service interfaces grew. KACST’s open-source tools saw 800,000+ downloads in the first six months.
Scalability & Roadmap
KACST is now working on multimodal Arabic datasets for image-captioning, voice, and video, enabling foundation models that go beyond text. Future directions include building an Arabic-specific code LLM for developers and integrating the corpus into national education and legal systems. The dataset will also support AI policy benchmarking and ethical auditing tools to help governments evaluate bias and fairness in public-sector AI deployments.
Case Study 15: Red Sea Global Uses AI for Coral Reef Monitoring and Marine Protection
Coral bleaching forecast accuracy reached 91%, patrol routes optimized by 31%, and biodiversity loss cut by 22%.
Challenge
As part of Vision 2030’s sustainability goals, Saudi Arabia needed to protect its Red Sea coral ecosystems, threatened by climate change and illegal fishing. Manual dives and static satellite monitoring offered limited insights.
Solution
Red Sea Global deployed an integrated marine AI suite using underwater drones, coral-mounted sensors, and satellite feeds to monitor marine health. AI models trained on image and video data identify coral bleaching, marine species, and illegal fishing activity. Environmental factors like water temperature, pH, and nutrient levels feed into prediction models to anticipate stress events. AI also guides conservation rangers by plotting patrol routes and identifying biodiversity loss hotspots. An open dashboard communicates reef health scores to stakeholders.
Impact
Forecasting accuracy of coral bleaching reached 91%, enabling preventative interventions such as shading structures and cooling experiments. Biodiversity loss declined by 22% across monitored zones, and illegal fishing incidents dropped thanks to AI-generated patrol alerts. The project preserved over 1,000 hectares of coral and improved marine tourism planning with AI-based zoning. Red Sea Global was recognized for setting new global standards in AI-led conservation.
Scalability & Outlook
The marine AI suite will be expanded to monitor mangroves, seagrass, and marine mammals along the entire Red Sea coastline. Red Sea Global plans to launch a biodiversity analytics platform that supports researchers and policymakers globally. The team is exploring collaborations with universities to train marine AI experts and with tourism operators to balance environmental protection with eco-tourism expansion. AI-guided conservation is expected to become a central pillar in Saudi Arabia’s marine sustainability strategy by 2030.
Related: Ways Saudi Arabia is using AI
Closing Thoughts
Saudi Arabia’s bold and strategic adoption of AI demonstrates how a nation can harness technology to transform public services, industries, and environmental stewardship. Across the 15 case studies highlighted, AI is not just improving efficiency—it is redefining how problems are identified, solutions are scaled, and value is delivered to citizens and businesses alike. From reducing flight delays and fraud losses to preserving coral reefs and enhancing cancer diagnosis, these initiatives reflect a national commitment to innovation with measurable impact.
What sets Saudi Arabia apart is its focus on scalability, ethical deployment, and integration of AI into core national objectives such as Vision 2030. By embedding AI into sectors like energy, health, education, and logistics, the Kingdom is setting a precedent for purposeful digital transformation. As AI technologies continue to evolve, Saudi Arabia’s proactive approach positions it to lead the region—and potentially the world—in building intelligent, resilient, and future-ready systems.
