In one recent survey, 88 percent of organizations say they're using AI across their businesses, but only about a third have moved it beyond the pilot stage. About 86 percent of high-growth manufacturers are accelerating their investments in AI, but the majority say their frontline leaders aren't ready to actually manage the change.
In my role as dean of the School of Engineering Technology, at one of the largest technical training programs in Virginia, I regularly hear from our manufacturing partners about their priorities. Their need is clear: they want graduates who can do more than simply use AI tools. They need graduates who apply the technology in novel and practical ways.
They are seeking to understand what AI can actually do on the factory floor and what kind of skills their workforce might need to make the technology useful in practice, including the critical-thinking skills to evaluate and question what AI actually gives them.
Institutions, meanwhile, are moving quickly to build AI into their programs, with some even adding AI graduation requirements.
But many of these efforts focus on giving students only a basic familiarity with new and emerging platforms, with only one-quarter of college seniors saying AI has been meaningfully integrated into their programs.
If institutions want their graduates to stand out, they will have to go further. Meeting a baseline of AI literacy is not enough. Higher education should prepare students to enter the workforce not just ready to use AI, but able to demonstrate to employers what the technology can do for them.
As AI transforms the workforce and automates roles that were once the domain of junior employees, students who graduate with only the basics will find themselves competing for fewer and fewer openings.
Entry-level job postings are down at least 30 percent since January 2024, and entry-level hiring at the 15 biggest tech firms fell 25 percent from 2023 to 2024. According to the World Economic Forum, about 40 percent of employers plan on reducing staff where AI can automate tasks. Yet a Tufts University analysis finds that physical and variable-condition work, the kind done on factory floors, faces far less displacement risk than white-collar roles, making technically trained graduates all the more valuable."
Colleges that want their graduates to stand out will need to cultivate deep partnerships with employers and industry leaders, treating those relationships as a shared investment in shaping what comes next and to align training with the evolving needs of the field.
Employers should be at the table, not as occasional guests, but as continuous co-designers of the educational experience. That means assembling structured advisory boards in which hiring managers and subject-matter experts regularly review curriculum, certifications, and lab technologies.
It also means developing registered apprenticeship programs and other work-based learning opportunities that pair classroom instruction with paid on-the-job training—allowing students to learn in the same kinds of environments where they will soon work.
But listening to employers, while essential, is not sufficient on its own. Half of manufacturers say they struggle to identify the right technology that could benefit their company, and nearly 40 percent say they lack the internal expertise to fully implement the technology they already have.
This is where colleges and universities can help their graduates stand out the most.
Students already have a generational advantage. Pew Research shows that younger workers are, unsurprisingly, more likely to use AI in their jobs. Randstad, meanwhile, has found that over half of Gen Z workers already regularly use AI to problem-solve at work. The appetite for learning with and about this technology is there. Students just need more structured opportunities to turn that interest into a professional advantage. Institutions should embed AI across the curriculum in a meaningful and appropriate way.
They can weave AI into coursework across programs so that students encounter applied uses such as predictive modeling, anomaly detection, and workload optimization as a routine part of their education, no matter the subject area.
Faculty with industry certifications in areas such as deep learning and predictive maintenance can bring that instruction to life in ways that learning how to use general-purpose AI cannot.
A graduate who can maintain a robotic system and one who can also evaluate whether an AI-driven predictive maintenance tool could reduce downtime are fundamentally different hires. The companies that figure out how to quickly deploy AI strategically will gain a decisive advantage in the years ahead. If higher education does its job, our graduates should be the ones helping them get there.
Dr. Wael Ibrahim is the Dean of the School of Engineering Technology ECPI University.
