It's university graduation season. This means that over 4 million seniors will graduate in the coming weeks, allowing new candidates to flood the job market. One area that has shown high potential for the right candidate is artificial intelligence and machine learning. Both areas are part of a larger data and analytics career path.
So, it's still a promising option for qualified new alumni, and what is the outlook for AI? Also, what should undergraduate and new alumni do to ensure that each is the most attractive candidates for new opportunities in these fields?
Early job market trends show that one in four US high-tech jobs posted so far this year are seeking employees with artificial intelligence skills, Wall Street Journal. AI Job Job Job vacancies have increased significantly, with a 21% growth rate from 2018 to 2024. WSJ. The World Economic Forum's 2023 Future of Jobs Report predicts machine learning demand (ML) experts will rise by 40%. Machine learning engineers experienced a salary growth rate of 15% per year between 2019 and 2024.
There is no doubt that AI-equipped students are poised to take advantage of the business transformation opportunities that AI has made possible. But what are AI skills, and more importantly, data analysis and predictive modeling skills have been in demand for over a decade, and are there any differences that they are familiar with current generations of AI models? Will the constant capabilities of current generation AI models change the nature of the skill-building and talent pipeline needed for new university 2025 graduates into meaning, even those with data and analytical skills?
There is a distinction between AI and ML skills, as AI also includes cognitive tasks such as understanding natural language, computer vision, and problem solving. Another difference between the previous generation of predictive analytics and the current generation of AI models is the acceleration of the capabilities of Generator AI (Genai). This can be defined as “artificial intelligence that can generate new content rather than simply analyzing or interacting with existing data.” The genai model can assist in a variety of tasks that require inference and creativity. Additionally, a report by WSJ shows sector differences in healthcare and retail sectors are increasing AI job growth by 40% and 35%, respectively. A report from the Federal Reserve Bank of Atlanta predicts that balances have shifted to AI skills between 2016 and 2024 compared to past trends from 2010 to 2016.
Beyond the differences in the types of technical skills required for AI vs. ML jobs, the broader impact alumni need to tackle is what types of technical AI/ML skills are needed in the long run. With the popularity of AI coding assistants such as Cursor, Windsurf and Microsoft's Copilot, Genai could potentially be used to increase worker effort and increase productivity. Software developers can use Genai to develop, test, and document code. Improves data quality. Create user stories that clarify how software features provide value. Research suggests that genai tools based on large-scale language models (LLM) can generate logically correct code from natural language prompts. It remains to be seen whether these tools will change the productivity of AI/ML developers, but new 2025 students will be able to pay attention to how such tools can help them increase productivity.
It also shows that large-scale language models, such as law and business, are integrated widely into a wide range of applications. The Bureau of Labor Research states, “AI is suitable for occupational tasks. On the other hand, it predicts that increased productivity through the use of AI could increase the demand for software products and increase the employment demand for software developers. There was some preliminary evidence that the use of Genai affects both the amount and quality of developers. Some types of AI skills, such as rapid engineering, welcome the need for rapid engineering skills as an employment standard, encouraged both skepticism and more recent reports dismissed the need for rapid engineering. As new graduates appear to be changing every day depending on expectations about which types of AI/ML skills are valuable or not, new graduates need to develop a portfolio of skills and technology.
Another development new job market entrants should know is that AI/ML is increasingly woven into so many occupational functions. A survey by the 2023-2024 Census Bureau shows that generative AI use has a significant impact at the workforce level rather than at the overall employment level at the enterprise level. Almost 27% of US companies report using AI to perform tasks previously performed by workers. With the myriad organizational capabilities of AI, the use of AI-driven product development, and the widespread deployment into new vulnerabilities created by GENAI systems such as Jailbreak, not only will AI/ML skills be deployed, but we must be prepared to develop critical thinking about how AI/ML operates business landscapes.
