According to Forbes contributor Bernard Marr, the global economy will create ninety-seven million new AI-related jobs between now and 2025. However, the ever-present skills gap makes it difficult for organizations to find employees with the necessary abilities to create, train, and collaborate with intelligent machines. 

These struggles will only grow as organizations recognize the efficiency gains of leveraging ML, computer vision, and related technologies. The demand for skilled workers will quickly outstrip the supply. With that in mind, Marr offers an overview of critical skills for IT practitioners to sharpen to prepare for work with the future’s automated, intelligent machines. These include: 


While no-code and low-code AI solutions are beneficial in many scenarios, any organization that wishes to deploy its own bespoke AI solution will still require skilled coders for the foreseeable future. Marr recommends at least a basic understanding of Python, R, C++, and Java—the most popular programming languages for AI. 


This skill focuses on administering and managing all connected systems in delivering modern AI infrastructure to ensure continuous uptime and a good level of service—whether it’s to the business itself or its customers. In addition, AIOps ensure the implementation of ML processes to support more efficient use of data within the company or its IT infrastructure. For more on how this works, look at this previous APEX of Innovation post

Data Science 

Since data is fundamental to machines’ ability to think and learn, it should be no surprise that data science is a critical skill for anyone looking to work with AI. Without ample data science talent, companies will lack the advanced analytics necessary for ML algorithms. 

Communication and Visualization

Those in the analytics industry recognize computers’ abilities to make decisions and provide a deeper understanding of complicated subjects than is possible using human-scale analytics alone. However, without the ability to communicate the implications of these findings to other humans, this knowledge is virtually useless. As such, “data communicators” and “data translators” are increasingly sought after by businesses. In addition, Marr notes that strong visualization skills are critical in translating ML insights into business value. 

For more on these and other in-demand AI skills, read his Forbes article here.