Originally published by TechRadar
Artificial Intelligence is introducing a new wave of technological capabilities, and therefore, businesses are increasingly looking for ways to integrate it into their products and day-to-day operations. As businesses race to unlock the potential of artificial intelligence, they increasingly recognize that cloud infrastructure is essential. It may come as a surprise, however, that although 67% of companies report having advanced cloud infrastructure, only 8% have fully integrated AI into their business processes (Infosys & MIT, 2024). And this figure highlights a clear disconnect that, despite cloud maturity, businesses are lagging behind when it comes to AI implementation.
This article will explore the reasons behind this lag and outline the key strategies for businesses to align their cloud infrastructure with the specific demands of AI to unlock its full potential.
Why is there a disconnect between cloud and AI readiness?
There are many factors to consider when implementing AI and the most important of which is cost. Currently, the biggest challenge in adopting AI is the significant upfront investment required to create an AI-ready environment. The hardware costs alone often don’t match the short lifecycles of the technology, as it evolves rapidly and organizations need to continually upgrade their systems to meet the demands. As a result, it can be difficult to justify the long-term ROI of AI.
Many organizations are rushing to integrate AI tools into their operations without fully considering the infrastructure implications. Despite widespread recognition of AI’s potential, as evidenced by 98% of executives expecting increased AI spending on the cloud, businesses often neglect the specific technical requirements of AI.
To effectively support AI workloads, organizations must prioritize compatibility, scalability, security, and cost-effectiveness. However, performance remains a critical factor, and striking the right balance between Graphics Processing Units (GPU) requirements and costs is essential. AI’s demanding nature necessitates cloud environments capable of handling intensive data processing, low-latency response times, and specialized hardware like GPUs or custom accelerators.
Another factor influencing the disconnect is the IT industry’s ongoing skills gap, with 84% of UK businesses currently struggling to source the talent they need to address their IT challenges. Since there are a limited number of skilled professionals who can manage AI workloads, even businesses that have prepared their cloud infrastructure may lack the expertise needed to fully embrace AI’s capabilities.
Key considerations for AI
1. AI workloads
The specific AI requirements of different companies can vary significantly. For example, a company developing an advanced image recognition system may have different infrastructure needs than one building a sophisticated chatbot. To address these unique demands, bespoke cloud optimization strategies are essential for businesses to consider.
Each AI project has unique resources and high-performance computing requirements. For example, one of our customers is developing an alternative to neuro-symbolic architecture, combining neural and symbolic learning, which acts similarly to the human brain. The company needed a hosting provider for training one of their products – the Expert Verbal Agent (EVA) model, an LLM designed for thoughtful queries and problem-solving. Unlike many AI models, which only run on GPUs as their computational model, EVA can use CPU, GPU, or both. Consequently, they required a CPU-powered server for software development and testing.
2. Scalability
Scalability is vital for AI, but it must be balanced with cost-effectiveness. An AI environment should be able to adapt to changing demands, providing additional processing power when needed but this can be expensive.
AI workloads can be unpredictable and fluctuate in size. It’s common for AI workloads to be needed only for short, intensive periods of time, for example to regenerate a model. This often means that the hardware involved sits idle for long periods of time and therefore does not generate a return on investment. This is an important consideration for companies looking to build AI-enabled platforms, who should consider leasing time on pre-built environments as an alternative to ensure the best and most resource-efficient outcome. While public cloud models offer flexibility, they tend to be more expensive for such projects, especially during peak usage periods. Organizations need to carefully consider their scalability demands and choose the infrastructure that is right for them.
3. Security
Security is critical in AI projects, especially when outsourcing GPU or processing components. Sensitive data must be protected to safeguard customer privacy. While public cloud models can be convenient, they may not offer the same level of security as private or hybrid cloud solutions, where servers are dedicated solely to a business. Businesses should evaluate the sensitivity of their data and select a cloud environment that aligns with the security and control requirements of their AI workloads.
AI security in the cloud is a critical concern as organizations increasingly leverage the power of artificial intelligence (AI) to process and analyze vast amounts of data in cloud-based environments. The first key aspect of AI security in the cloud involves protecting the AI models and data. Encryption and access controls are vital to ensure that sensitive AI models and training data are safeguarded from unauthorized access or breaches. Additionally, regular audits and monitoring are essential to detect any unusual activities or vulnerabilities that could compromise AI systems in the cloud.
4. Performance
Certain AI tasks require specific hardware to run most effectively. In some scenarios, GPUs are essential, while some projects require specialized AI chips or TPUs (Tensor Processing Units). These chips are specifically designed to deliver the best performance when processing machine learning workloads. It is extremely important for companies to understand the specific technical needs of each project when choosing the perfect architecture for running an AI model, as there are many variations of hardware that can be used for these platforms.
Understanding the memory requirements of the AI model being trained is also extremely important. Some models will not fit on a basic graphics card, while others will require huge amounts of onboard RAM to be processed at all. NVIDIA‘s latest cards, such as the H100 NVL, have a whopping 188GB of HBM3 memory, allowing very large models to be trained. Cloud providers often have access to advanced hardware and infrastructure that can significantly improve the performance of AI algorithms and reduce training time.
Steps to bridge the disconnect
To bridge the gap between cloud readiness and AI integration, businesses can start by understanding their key requirements and clarifying desired goals for the AI. This will allow the creation of a comprehensive brief which is an essential first step.
Next, evaluate existing cloud capabilities and identify key goals and requirements in order to identify any gaps in performance, scalability, or data handling – all necessary for the effective use of AI applications. Furthermore, establishing data management, security, and compliance policies ensures that quality data is readily available for AI initiatives.
Companies should also consider which cloud infrastructure best suits the unique needs of each AI project. For example, if security and regulatory compliance are priorities, hybrid or private cloud models, with infrastructure dedicated to a business rather than shared across businesses, maybe a better fit than public cloud options.
Finally, incorporating regular performance evaluations and iterative infrastructure adjustments will help maintain alignment with evolving AI capabilities, ensuring a strong foundation that adapts as AI technology advances.
Working with a Managed Service Provider
These steps can seem overwhelming to tackle alone, which is why some businesses opt to work with a Managed Service Provider (MSP) on their AI integration. Currently, 65% of UK businesses work with MSPs as they offer a holistic approach to AI optimization by supporting infrastructure design, compliance, and ongoing optimization. MSPs also help companies with their security posture through continuous monitoring to protect cloud environments from threats and vulnerabilities.
Additionally, MSPs can help bridge the skills gap, which remains a common barrier to successful AI adoption. In fact, 46% of businesses use MSPs to address the ongoing skills shortage. MSPs can help businesses achieve their AI goals cost-effectively by providing the most efficient infrastructure and hardware backed by their expertise and service. Collaborating with cloud infrastructure management experts also reduces the risk of misconfigurations as well as unnecessary costs, ensuring that businesses have an optimized and secure foundation for AI.
Cloud readiness and AI go hand-in-hand
As AI continues to transform our lives and modern businesses AI integration will be essential for companies aiming to stay competitive. By tailoring cloud infrastructure to AI-specific requirements and leveraging the expert knowledge of MSPs, organizations can overcome the most pressing hurdles (financial, technical, and talent-related), to make the most of AI’s potential. With a strategic approach and the right support, businesses can lay a solid foundation that can not only meet current demand but also adapt as AI technology evolves.
