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There’s no doubt that Artificial Intelligence (AI) has taken the world by storm both for business and for pleasure. AI is immersed in countless aspects of our daily lives and with no end in sight. From opening our phones with face ID and digital voice assistants to smart home devices and even working out our quickest way to work. But it is businesses that have also reaped the benefits of this remarkable technology. In fact 37% of companies already employ AI within their operations. AI has helped companies of all sizes combat many different obstacles, such as spam filters to increase security, process automation to improve efficiency and sales and business forecasting to better future decision making. Implementing AI provides companies with huge benefits and opportunities to remain competitive, even becoming a market leader due to the additional flexibility it provides when managing large data repositories, streamlining data, optimising workflows, and producing real-time insights to transform day-to-day operations and re-imagine end customer experience.
Drawbacks: scalability & cost
However, housing AI processes within a business can be challenging to say the least. Implementing AI involves an immediate requirement for vast amounts of computing resources such as processing, RAM, many Terabyte and Petabyte of storage space that is also scalable on demand. During a time of rapid business growth, companies will need the ability to scale up their AI processes to match the level of productivity. If the AI infrastructure is unable to scale, a business will no longer be able to get the most out of their technological investment.
Building and implementing an AI system does not come without a hefty price tag either. Already, analysts predict spending on AI systems will reach almost $58 billion in the next few years. Additionally, on-site service provisions for AI systems are a huge upfront and ongoing investment for businesses. Hardware and on-premise systems require frequent maintenance and can be challenging to scale up or down depending on the growth of the business.
According to a recent AI pricing report, custom AI solutions can cost anywhere from $6000 to over $300,000 including development and rollout. Factoring the ongoing AI services, like for consulting, will rack up extra charges too. These upfront and ongoing costs can be detrimental or even prohibitive for smaller companies wishing to adopt AI into their processes.
Advantages of the cloud
The cloud has gained substantial adoption in the past five years on a business level. For example, by the end of the year, the market is projected to grow to more than $76 billion in 2021 and is predicted this number will balloon to more than $390 billion by 2028. This is also reflected in that 94% of enterprises are already using a cloud service. In principle, cloud computing is the on-demand delivery of IT resources using an internet connection and allows companies of all sizes to achieve brilliant results through increased productivity, efficiency and so much more.
Hosting AI processes in the cloud offers businesses many benefits that combat both cost and scalability. Firstly, as the cloud is accessed via an internet connection, the need for costly on-premise AI hardware and maintenance are eliminated. In turn, these cloud-based environments are enabling enterprises to become more agile, flexible, and cost-effective as this substantially reduces infrastructure management costs for enterprises. What’s more, the adoption of the cloud enables businesses to only pay as much as they use. This is a considerable cost saving over traditional infrastructure costs of setting huge data centres and managing them. The cost saved can be used to set up the more strategic development of AI tools and accelerators that can be further leveraged to generate greater revenue and save fundamental costs for the business overall.
Implementing cloud agnostic solutions that are compatible with any cloud infrastructure and able to access the right resources are a viable solution for companies looking to tackle scalability issues with AI. Scalability refers to the idea of a system in which every application or piece of infrastructure can be scaled up or down to handle increased or decreased load. Implementing a scalable Everything as a Service (XaaS) or Platform as a Service (PaaS) ensures that it can scale up or down to handle the load and not crash. This is vital with an AI application due to the vast amount of data being handled that will need to be increased or decreased according to the needs of the business.
Businesses of all sizes are adopting AI technology to remain ahead of the curve yet are also met with expensive costs and effective ways to utilise the data. With instantly available cloud solutions that are scalable, easy to manage and efficient, makes AI solutions more accessible for organisations. In an ever-increasing competitive market, making sure businesses IT infrastructure is put to the best use is critical and AI processes should not be overlooked.