What are the most sought after AI skills?

What are the most sought after AI skills?

It is predicted that 97 million jobs involving artificial intelligence (AI) will be created between 2022 and 2025. AI has the potential to transform every industry; however, companies are still struggling to find employees with the skills needed to create, train and work alongside smart machines.

As companies become aware of the efficiencies that can be achieved through harnessing the power of machine learning, computer vision and similar technologies, the demand for skilled workers in the field is rapidly outstripping supply.

Colleges and universities have responded to this by creating new courses and educational programs that focus on these skills. But those looking to enter the industry may still be confused by the options available. So here’s a rundown of some of the most valuable skills you can learn today if you want to be prepared to work with the automated and intelligent machines of the future!


While no-code and low-code AI solutions are appearing that allow us to leverage AI solutions without getting our hands dirty, it is likely that companies looking to deploy their own bespoke AI solutions will still need skilled coders for a long time to come. A basic understanding of at least one of the most popular programming languages ​​for AI – Python, R, C++, and Java – is very helpful for anyone working with machine learning algorithms. This might seem a bit counter-intuitive – as the purpose of AI is to allow computers to “learn” without needing to be coded specifically to do a job. However, most people working in roles involving AI today recommend that some level of coding experience is highly valuable for anyone looking to prepare to use AI.

data science

Data is absolutely fundamental to the ability of machines to think and learn. Data is the input used to train AIs to make decisions and perform tasks. Data scientists understand how to capture, manipulate and work with data to extract insights from it. These skills are essential for the AI ​​field because they cover the advanced analytics needed in machine learning algorithms. Data science has been a part of computer science educational curricula for a long time and today they are often heavily focused on applying AI to solve business problems using available information.


This is a relatively new term that has emerged in recent years to cover the skills needed when it comes to working with the plethora of AI-related tools and services that have become available. AIOps involves administering and managing all connected systems that provide modern AI infrastructure in order to ensure continuous uptime and a good level of service to the end user, which can be the company itself or its customers. It can involve coordinating the use of various AI-as-a-service elements that connect to create the organization’s AI infrastructure. AIOps also refers to the process of administering or overseeing AI analysis of an organization’s IT operations and data. This may involve implementing machine learning processes to enable more efficient use of data within the organization or its IT infrastructure as a whole.

Statistics and probability

These are old-fashioned math skills that are still considered essential for anyone who wants to understand how AI works, why it is useful, and where it can be most usefully deployed. Techniques such as linear regression, logistic regression, clustering, Bayesian modeling, and random forest analysis existed long before AI became a buzzword in business and industry and performed the core task of making predictions based on identifying patterns and in identifying outliers. That’s why they are still at the heart of many of the most sophisticated AI algorithms. Understanding the principles behind how they work is critical to understanding why computers are such powerful tools when it comes to automating decision-making in businesses and other organizations. A firm understanding of statistics and probability is extremely valuable when starting out in AI, as it helps us understand how to articulate problems and propose solutions by selecting the most appropriate models and techniques.

Communication and visualization skills

It’s great to be able to use computers to make decisions and gain a deeper understanding of complicated issues than would ever be possible using purely human-scale analytics. However, if we don’t have the ability to communicate these findings to other humans – and explain why they are so valuable – then everything will be a waste of time. Many organizations have proven that it is possible to bring about positive change on a large scale – both internally and across societies as a whole – using AI and machine-driven decision making. But communication skills are essential to generating the buy-in needed to reap the benefits. This is why “data communicators” and “data translators” are one of the most sought after skill sets when it comes to AI and machine learning in business right now. Strong visualization skills mean the ability to leverage the insights uncovered by machine learning tools and convert them into compelling narratives that communicate exactly what needs to be done, when and by whom, in order to achieve growth and results.

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