Career in AI: math and biz dev, the two skills that really matter

Estimated read time 3 min read

Career in AI: math and biz dev, the two skills that really matter

Artificial intelligence (AI) continues to shake up our ideas about the professional skills required. On the one hand, AI requires a thorough knowledge of the underlying technologies (Machine Learning and Deep Learning, for example), data science and statistics. On the other hand, the use of AI in business also requires an ability to keep an eye on business benefits.

And with the rise of AI, technology managers and professionals must determine which of these two seemingly divergent skills they should pursue.

Tools based on generative AI offer compelling productivity advantages for developers and other IT professionals. But it also implies rethinking their role. For those who want to delve into the creation of AI applications, this may be the time to strengthen their math skills. For others who are looking to play a more intellectual role, business development skills are the first requirement.

Mathematics can be at the forefront of a large number of AI works

This is what Maxwell Wessel, head of learning at SAP, says. Maybe it’s time to review math skills, he advocates. “Most IT professionals are going to have to go back to basics: mathematics,” he says. “The problems that were defined when I was young were defined in code. The limits of coding languages, operating systems and hardware were often the most fundamental to understand. In the world of AI, these same systemic problems will be better defined by statistics. The more people understand mathematics, the easier it will be to understand the usefulness of models”.

Mathematics may be at the forefront of a lot of AI work, but for those who are not directly building AI applications or who are not their architects, “the skills that will come into play will be less technical,” continues Mr. Wessel. “They may not need to code as much, but they will need even more capabilities in product management, design and user research to get the most out of their new tools”.

We should expect other changes in the roles of computer scientists. “The function of IT specialists will continue to evolve along with technology, which means that the roles and expectations of IT specialists will change simultaneously,” predicts Mr. Wessel. “We have seen repetitive and time-consuming tasks become more and more automated over the last 40 years, and this will certainly continue to progress as AI becomes smarter and more able to integrate into app development and deployment processes.”

AI-based tools “make the complexity of coding more accessible”

Wessel is optimistic about the potential impact of this emerging technology. “Generic AI will trigger a wave of innovation, which will make it possible to remedy a large part of the skills shortages that we are facing,” he says.

“Generative AI offers an incredible tool to help developers do their jobs better,” he continues. “She can help with debugging. She can offer suggestions on how to approach a problem. All this helps developers save time and move on to more strategic thinking.”

IT professionals will continue to see their role enriched as they get closer to the needs of the company. “By breaking out of the monotony of daily automations, they will have more opportunity to partner with business leaders to take advantage of their creativity, their understanding of technology and their new ability to solve big problems,” explains Mr. Wessel.

AI-based tools, in essence, “make the complexity of coding more accessible. In this sense, it is a low code tool. This will allow computer scientists and data scientists to focus more on issues that require a thorough understanding of how systems work”.

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