How AI can help with the five hardest roles in IT

As most countries around the world celebrate Labour Day on May 1^st, the growing influence of technology, especially artificial intelligence (AI) capabilities could eventually bring a new definition to the occasion. There is a growing concern that AI could eventually replace most roles in organizations.

While Labour Day was initially regarded as a day to honor the achievements of workers, most of the roles that were celebrated are becoming increasingly scarce, experiencing a shortage or being replaced with AI. With more industries increasing their use of technology, there are more IT roles in the market, but the skilled IT workforce isn’t enough to meet demand. Hence, organizations are opting to use AI.

According to a recent IDC report, Enterprise Automation to Mitigate the Digital Skills Shortage , around 60%-80% of Asia Pacific organizations find it difficult or extremely difficult to fill vacancies in many IT roles including security, developers, and data professionals. Major consequences of the skills shortage are increased workload on remaining employees, increased security risks, reduced customer satisfaction, and loss of critical knowledge.

Although big tech companies’ layoff announcements are making headlines, these layoffs are not representative of the overall skills shortage in the market. For organizations, the shortage of workers in the IT sector is still an issue that can’t be resolved by just relying on AI. With technology constantly evolving, the job market in the tech industry is always changing as well.

In this article, Tech Wire Asia takes a look five most challenging roles in IT that require a combination of technical expertise, analytical skills and adaptability, and how AI can help ease the burden.

Cybersecurity Specialist

Without a doubt, cybersecurity specialists continue to be in demand, especially with increasing cyberthreats today. Responsible for protecting an organization’s network, systems and data from cybercriminals, cybersecurity specialists need to be well-versed in the latest security technologies and techniques and must continually update their skills to keep up with new threats.

Be it the Chief Information Security Officer or any other cybersecurity-related role, being able to take a proactive approach to identifying potential vulnerabilities in systems and networks is a prerogative for them.

While the role is challenging, most cybersecurity specialists today rely on AI-powered tools that can help automate threat detection and response processes, making it easier for them to identify potential vulnerabilities in real time. Machine learning algorithms can also analyze large amounts of data to identify patterns and anomalies, flagging potential security threats before they cause harm.

Cloud Architect

Businesses have increased their cloud adoption as it provides greater flexibility and scalability to their IT infrastructure. The cloud architect is responsible for ensuring the implementation of cloud-based solutions meets the company’s needs while ensuring security and reliability. While there are managed service providers that can manage these for organizations today, businesses should still consider having a cloud architect to help them with their decisions.

At the same time, the role of the cloud architect is also becoming increasingly challenging especially with businesses taking a multi-cloud approach. The complexity of using the multi-cloud requires the cloud architect to be able to understand how to integrate and create a cohesive solution.

Fortunately, cloud architects can rely on AI-powered tools to help automate the deployment, scaling, and management of cloud infrastructure, making it easier for cloud architects to optimize resources and ensure high availability. With machine learning algorithms, cloud architects can also analyze performance data to identify potential bottlenecks or areas for improvement.

Data Scientist

Exponential data growth requires data scientists to uncover insights and solve complex business problems. Today, data scientists are challenged by the sheer volume of data. While data management tools and artificial intelligence is providing them a big help in managing the data, the reality is they still need to have the right understanding of the tools, such as programming languages like Python and Java.

AI can also help automate data processing and analysis tasks, making it easier for data scientists to focus on the most critical tasks. Machine learning algorithms can also help identify patterns and trends in data, making it easier to uncover insights and make predictions.

DevOps Engineer  

Responsible for bridging the gap between software development and IT operations, DevOps engineers must be proficient in both disciplines and have a deep understanding of automation and optimization techniques. One of the biggest challenges faced by DevOps engineers is the need to work across multiple teams and departments. They must be able to communicate effectively with developers, IT operations teams, and other stakeholders to ensure that software releases are timely and meet the needs of the business.

Using AI, software deployment and testing processes can be automated today, making it easier for DevOps engineers to ensure high-quality software releases. DevOps engineers can also rely on machine learning algorithms to analyze performance data to identify potential performance issues or areas for improvement.

Software Engineer

Apart from being proficient in programming, software engineers work collaboratively with data scientists and other team members to design and implement machine learning tools. Software engineers today face the challenge of balancing the technical requirements of machine learning with the needs of the business. They must be able to identify the right algorithms and models to use and must be able to explain their findings to non-technical stakeholders.

With AI, the machine learning model-building process can be automated, making it easier for software engineers to create and deploy machine learning models. To speed up performance data analysis to identify potential performance issues or areas for improvement, machine learning algorithms can be used by software engineers as well.

Can AI solve the skills shortage problem?

Despite AI being capable of automating routine tasks and helping with decision-making processes in various IT roles, it is unlikely that AI will completely take over these roles to solve the skills shortage problem. Organizations still need to invest in their IT workforce. Skilled IT professionals will continue to be required to handle complex situations and communicate effectively with the board.

AI is only as unbiased and ethical as the data it is trained on, and there are concerns about AI being used to automate decisions that should be made by humans. In many IT roles, there are ethical and moral considerations that require human judgment and intervention. For example, while AI can be trained on large amounts of data and algorithms, it cannot replicate the human intuition and judgment that is often required in IT roles.

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