The gloomy outlook for the global economy in 2023 has pushed several tech firms and Wall Street titans to lay off staff across offices worldwide. A string of tech companies like Microsoft, Twitter, and Meta, are already in the grasp of this layoff wave. So why do companies need to lay off employees even when reporting year-high profits? The theory behind layoffs is that it saves the company money, even though there’s an initial severance payment of millions or billions of dollars. This is because of the belief that cutting salaries will gradually decrease the business’s costs. While this is true in hindsight, this also robs companies of some much-needed man force that could have been better used elsewhere. So, this begs the question: Is there any solution to address this global problem of layoffs?
Often lauded for his significant original contribution to the field, Sandesh Achar tries to answer this question in his new research, which focuses on the adoption of artificial intelligence and deep learning techniques in cloud computing for operational efficiency. It is well documented that, as a company, it is important to prioritize cost-saving measures to avoid layoffs. And Sandesh believes that cloud computing is one area where significant savings can be made.
Recent rapid advancements in cloud computing technology have led to the incorporation of AI to improve and optimize the technology services provided. Autonomous computing, in which systems do stated tasks without human involvement, has been made possible using AI in cloud-based applications. The integration of AI/ML into cloud computing to improve its performance and resource allocation is now very much a possibility.
Nowadays, AI algorithms are effectively used in several fields and have greatly outperformed the prior state-of-the-art. Deep learning and machine learning are resource-intensive approaches that need training data to complete a given task effectively. Today, high-performance tensors and GPUs are used for training AI models. But, with the aid of AI-based cloud computing technology, it is now possible to train and use AI algorithms effectively online. The performance, digital transformation, and efficiency of the cloud can all be improved by incorporating AI. An AI-enabled cloud computing environment is crucial to help enterprises become more strategic, insight-driven, and effective while providing more cost savings, agility, and flexibility.
The problem of resource management has gone unattended for too long. Software engineers are working to create a smart factory supported by AI algorithms’ capabilities that is scalable and reliable thanks to the cloud. Cloud computing with AI attempts to create a self-managing cloud service with high productivity. To put it another way, as AI advances, more advanced public and private clouds may significantly rely on AI platforms to manage, track, and self-heal when an issue occurs.
Businesses can now transition from semi-organized to unstructured documents cognitively automated with the help of AI and cloud computing platforms, pushing the boundaries of active infrastructure management, and assuring minimal effect and downtime. As a result, the customer experience is improved, and business expenses are reduced.
The best part is Cloud computing only charges businesses for the resources they utilize. This means a significant portion of company’s money can be saved simply by using cloud computing compared to the conventional infrastructure cost of designing and maintaining massive data banks and centers. The saved money can be used to develop more strategic AI tools and accelerators that can be used to generate more revenue and make more cash available for the company, thereby leading to enhanced working quality and lower expenses. The result will be lesser layoffs as the companies will have more cash in their bank!
In conclusion, implementing AI-based algorithms and approaches will boost operational effectiveness, cost savings, automation, consumption of energy, and the ability to solve complicated cloud computing issues and, most importantly, put an end to layoffs problems due to better operational efficiency. After all, isn’t that what we all want?
