Table of contents
In this article, you will learn:
- How cloud technologies and AI interact with each other
- What the financial perspective of combining cloud with AI looks like
- What to pay attention to when combining AI with cloud technology
- What the shared future of cloud and AI looks like
The continuous development of cloud computing and artificial intelligence means that both cloud providers and their clients are increasingly combining these two technologies within a single platform. What are the pros and cons of AI-powered cloud solutions, and is it worth considering their implementation in the workplace?
What are the advantages and disadvantages of AI-powered cloud solutions?
Although there are many cloud management tools available on the market, the popularity of AI-powered cloud solutions is growing today due to the dynamic development of artificial intelligence and its accompanying technologies. And while at first glance it may seem that cloud computing and AI are unrelated, there is nothing to prevent these technologies from being combined. A report published by Gartner shows that in 2023, global spending on public clouds alone reached 600 billion dollars, and data from Goldman Sachs indicates that the AI industry will be worth 200 billion dollars by 2025. It can therefore be assumed that the synergy between these sectors will continue – AI development requires computing power that cloud technologies can provide, and clouds can improve their quality through the implementation of artificial intelligence. The advantages of AI cloud solutions have already been recognized by cloud providers such as Microsoft and Google. Combining these tools allows for easy resource scaling, which can be important for growing businesses, and also enables the automation of many processes. It is also invaluable in data analysis. At the same time, cloud computing using AI is easy to use and ensures an appropriate level of security and data privacy. However, the disadvantages of AI in the cloud cannot be ignored. In addition to costs (which depend mainly on the chosen payment model and data consumption), one can mention vendor lock-in, as well as many security-related risks – despite constant improvements in security, hackers are finding new methods of attack.
What are the applications of AI-powered cloud solutions?
What is the reason for the growing popularity of AI-powered cloud solutions? Among other things, it is the broad possibilities offered by combining these technologies. Today, they can be adapted to the specifics of virtually every industry – this applies not only to highly algorithmic industries that rely on executing specific sequences of actions, such as manufacturing, but also to sectors requiring a “human touch,” such as healthcare or education. How, then, can AI in the cloud help in business? One of the most common applications is business process automation – companies mainly automate tedious and monotonous tasks, and the development of AI expands the scope of activities that can be automated. According to a McKinsey report, thanks to advances in AI, it will be possible to automate up to 50% of all business processes between 2030 and 2060. AI in the cloud is also used for data analysis and business intelligence activities. The disk space offered by cloud platform providers allows for the analysis of large datasets, which in turn translates into improved decision-making efficiency. The results can be used, for example, in building advanced recommendation systems or in predictive analysis. It is also worth noting that artificial intelligence in cloud platforms allows for the implementation of natural language processing in business processes. As a result, users do not need to know the basics of programming, as they are able to perform at least some tasks with the help of a chatbot that will process commands written in natural language.
Challenges associated with implementing AI-powered cloud solutions
The implementation of cloud solutions using AI is generally similar to choosing between a generalist programmer and a specialist – every company must answer for itself whether a general cloud, which can handle a wide spectrum of activities, is a better choice, or a specialized one that will work great in one area, e.g., data analysis. Regardless of the choice, one must be aware of the challenges associated with implementing AI in the cloud. The first (and most important from an economic point of view) is cost. Although these depend largely on data consumption and the billing method used, it cannot be denied that providers may charge higher fees for implementing AI-powered tools, as well as related activities – such as data migration or staff training. Another problem – both financial and technical – is integration with existing infrastructure. It cannot be ruled out that this will be expensive and difficult to perform, e.g., due to the incompatibility of solutions. Every company must also reckon with challenges in terms of security and data management. This refers not only to the types of security used but also to adapting the operation of the cloud and AI tools to the regulations in force in a given area. Equally important is the constant monitoring of the platform’s operation to avoid the accumulation of problems. It is also worth emphasizing that implementing AI in the cloud is not just a technical challenge, but above all a cultural and organizational one. Such tools can cause anxiety and resistance among employees, so it is necessary to clearly inform them from the very beginning about the goals and scope of the implementation, as well as to make them aware that it serves only to improve work.
Future trends in AI-powered cloud solutions
It cannot be denied that the development of both cloud platforms and artificial intelligence will be coupled and driven by achievements in other areas. The market offers a whole kaleidoscope of technological solutions. One can be sure that the quality of AI tools will improve year by year, which may be due to the development of learning methods, e.g., expected progress in automated machine learning or federated learning, in which models are trained using many independent sessions using different data. Other technologies that will influence the development of cloud solutions using AI include augmented reality (AR) and virtual reality (VR) – these may prove particularly useful in the entertainment industry, but they may also find application in e-commerce. It can be expected that cloud computing will also develop alongside edge computing – processing data closer to the source of its generation is not only positive for the environment (which organizations will place increasing emphasis on), but also allows for better management of Internet of Things devices. One of the most important trends in the development of both clouds and AI will be XaaS – everything as a service (described, among others, by the KPMG report). It cannot be ruled out that companies will decide that it will be much faster and, in the long run, cheaper to entrust all IT operations to external providers. This may result in high dependency, but ultimately the convenience and lower costs of such a solution may prevail. Will that be the case? Time will tell.
