In 2024, enterprises are adopting AI for compute-heavy applications, leading to a growing need for high-performance infrastructure. A survey conducted by Gartner revealed that 29% of 644 respondents from organisations in the US, Germany and the UK had implemented and were actively using generative AI (GenAI), making it the most deployed AI solution. The study also predicts that by 2026, over 80% of enterprises will deploy AI-powered applications into their operations. Let’s explore the most prominent Enterprise AI use cases and how AI Supercloud can support these innovations with its advanced infrastructure.
During the COVID-19 outbreak, many companies invested heavily in upgrading their surveillance systems. AI technologies have enabled capabilities that were previously unimaginable, such as recognising patterns, detecting unusual activities and automatically alerting relevant authorities for early warnings. It's no surprise that AI is increasingly integrated into various security applications.
But here’s the catch- processing vast amounts of real-time video feeds requires substantial computational power. The challenge increases as the quality of video resolutions, such as 4K, and the number of cameras grows. Traditional cloud solutions often face latency issues and struggle to provide the necessary scalability, resulting in performance bottlenecks.
The AI Supercloud offers state-of-the-art NVIDIA HGX H100 GPUs, optimised for intensive workloads. Enterprises can easily benefit from our burst scalability with Hyperstack to scale their AI infrastructure on demand during peak times. With GPUDirect Storage and high-performance WEKA networking, latency is minimised to ensure real-time video analytics remain seamless and efficient.
According to a study by the Journal of Safety Research, about one-third of serious car crashes could be prevented by autonomous vehicles. This is why AI has become prevalent in powering functions such as object detection, path planning and sensor fusion. The ability to interpret vast streams of data from cameras, radar and LIDAR in real time is crucial for safety and accuracy.
Developing and deploying AI models for autonomous vehicles demands immense computational power and efficient handling of large datasets, a considerable infrastructure challenge. However, the NVIDIA Blackwell GB200 NVL72/36, available through AI Supercloud, is purpose-built for these compute-intensive tasks. Leveraging the high-bandwidth memory performance, NVLink-C2C technology and dedicated decompression engines of the NVIDIA Blackwell architecture, the NVIDIA GB200 NVL72/36 accelerates data processing 18 times compared to CPUs.
Enterprises use cases for generative AI leverage automated content creation, from text and video generation to 3D model designs. AI is helping brands scale their content production while maintaining creativity and consistency. However, building high-quality generative AI applications demands significant computational power and large datasets, especially during the fine-tuning phase of training large-scale models like GPT-4 or Stable Diffusion. To stay ahead of the competition and reduce time to market, enterprises can access cutting-edge NVIDIA Blackwell GPUs on the AI Supercloud, designed for the fastest performance in training and inference. Our platform also integrates NVIDIA-certified WEKA storage, optimised for high I/O and low latency for efficient and seamless management to handle the large datasets of GenAI models. This combination ensures unmatched performance and scalability for your enterprise AI workflows.
Pharmaceutical companies are using enterprise AI applications to accelerate drug discovery, from predicting molecular structures to simulating biological systems. AI’s ability to analyse vast datasets and simulate outcomes speeds up the development of new drugs. However, simulating complex biological processes and analysing massive healthcare datasets requires significant computational capacity and storage. Another important factor is ensuring data privacy and security in the healthcare industry.
The AI Supercloud is designed with data sovereignty in mind, offering deployments in both Europe and Canada to ensure regional compliance with regulatory requirements. The NVIDIA-certified WEKA storage ensures fast and secure storage for large-scale healthcare datasets, while GPUDirect Storage accelerates data transfer for quicker drug simulations. With our high-performance liquid-cooled GPUs, enterprises can run complex simulations and analyses efficiently.
The financial industry has been employing AI to detect fraudulent activities by analysing patterns in transaction data. As Forbes reports, AI systems improve fraud detection accuracy by more than 50% compared to traditional methods. Such real-time fraud detection systems can spot anomalies and prevent financial losses. However, ensuring that fraud detection algorithms can scale during periods of peak transaction volumes without compromising performance or accuracy is imperative. Traditional infrastructures may not provide the scalability and real-time processing capabilities needed to address this. But with on-demand workload bursting, our AI Supercloud ensures that enterprises can handle fluctuating data loads without experiencing downtime for financial fraud detection.
AI-powered medical imaging tools are improving diagnostic accuracy by helping healthcare providers analyse images for signs of disease. AI models can assist in detecting tumours, analysing X-rays, and interpreting pathology slides, leading to faster and more accurate diagnoses and improving patient outcomes. One of the most impactful applications of AI in healthcare is breast cancer detection. A study by Nature.com found that AI models could match or even exceed the diagnostic accuracy of expert radiologists in detecting breast cancer from mammograms. The study showed that an AI system developed by Google Health achieved a sensitivity of 94.6% compared to 88.0% for human radiologists. This is attributed to the ability of AI to analyse mammograms with greater precision, reducing the rate of false positives and false negatives.
However, running diagnostic models in real-time demands high computational power and secure data storage, especially for large healthcare organisations. AI Supercloud’s NVIDIA-certified WEKA storage provides fast, scalable and secure storage for large medical image datasets, ensuring that healthcare providers can access and analyse these images quickly. GPUDirect Storage further accelerates the process by reducing data transfer times and enabling real-time diagnostics.
Predictive maintenance powered by AI helps manufacturers predict when equipment will fail, reducing downtime and maintenance costs. Sensors on machinery provide real-time data, which AI uses to predict potential issues before they happen. However, processing large streams of sensor data and predicting equipment failures in real time requires powerful AI models and infrastructure that can scale as data grows.
With Hyperstack's burst scalability, manufacturing companies can quickly scale their AI workloads to accommodate large volumes of sensor data. Our liquid-cooled GPU configurations further ensure efficient processing, even under heavy workloads.
Retailers have also started leveraging AI to personalise customer experiences, from personalised product recommendations to dynamic pricing based on real-time customer data. However, providing real-time personalisation requires processing customer data at scale, often leading to high infrastructure costs during peak seasons or sales events. AI Supercloud’s burst scalability enables retailers to scale their infrastructure on demand during peak times, without committing to long-term hardware investments. Our customised configurations ensure that your AI models for personalisation run optimally, delivering real-time insights
NLP models help companies improve customer satisfaction by providing instant answers to common questions. Customers prefer prompt responses from customer service over lengthy wait times. Chatbots developed with NLP models can help retain customers by handling exchanges and returns for large enterprises. By automating about 35% of tasks, chatbots can lead to substantial annual cost savings.
However, deploying NLP models for real-time customer interactions requires significant resources, especially when handling large volumes of queries. Scaling these models to meet growing customer demand while ensuring low-latency responses can be a challenge for many enterprises. With workload bursting through our on-demand platform Hyperstack, enterprises can seamlessly handle large inferencing spikes. This ensures consistent uptime during periods of high demand to maintain smooth chatbot operations even during sudden popularity surges.
But that’s not all, our fully managed Kubernetes and MLOps services streamline the deployment and scaling of NLP models.
A Capgemini study reveals that 68% of supply chain organisations have implemented AI-enabled traceability and visibility solutions. This technology has greatly enhanced transparency throughout the supply chain, resulting in a remarkable 22% boost in efficiency. However, supply chains generate enormous amounts of data, which must be processed in real-time to enable accurate predictions and optimisations. Traditional cloud infrastructures often struggle to scale in response to fluctuating demand, leading to inefficiencies and increased costs.
AI Supercloud provides on-demand burst scalability, ensuring that enterprises can scale their supply chain optimisation systems as needed without over-committing to infrastructure. NVIDIA HGX H100 GPUs offer the computing power necessary to process supply chain data in real time, while our customised configurations ensure that AI models are optimised for specific supply chain tasks.
As AI adoption increases across these key use cases in the coming years, the demand for computing power will continue to grow. Our AI Supercloud provides tailored solutions, NVIDIA’s latest GPU technologies, burst scalability and end-to-end support to ensure enterprises can scale their AI applications efficiently. No matter if it's real-time video analytics or predictive maintenance, our flexible infrastructure helps businesses achieve their AI goals while staying cost-effective, compliant and sustainable. We integrate the world’s most energy-efficient GPU hardware, powered entirely by 100% green energy. Our partnerships with Tier 3 data centres ensure sustainable and reliable power.
Book a call with our experts to identify the ideal AI solutions that align with your budget, timeline and technologies.
AI helps enterprises enhance efficiency by automating tasks, improving decision-making, and enabling real-time data analysis, leading to cost savings and better outcomes.
AI Supercloud offers advanced infrastructure with NVIDIA GPUs, burst scalability, and optimised storage to ensure enterprises can efficiently run compute-heavy AI workloads, reducing latency and boosting performance.
AI Supercloud provides on-demand burst scalability, allowing industries like finance and retail to scale their infrastructure during peak times without committing to long-term hardware investments.
AI Supercloud supports healthcare by offering secure, fast storage and real-time data processing capabilities, ensuring the efficient handling of large medical datasets for applications like image-based diagnostics and drug discovery.