A job advertisement for an “AI Competency Leader” was recently posted. This was a role that would “work closely with cross-functional teams to develop and execute strategies that leverage generative artificial intelligence technologies across a variety of disciplines.”
These types of ads for jobs that were unheard of a year ago could become the norm in the AI era. Everyone in business wants to get the most out of AI, but getting the most out of emerging technology requires more than just development and data science skills. From training algorithms to overseeing ethics, there are many responsibilities that are essential to AI efforts.
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Robert Grist, associate dean for undergraduate education in the University of Pennsylvania's College of Engineering and Applied Sciences, said it's become clear that there are two levels of AI positions. “The first one is what we call an AI specialist, someone who has extensive training in AI, from machine learning to neural nets to large-scale language models, etc.,” he explains.
The second category of AI jobs is more closely integrated with a wide range of business and managerial roles. “This is a more interesting kind of job where you have ‘AI plus X,’ where ‘X’ is a variable like law, medicine, education, etc.,” Grist continues. “These will be richer, but harder to fill, and will require core expertise and AI implementation skills.”
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Prompt engineering is also considered a hot new job in the AI era. But its long-term future as a professional pursuit is uncertain, said Tony Lee, chief technology officer at HyperScience. That is for the hiring company to decide. ”
While agile engineering skills are in demand today, Lee says the future may look different. “This is a new way of interfacing with computers that requires different skills. But it remains the same, as interfaces become more conversational and more human-like. Is this a new career path or is it just a new way of interfacing with computers? You have to figure out if it’s an opportunity at a particular point in time.”
As we look deeper into the future (let's say 1-2 years of internet time), new roles focused on deploying and managing AI applications are likely to emerge. These roles include positions such as “AI trainer, AI auditor, and AI ethicist,” said Nick Magnuson, his head of AI at Qlik.
“These roles focus on data, which is at the heart of AI, while ensuring the ethical use of technology. AI trainers prepare and calibrate technology models, while AI auditors and AI Ethicists ensure that your organization's data is not only accurate, but also reliable and can be extended across your business, strengthening the integrity of your AI. ”
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However, it is also important to consider how AI is replacing many of the lower-level tasks associated with IT development and management. Interestingly, Grist says this trend is welcome. “No one likes to eliminate jobs, but AI taking over low-level tasks is good news. Starting with the most boring, repetitive, and low-level tasks, I believe that AI will I believe and hope it will be abolished,” he says. “Examples include low-level coding, updating legacy code, and implementing SDKs.”
Early in his career, Grist “worked in a magnetic tape library on a mainframe computer, and I'm very happy to no longer have that job,” he recalls. “Now it runs a billion times faster with a one-ounce, $15 flash drive.”
What is already clear is that AI is poised to simplify and automate a variety of development tasks while creating new opportunities for human talent. “Software engineering has moved from the days of developers writing code from scratch to the days of Stack Overflow and now completely to code generated by AI,” he says. “However, during this journey, the demand for talented people will only increase. We do not expect this demand to diminish even as AI takes over more mechanical tasks.”
An important and in-demand sector is “skilled workers who can analyze data and train LLMs,” Lee says. “As more technical tasks become automated, the demand for human oversight of training data becomes critical to ensuring that technology continues to complete complex tasks.”
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Areas where management skills “will continue to shine and add value include tasks such as dealing with ambiguity and monitoring AI, creative tasks that require intuition and context, and roles that require collaboration across teams.” around,” Lee added.
Magnuson says it's important to recognize how effectively deploying AI requires a range of skills that no one person can typically possess. “It’s important to find a capable AI lead with both technical ability and creative experience,” he says. “This leader can assemble an AI team that checks all the boxes. He typically includes data scientists and machine learning engineers who work in conjunction with legal, IT, and human resources teams.”
An example of such cross-disciplinary collaboration, says Lee, is “front-end engineers collaborating with designers and product managers to solve usability problems. This is a challenge for AI today because it matters.” Yet it can best be understood and resolved by other humans. ”
But there is no room for complacency. Grist said professionals should recognize that there are no skills that “we have a monopoly on.” He added: “AI will be able to enhance all hard and soft skills in technology. There are no exceptions. The key is co-evolution. We will work together and adapt. One skill is adaptability.”
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Still, certain fundamental skills remain just that: fundamental. “His skills in mathematics and computer science will always be relevant as a pioneer in specialized AI knowledge,” Grist says. “Coding is always important, not because you code, but because you manage a team of AI programmers. Like any good manager, you need to know enough to guide your team.”
Anything related to mathematics and computer science “significantly enhances all other technical work, both now and in the future,” Grist says. In addition to basic competencies, soft skills such as “communication, empathy, creativity, ambition, etc.” are becoming increasingly valuable.
Professionals looking to advance their careers should look for courses and training programs or focus on areas that incorporate AI skills. “We encourage all professionals to gain a deeper understanding of the fundamentals of AI, including machine learning, deep learning, and natural language processing,” says Magnusson. “Learning about AI and how it works is important for everyone, not just technologists, just like the internet is something we all need to understand.”
Grist advises professionals to focus on mathematics and computer science. “Without them, the rest becomes a black box of incomprehensible behavior.” His second priority for learning, he continues, must be “the soft skill of adaptability.” “As AI technology increases hyper-linearly, the most difficult question for most businesses will be how to keep up.The best way for professionals to stay up to date is to , ignore politics and have a well-curated social media feed.'' Find the latest news. ”
Grist concludes: “'More math, more Twitter' is quaint advice, but we're in strange times.”