A job ad appeared recently for an “AI competency leader”, which was a role that involved, “collaborating closely with cross-functional teams to develop and execute strategies that leverage generative artificial intelligence techniques across various domains.”
These kinds of adverts — for job roles that were unheard of even a year ago — are likely to become the norm in the AI era. While everyone in business wants to make the most of AI, it’s going to take more than development or data science skills to make the most of emerging technology. There’s a raft of responsibilities that are essential to AI efforts, from training algorithms to overseeing ethics.
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There are two levels of AI positions becoming apparent, says Robert Ghrist, associate dean for undergraduate education at the University of Pennsylvania School of Engineering and Applied Sciences. “The first is what you might call AI specialist, someone who is broadly trained in AI from machine learning to neural nets, large language models, and more,” he explains.
The second category of AI jobs are more closely fused to broad-based business and managerial roles. “This is a more interesting class of jobs in the form of ‘AI plus X’, where ‘X’ is a variable such as law, medicine, or education,” Ghrist continues. “These will be more abundant yet harder to fill, requiring core expertise plus AI implementation skills.”
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Prompt engineering is also seen as a hot new job in the AI era. However, its long-term future as a professional pursuit is uncertain, says Tony Lee, CTO at Hyperscience: “I see it as a skill and expertise that is valuable and distinct. Is it a full-time job though? I’ll leave that to the hiring company to decide.”
While prompt-engineering skills are in demand now, Lee says the future might look different: “It’s a new way to interface with a computer that requires different skills. But as the interface becomes more conversational and more human-like, it remains to be seen if this is a new career path or just a point-in-time opportunity.”
Looking deep into the future — let’s say a year or two in internet time — new roles focused on AI application adoption and management might come to the fore. These roles include positions such as, “AI trainers, AI auditors and AI ethicists,” says Nick Magnuson, head of AI for Qlik.
“These roles really focus on the heart of AI — its data — while helping ensure the ethical use of the technology. AI trainers prepare and adjust the technology models, while AI auditors and AI ethicists ensure an organization’s data is not only accurate and trusted, but also reinforce the integrity of the AI and scale it across the business.”
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However, it’s also important to consideration how AI is overtaking many of the lower-level tasks associated with IT development and management. Interestingly, Ghrist says that trend should be welcomed. “Nobody likes eliminating jobs, but AI usurping low-level tasks is good news. I both believe and hope that many tasks will be made obsolete by AI, starting with the most tedious, repetitive, and low-level,” he says. “Examples include low-level coding, updating legacy code, and implementing SDKs.”
Early in his career, Ghrist “worked in a magnetic-tape library for a mainframe computer and I am so happy that job no longer exists,” he recounts. “Now it’s done a billion times faster by a one-ounce, $15 flash drive.”
What’s already clear is that AI is poised to ease and automate a range of development tasks while still creating fresh opportunities for human talent. “Software engineering has gone from where developers wrote code from scratch, to the Stack Overflow era, and now to full AI-generated code,” says Lee. “Yet during this journey, the demand for top talent has only grown. I do not expect this demand to decrease, even as AI takes on more of the mechanical work.”
A critical, in-demand area is “skilled workers who can analyze data and train LLMs,” says Lee. “As more technical tasks are automated, the demand for human oversight in training data will be extremely relevant to ensure the technology can continue to complete complex tasks.”
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Areas where managerial skills “will continue to shine and add value are around tasks dealing with ambiguity and supervising AI, creative tasks that require intuition and context, and roles that require cross team collaboration,” Lee adds.
Magnuson says it’s important to note how effective AI deployments require a range of skills that typically aren’t held by a single person. “Finding a capable head of AI that has both technical prowess and creative experience is crucial,” he says. “This leader will be able to assemble an AI team that checks all the boxes, and typically includes data scientists and machine-learning engineers that work alongside legal, IT and HR teams.”
Lee says an example of such inter-disciplinary collaboration might involve, “a front-end engineer sitting with a designer and a product manager to solve a usability problem. This is a challenge for AI today, as the human aspect of the usability problem is still best understood and solved by other humans.”
However, there’s no room for complacency. Ghrist says professionals should recognize there are no skills “for which we have an exclusive monopoly.” He continues: “AI will be able to augment all hard and soft skills in tech — no exceptions. Co-evolution is the key: we work together and adapt. As such, the most valuable skill is adaptability.”
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Still, certain foundational skills will remain exactly that — foundational. “Skills — from mathematics and computer science — will always be relevant as a precursor to specialized AI knowledge,” says Ghrist. “Coding will always be important, not because you will be coding, but because you will be managing a team of AI coders, and, like any good manager, you need to know enough to guide the team.”
Anything associated with math and computer science, “supercharges all other technical work, now and to come,” says Ghrist. Along with foundational capabilities, soft skills such as “communication, empathy, creativity, ambition, and more are increasingly of value.”
Professionals looking to advance in their careers should seek out courses, training programs, or focus on areas that incorporate AI skills. “I would encourage all professionals to gain a deeper understanding of the fundamentals of AI, including machine learning, deep learning, and natural language processing,” says Magnuson. “Learning about AI and how it works is important for everybody — not just technical people — akin to how the internet is something we all have to understand.”
Ghrist advises professionals to focus on math and computer science, as “without them the rest is a black box of action without understanding.” A second learning priority must be “the soft skill of adaptability,” he continues. “As AI tech increases super-linearly, the most difficult thing for most companies is going to be how to keep up. The best way for a professional to stay current is to have a well-curated social media feed — ignore the politics. Search for updates on the state of the art.”
As Ghrist concludes: “‘More math and more Twitter’ is eccentric advice, but we are in strange times.”