Home Uncategorized Addressing the infrastructure requirements needed for effective AI adoption

Addressing the infrastructure requirements needed for effective AI adoption

Addressing the infrastructure requirements needed for effective AI adoption
Addressing the infrastructure requirements needed for effective AI adoption

The COVID-19 pandemic has greatly accelerated the rate of artificial intelligence (AI) adoption throughout the business landscape, but in doing so, it has also placed a huge amount of unexpected extra demand on many organisations’ existing computing resources and supporting infrastructure. 

A recent report into the state of AI and machine learning found that throughout 2020/21, over half (55%) of businesses questioned had significantly accelerated their AI adoption compared to the previous year, with more than two thirds (67%) of respondents expecting to increase it even further over the coming 12 months. With adoption accelerating at such an unprecedented rate, the cost of overheads is becoming more central to discussions. So much so that business leaders who fail to properly plan ahead can quickly find themselves with a serious headache. 

The rapidly growing trend towards AI adoption was reinforced by Ritu Jyoti, group vice president for AI and Automation Research at IDC, who recently commented that COVID-19 and the problems it created had quickly “pushed AI to the top of the corporate agenda, empowering business resilience and relevance.”

Jyoti added: “We have now entered the domain of AI-augmented work and decision making across all the functional areas of a business. Responsible creation and use of AI solutions that can sense, predict, respond and adapt at speed is an important business imperative.”

In light of these comments, it’s perhaps unsurprising that IDC expects the value of the global AI market to increase by more than 15% to $341.8 billion this year and surpass $500 billion by as early as 2024.

Flexibility is crucial in modern business infrastructure

When it comes to modern business infrastructure, flexibility is absolutely crucial. This makes cloud – and specifically hybrid cloud – the ideal foundation of AI, particularly as the volume of data involved continues to grow over time. The versatility of hybrid cloud solutions means organisations of all sizes can meet the technology demands of AI at a cost that is sustainable to them, even as their business naturally fluctuates over time. 

Adopting Infrastructure-as-a-Service (IaaS) gives organisations the ability to use, develop and implement AI without sacrificing performance. However, choosing a suitable IaaS provider is key, and there are a number of elements that every organisation needs to address before making a final decision. 

1. Storage scalability and capacity

For many businesses, the ability to scale storage as data volumes grow is now a fundamental requirement. However, the type of storage best suited to an individual business’s needs depends on a variety of factors including whether they need to make real-time decisions, as well as the level of AI they plan to use. For example, a financial organisation that uses AI to make real-time trading decisions will likely require an extremely fast, all-flash storage solution. However, other companies in sectors that don’t move quite so quickly tend to be better served by larger capacity but less rapid storage options. 

Another major consideration for businesses is how much data their AI applications will generate. The more data AI applications are exposed to, the better, more accurate decisions they are likely to make. Databases continually grow over time as well, so storage and expansion capacity need to be under constant review in order to avoid unanticipated issues down the line. 

2. Overall computing performance

In order to take full advantage of the benefits AI has to offer, organisations need access to powerful computing resources. In particular, machine learning algorithms require speed and performance to constantly transact vast numbers of calculations.

While CPU-based environments can handle basic AI workloads, deep learning involves multiple large data sets and the capability to deploy scalable neural network algorithms, which can make GPUs a better bet. The superior performance provided by GPUs can significantly accelerate deep learning compared to CPUs. However, it’s perhaps no surprise that this increased speed and performance comes at a significantly higher cost, which means it may not always be financially viable to make the switch. Ultimately, finding the right balance for the required task is key.

3. Data security

AI can involve handling large amounts of sensitive data, including personally identifiable information and financial records, so it’s critical that any infrastructure used is fully secured with the latest end-to-end encryption technology. 

It goes without saying that a data breach can be disastrous for any business, and history is littered with examples of this. However, the introduction of AI adds another element of danger to this because any infusion of bad data (whether malicious or otherwise) could potentially cause the system to make incorrect inferences, leading to flawed decision making that could significantly impact operational efficiency.

4. Reliable networking infrastructure

Networking is another key component of AI infrastructure. Good, fast, reliable networks are essential to maximising the delivery of results. Deep learning algorithms are highly dependent on communications, so networks need to keep pace with demand as AI efforts expand. Scalability is a high priority and AI requires a high-bandwidth, low-latency network. It is important to ensure the service wrap and technology stack are consistent for all regions.  

5. Overall cost-efficiency

The more complex an AI model is, the more expensive it is to run and maintain. Consequently, it’s important to ensure that the extra cost can be justified by the extra performance achieved. Businesses need to make careful choices and identify IaaS providers that can offer cost-effective dedicated servers as a means to boost performance and enable them to continue investing in AI without increasing their budget.

Any organisation looking to successfully incorporate AI into its business operations for the first time must ensure the right infrastructure is in place to support the move. Finding the right IaaS provider will play a huge role in this and can ultimately dictate overall success or failure. As such, it’s crucial that organisations take the time to find a provider that is best suited to their overall business needs and objectives. Focussing on the five key areas specified in this article is a great place to start and will help set you on the path to long term success.