You cannot force companies to post more internships. That supply problem is structural and it is not going to reverse on your timeline. What you can control is how prepared your students are when they compete for the postings that do exist.
The institutions that figure this out are not waiting for the market to open back up. They are equipping students with AI credentials, AI-built portfolio work, and demonstrated tool fluency that employers now use as a direct signal. It is not a workaround. It is the actual differentiator in a compressed entry-level market.
The Core Argument
The internship shortage is real and structural. Post-2022 hiring contractions removed early-career roles that have not fully returned. Students cannot solve this by applying harder. But they can close the experience gap with AI credentials, AI-built portfolio artifacts, and mock-interview volume, all of which are things institutions can systematically deliver. With a national advisor-to-student ratio of 1:1,568, this only works at scale if an AI platform is doing the proactive reach.
The Internship Supply Problem Is Real
After 2022, companies pulled back hard on early-career hiring. The reasons were layered: over-hiring corrections, rising interest rates, and automation absorbing the work that used to go to interns and entry-level employees. Some of that early-career pipeline has come back. A lot of it has not. The result is a structural internship bottleneck that is not going to reverse on any institution's timeline.
The effect for students: a smaller pool of postings, more applicants per opening, and employers who now screen for prior experience even for first internship roles. Students applying with no experience are competing against students with one or two prior internships. The bar moved without the pathway moving with it.
This is not a problem career services can solve by posting more job board links or running more employer panels. Those are distribution efforts. The underlying issue is that students are showing up underprepared relative to what the compressed market now expects.
What Companies Actually Respond to Now
When employers screen early-career candidates with no formal work history, they are looking for three things: demonstrated skill, proof of initiative, and evidence that the person can operate in a modern work environment.
AI fluency now reads as all three. A student who can show that they built a portfolio project using AI tools, completed an AI certification from a credible provider, and can speak to how they use AI in their actual workflow is demonstrating exactly what hiring managers are looking for. It is initiative. It is skill signal. It is proof they can operate in an environment where AI is already part of the job.
Prior experience on a resume used to serve this function: it told employers the candidate could produce in a real work context. AI credentials and AI-augmented portfolio work now serve the same function for students who do not have prior experience to show.
Old Differentiators
- Prior internship experience
- Campus leadership roles
- GPA signals
- School brand name
- Generic coursework on resume
What Moves the Needle Now
- AI certifications (Google, Coursera, Microsoft)
- Portfolio projects built with AI tools
- Demonstrated AI workflow fluency
- Mock-interview-tested confidence
- ATS-optimized, keyword-matched resume
AI Credentials as the Gap-Filler
The fastest-growing category of early-career differentiation is AI credentials: certificates and portfolio signals that prove a student can use AI tools in a professional context.
Certifications from credible providers
Google AI Essentials, Coursera's AI for Everyone, and Microsoft AI fundamentals certifications are now appearing on entry-level job postings as preferred qualifications. They are free or low-cost, completable in days, and they show up as a recognizable signal to recruiters scanning resumes quickly. For a student with no work experience, this is a concrete line item that distinguishes their resume from the stack.
Built-with-AI portfolio projects
A student who has used AI to build a market analysis, create a financial model, or produce a documented research report has something to show and discuss in an interview. The project itself demonstrates skill. The AI workflow behind it demonstrates adaptability. Together they close the "but do you have experience" objection without requiring an employer to have posted a prior role.
Mock interview volume
Students who practice interviews answer confidently. Students who have not practiced do not. An advisor running 1:1 sessions can get a student through one or two mock interviews per semester. AI removes that ceiling entirely. Students who run 10 or 15 sessions show up to real interviews with a measurable edge. The fluency is visible immediately.
"You can AI-maxx your students to get them hired. That is what the institutions actually closing the gap are doing."
-- Rod Danan, CEO, Prentus
The Institution's Role: Scale What Advisors Cannot
The national advisor-to-student ratio is 1:1,568. That number makes proactive outreach to every student mathematically impossible.
The career services teams doing the best work know this. They have moved away from the idea that they can personally reach every student before graduation. Instead, they are using AI platforms to push credential guidance, portfolio project prompts, and mock interview reps to students who would never walk into the office. They are doing it at the start of the semester, not the week before graduation.
This is where the 10-20% utilization stat becomes the real problem. Only 10-20% of students use career services at most institutions. The other 80% graduate without meaningful preparation. An AI platform that reaches every student from enrollment does not just improve that number. It changes the category entirely.
- 1
Deploy AI credential guidance from orientation, not senior year
A student who starts building AI credentials in their first semester has two years of portfolio work by the time they apply. A student who hears about it during spring of junior year has a week.
- 2
Make AI mock interviews default, not optional
Optional programs serve self-selected students. Default access reaches the full population. The students who most need practice are the least likely to opt in voluntarily.
- 3
Track outcomes, not just engagement
The metric that matters is time-to-hire and placement rate, not session counts. Institutions need platforms that track both, so advisors can see what is actually working and intervene when a student stalls.
How Prentus Helps Institutions Close the Gap
Prentus is a career outcomes platform built for the exact problem described above: too many students, too few advisors, too little time before graduation. The platform gives every student AI coaching, mock interview practice, job search tools, and outcome tracking from day one.
The results across institutions like DeVry, which runs 30,000 students and 50,000 alumni on the platform:
54%
faster time-to-hire
3x
student engagement vs. traditional portals
50%+
student activation rate
80%
reduction in advisor admin time
The 80% admin time reduction is the piece that matters most for institution leaders thinking about scale. When advisors stop spending most of their hours on resume formatting and job search logistics, they can focus on the conversations that actually require a person: major changes, mental health, career pivots. The AI layer does not replace advisors. It frees them for the work only they can do.
87% of students say they want help mapping their career paths (Strada). Most of them never get it because the ratio makes it impossible. That is the gap Prentus closes.
If you want to see how this works for your specific institution, let's set up a conversation.
