Right now, there are growing concerns about the impact of AI on the entry-level roles available to new graduates.
Recent research from Stanford University suggests there is a relative decline of 16% in employment for US workers aged 22–25 whose occupations are most exposed to AI, such as software developers and customer service representatives.
The So What
For universities working to ensure their degrees are relevant and valuable to students, there are two main areas to address:
- Enabling all students to achieve AI fluency.
- Aligning academic offerings with the realities of AI-enabled roles.
“It’s critical for universities to rethink how they are preparing their students for careers from their first day at university,” says Tejus Kothari, a managing director and partner who works with higher education institutions in the US.
“That means a systematic approach to embedding AI skills across all subjects, and increasing hands-on experience of skills that can’t be assessed by traditional exams. These include teamwork, navigating conflicts, managing multiple stakeholders, and workplace ethics.”
When it comes to embedding AI skills, there are some universities that are managing to do this at scale. Ohio State University, for example, has launched an AI Fluency initiative that embeds AI education and innovation into the core of every undergraduate curriculum—regardless of major. But in many other universities, piecemeal offerings and certificates are still the norm.
As well as overhauling the academic offering, students will also need to gain real-world experience of work. More universities will need a proactive strategy to ensure internships and work placements are part of the university’s core offering and business case, rather than a nice-to-have extra.
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Some universities are already doing this:
In Boston, for example, Northeastern University has a cooperative education program where students alternate between periods of academic study and work experience in their fields of interest. More than 90% of students complete a co-op.
“With the rapid pace of change, the onus is also on students to share the insights they are gathering during those work placements,” says Nithya Vaduganathan, a BCG managing director and senior partner based in Boston.
“This will create a faster feedback loop so that academic curricula can better respond to the needs of workplaces— whether that’s the tech itself, or human capabilities such as redesigning workflows or supporting upskilling pathways.”
Universities can also respond to their local context by sourcing the data that highlights the specific skills that employers need in their town or region, and then map their needs against student skill sets.
In Indiana, for example, Purdue University has launched a scheme to pair technical students with businesses who want to get more value from their data sets. Organizations can submit data-intensive projects while committing to mentoring the students. By offering students the chance to help solve real-world problems, the university is also creating a pipeline of data-fluent graduates who will have expertise from their first day at work.
When looking to find more opportunities for students, small and medium-sized enterprises may be of particular interest, Kothari notes, as they may have specific needs but lack the scale to invest in AI capabilities and training. They are also less likely to have structured internship programs. For universities, the key is to find frictionless ways to partner with such companies.
Closer partnerships between employers and universities have been talked about for many years. However, it is often difficult to align two organizations with separate KPIs, funding mechanisms, and ways of working, notes Anton Stepanenko, a BCG partner who specializes in higher education and skilling.
“Now is the time to pull out all the stops and ensure these partnerships live up to their potential. AI is accelerating the world of work. And universities need to make sure the partnerships they form are developing equally fast,” he says.
Now What
Leadership commitment and conviction. The scale of change that is needed requires more than pilot schemes or piecemeal initiatives. It requires commitment and investment from leadership to make systemic change and challenge academic leaders to ensure the curriculum is adapted, AI learning tools are embedded, and the student experience is prioritized. Leaders will also have to find ways to overcome bottlenecks, including a lack of financing, the lack of capability, and a lack of upskilling within the faculty. They will also need to speed up the decision-making process and become more nimble. That means quickly testing new ideas and embedding the learnings into processes and workflows.
Revamp the careers function. The mandate and scope of careers services should be overhauled: Rather than offering students advice on career choices after graduating,careers services should focus on internships and development throughout a student’s time at university. To ensure new opportunities are embraced, students should be actively involved in planning these opportunities. AI can become an enabler here, for example, by simulating workplace scenarios for students so they get a deeper understanding of their own skills and preferences.
Use the potential of AI agents to accelerate change. AI tutors could create personalized learning pathways for students working on improving their AI skills, while AI can also help create and refine parts of the curriculum.AI agents can also be used to scan employment data and infer employer demand, mapping course catalogs and completion data to labor market data. This could then help create funding narratives for universities to apply for new programs or partnerships.
Track progress. To drive lasting change, it will be important to create the right mechanisms to measure progress, including real-time data on the number of students who are gaining workplace experience. This will enable universities to attract investors, partners, and students.
