It’s college graduation season, which means over 4 million seniors will graduate in the next few weeks, flooding the job market with new candidates. One area that has shown high potential for the right candidates is artificial intelligence and machine learning. Both disciplines are part of the larger data and analytics career path.
So, is that still a promising option for qualified new grads, and what are the prospects in AI? Also, what should undergrads and new grads, respectively, be doing to make sure they are the most attractive candidates for any new opportunities in these areas?
Early job market trends indicate that one in four U.S. tech jobs posted so far this year are seeking employees with artificial intelligence skills, according to a report published in the Wall Street Journal. Job postings for AI jobs have seen a substantial increase, with a 21% growth from 2018 to 2024, as reported by the WSJ. The World Economic Forum’s 2023 Future of Jobs Report predicts that the demand for machine learning (ML) specialists will rise by 40%. Machine learning engineers experienced a salary growth rate of 15% annually between 2019 and 2024.
It is undoubtedly the case that students equipped with AI are poised to take advantage of the opportunities for business transformation enabled by AI. But what exactly are AI skills, and more importantly, is there a difference in data analytics and predictive modeling skills that have been in demand for more than a decade, vs. familiarity with the current generation of AI models? Do the ever-enhancing capabilities of the current generation of AI models meaningfully alter the nature of skill-building and talent pipeline required for new college 2025 grads, even ones with data and analytics skills?
There is a distinction between AI and ML skills since AI also encompasses cognitive tasks like natural language understanding, computer vision, and problem-solving. Another difference between prior generations of predictive analytics and the current generation of AI models is the acceleration of capabilities of generative AI (GenAI), which can be defined as “artificial intelligence that can generate novel content, rather than simply analyzing or acting on existing data.” GenAI models could potentially aid a variety of tasks that require reasoning and creativity. We are also seeing sectoral differences in that the healthcare and retail sectors are leading the growth in AI job postings, with increases of 40% and 35%, respectively, according to the WSJ report. A report from the Federal Reserve Bank of Atlanta forecast that the balance has shifted more towards AI skills between 2016-2024 compared to past trends from 2010-2016.
Beyond the differences in the types of technical skills required in AI vs. ML jobs, a broader impact that graduates need to grapple with is what type of technical AI/ML skills are necessary in the long run. With the popularity of AI coding assistants such as Cursor, Windsurf, and Microsoft’s Copilot, there is the potential that GenAI could be used to augment worker efforts and increase productivity. Software developers can use GenAI to develop, test, and document code; improve data quality; and build user stories that articulate how a software feature will provide value. Studies have suggested that GenAI tools based on large language models (LLMs) could produce logically correct code from natural language prompts. Whether these tools will transform the productivity of AI/ML developers remains to be seen, but new 2025 grads could pay attention to how such tools might help augment their productivity.
We are also seeing a broader integration of large language models into a variety of applications, such as law and business. The Bureau of Labor Studies projects that on the one hand, “AI is well suited for the occupation’s tasks; on the other hand, increased productivity from the use of AI may lower prices and increase demand for software products, thus boosting employment demand for software developers. In addition, AI itself may lead to increased demand for software developers, who may be needed to develop AI-based business solutions and maintain AI systems.” There has been some preliminary evidence that GenAI use affects both developers’ coding quantity and quality. Some types of AI skills, such as prompt engineering, have prompted both enthusiasm as well as skepticism, where experts have either hailed the need for prompt engineering skills as a hiring criterion, while more recent reports dismiss the need for prompt engineering. With expectations about what types of AI/ML skills are valuable seemingly changing daily, new graduates should develop a portfolio of skills and technologies.
Another development that new job market entrants should be aware of is that AI/ML is increasingly interwoven with so many occupational functions. The 2023-2024 Census Bureau surveys indicate that generative AI use has a greater impact at the worker level rather than with overall employment levels at the firm level, with almost 27 percent of U.S. firms reporting the use of AI to perform tasks previously done by workers. With the widespread deployment of AI into a myriad of organizational functions, the use of AI-powered product development, and newer vulnerabilities created by GenAI systems such as jailbreaking, we need new job market entrants to be prepared to not just deploy AI/ML skills but also develop an awareness and critical thinking of how AI/ML alters the overall business landscape.