Artificial intelligence (AI) is becoming more intelligent and efficient than humans. As a result, many jobs will be automated and replaced by AI in the near future. Robots are extremely intelligent and have a low margin for error when compared to humans. They are superior to humans in many ways. That is why machines are taking over so many jobs. Machines are not only less expensive, but they are also less troublesome. They would also not ask for a raise or slack off and would work nonstop for 24 hours a day, seven days a week. However, AI will never be able to replace all human jobs, but it will assist employees in performing better in jobs that cannot be fully automated. In this article, we will discuss eight IT jobs that will never be replaced by AI.
Any kind of finished software product goes through a process where we first gather all the specifications for the various features that will be included in the software, the various metrics on which the success or failure of the features will be based, and also make decisions regarding the architecture of the system while keeping in mind the most effective tools to use. For instance, selecting the databases that will power the system and considering how to translate strictly business-related tasks into code are all decisions that call for strategic, intuitive, and intuitive decision-making. Additionally, Software Developer jobs have the added challenge of overseeing cooperation between various teams that make up a single system.
Project managers are in charge of overseeing the entire project, including planning, budgeting, and resource allocation, even though AI can assist with scheduling and task management. Unlike project managers, AI will immediately give up when it comes to leadership, communication, stakeholder management, and decision-making because it is a data-feeding ML tool. A project manager cannot be replaced, but AI can be seen as an extension of the project management process. Project managers need to be able to adapt to changing conditions, comprehend and manage team interactions, and maintain a broad perspective of the project's goals. AI cannot perform these tasks because they require human intuition, creativity, and expertise.
Although AI can assist in network troubleshooting and provide some automation support, network engineers are in charge of designing, building, and maintaining complex computer networks. Network engineers will not become obsolete simply because AI exists. AI tools such as chatgpt and GPT-4 can only do so much automation. When there is a cloud outage, chatgpt will not be able to fix it and will need to be manually fixed. ChatGPT cannot perform tasks such as subnetting, device configuration, QoS, tailoring the networking experience to your specific environment, or cable installation. As a result, these IT Jobs will always be in high demand. They must also continue learning about new technologies such as cloud computing, IPv6, and automation to keep up with the latest advancements.
A QA engineer's job entails testing a product for various complex business scenarios. They must put on the hats of multiple users and consider the product's usability from all angles. To understand the full rationale behind a feature and to intuitively differentiate between a bug and a feature, a QA engineer must interact with the software developer, product, and design. Again, AI has the potential to automate several aspects of their journey and eliminate redundant or repetitive tasks. For example, AI can aid in the automation of integration tests, which are used by QA to determine whether an entire integrated system with multiple parts is functioning properly. All of the test scenarios for a given product would still require human intervention in these IT Jobs UK
Computer System Analysts
No matter how automated we become, there will always be a need for a human presence to perform maintenance work, update, improve, correct, and set up complex software and hardware systems that frequently require coordination among multiple specialists to function properly. A Computer System Analyst, a profession in high demand in recent years, is responsible for reviewing system capabilities, controlling workflow and scheduling improvements, and increasing automation. We will soon see the Internet of Things enter our lives, in which an enormous number of devices will be interconnected, exchange data (ostensibly for our benefit), and interact actively with one another. As a result, this particular professional category is limited.
While AI can detect potential threats and vulnerabilities, the job of a cybersecurity analyst is to protect an organisation's digital assets proactively and respond to incidents in real-time. Although AI, such as ChatGPT or GPT-4, can assist in automating certain cybersecurity tasks, such as identifying and responding to security incidents, it cannot replace critical thinking, problem-solving, and strategic decision-making skills possessed by Cybersecurity IT Jobs. We know that there is a new cybersecurity threat every day that is constantly changing with time and that human expertise is absolutely required to protect computer systems and networks from unauthorised hacking, theft, and damage.
While AI can make suggestions and recommendations for hardware problems, it cannot repair or replace hardware components. If a person's computer fails, he will not go to ChatGPT or Bard to have it repaired. These AI tools can provide some data and information on how to fix hardware problems, but a hardware technician must resolve the issue. A hardware technician troubleshoots and repairs computer components such as CPUs, motherboards, and hard drives. They frequently use specialised tools and equipment to resolve problems and ensure that the computer runs properly. AI can only replace this job if it grows a pair of hands.
We would need data scientists/machine learning (ML) engineers to create new algorithms to optimise existing ones and solve new problems. Machine learning algorithms can help in a variety of fields, including supply chain management and quality control in large-scale factories. If some changes are expected, a convoluted neural network may need to be debugged to optimise or correct the expected behaviour, which requires human intervention to understand and unravel the code to find the fixes. An intuitive human can handle performance metrics monitoring of the algorithms that are being used and subsequent calls for betterment or trying a new approach well.