Don’t let these three common cloud mistakes derail your AI projects

2024 has marked a transformative period for AI, with widespread cloud adoption paving the way for its integration. Over the last few years, we’ve seen a surge in cloud adoption, with 94% of large enterprises now using it in their operations. It's likely that we'll see a similar surge in AI adoption by 2025.

While great progress in AI projects has been made this year, 67% of companies still report that their AI integration is either limited or non-existent. This suggests that many organisations are still facing challenges in fully harnessing the potential of AI, and often due to mistakes related to adopting and managing their cloud infrastructures. 

In this article, we’ll uncover the top three cloud infrastructure mistakes commonly made by businesses that inhibit their AI integrations, along with some tips for unlocking AI’s full potential.

Mistake one: Assuming all AI workloads are the same 

AI projects often require specialised, high-performance computing resources, especially for more complex or demanding tasks. A common mistake made when it comes to AI implementation is overlooking how these computational demands are unique and vary from one business to another. With this in mind, companies should consider their infrastructure on a case-by-case basis rather than adopting a one-size-fits-all approach and risking suboptimal performance and missed opportunities.

By carefully assessing the specific hardware needs for each AI workload, businesses can ensure that their cloud environments are optimised for performance and resource allocation. An example of this could be one of our customers who is developing an alternative to neuro-symbolic architecture, which combines neural and symbolic learning and is designed to act similarly to the human brain. The company needed a hosting provider to train one of its products - the Expert Verbal Agent (EVA) model, a Large Language Model (LLM) designed for thoughtful queries and problem-solving. EVA can use a Central Processing Unit (CPU), Graphic Processing Unit (GPU), or both, unlike many AI models, which only run on GPUs as their computational model. Therefore, the setup of most AI models requires a bespoke cloud offering that is tailored to their specific needs.

Mistake two: mismanaging server scalability

AI workloads can fluctuate with periods of intensive processing, like model training. This means that environments need to be appropriately sized to cope with temporary spikes in demand whilst also ensuring that resources aren’t wasted during quiet periods. This is especially crucial for companies with limited budgets, as overprovisioning can lead to significant cost overhead. Meanwhile, running AI processing on environments that are too small or that lack resources will create long delays, potentially hindering operations.

To optimise performance and cost-efficiency, AI environments must be able to scale dynamically. Businesses can solve this by leasing time on pre-built environments as an alternative to capital investments. 

However, when using this approach, it’s essential to consider which type of environment is the best fit for their unique requirements. Public cloud options can quickly become costly when scaling up, whereas private clouds have more predictable and lower running costs. So, for companies that have ongoing workloads and capital to invest, a private cloud might be the most suitable option in the long term.

Hybrid options are another avenue to explore. Combining the benefits of both types of environments - the scalability and flexibility of public clouds with the control and security of private - they provide a balanced approach for AI projects. Strategically distributing workloads across public and private clouds can help organisations optimise performance, cost-efficiency, and risk mitigation. 

Mistake three: Security as an afterthought 

There’s no question that security should be a top priority for any business in today’s world, especially for those operating in highly regulated industries like financial services or healthcare. However, organisations are so focused on the potential benefits of AI that they are failing to implement robust security measures, potentially leading to severe consequences, including data breaches, reputational damage, and financial loss.

Choosing the right cloud environment goes hand in hand with security, and both public and private cloud systems offer distinct advantages and drawbacks. Public clouds, while they might appear more affordable and flexible for scaling, present heightened security risks due to shared server infrastructure. Conversely, private clouds provide dedicated resources and tighter control but demand significant upfront investment in infrastructure.

It's important for companies to choose a cloud provider that aligns with their specific business needs, and one of the most crucial factors for companies to consider when selecting a provider in 2025 will be the strength of its security measures.

Bonus tip: partner with a Managed Service Provider

Our IT Skills Gap Report 2024 revealed that a significant 65% of UK businesses opt to work with a Managed Service Provider (MSP) to achieve their goals cost-effectively. Partnering with an MSP can be an attractive option for businesses because they offer efficient infrastructure and hardware management, safeguarding against unnecessary expenses and misconfigurations. MSPs provide efficient infrastructure and hardware secured by a service that works to prevent unnecessary costs and misconfigurations. 

Additionally, 61% of businesses utilise MSPs specifically for their cybersecurity expertise. They provide continuous monitoring and protection against threats and vulnerabilities, helping to secure cloud environments. What's more, businesses can benefit from expert guidance in optimising their cloud strategies and selecting the most suitable and resilient environment for their specific AI needs.

Looking forward

Looking ahead to 2025, the convergence of AI and the cloud is set to drive significant advances in technology. The maturation of generative AI and the growth of agentic AI - the 'third wave' of artificial intelligence - will have a profound impact on a wide range of industries, from finance to healthcare. The potential applications of AI are vast, and as these technologies continue to evolve, we can expect to see significant improvements in efficiency, productivity and innovation.

To unlock the full potential of AI and drive innovation, organisations should take a phased, strategic approach to adopting these new tools. A well-defined cloud strategy focused on performance, scalability, security, as well as cost optimisation will help modern businesses maximise resource efficiency while minimising risks. By partnering with an MSP, organisations can streamline this journey and position their infrastructure to be AI-ready and deliver long-term value.