How AI is Changing School Selection in 2026

The skillsets employers value are rapidly changing. AI fluency is raising the baseline, with candidates now expected not only to know how to prompt, but also how to delegate, evaluate, and understand the consequences of a model’s outputs. 

Nowhere is this clearer than in Early Careers. Junior AI-exposed roles are now seven times more likely than other junior roles to demand skills traditionally associated with senior-level roles like judgement and leadership. With this in mind, how do we guarantee that our school selection strategy matches the reality of the labor market?

Schools are starting to change how they teach to integrate AI skills.

Universities see this new demand and are pivoting curricula accordingly. There are two main approaches they’re taking.

The first is establishing AI-enabled majors. From 2024 to 2025, AI-related Bachelor’s programs grew by an astounding 119%. Many are in computer science and tech, but they span a variety of fields with new majors like UPenn’s BS in Artificial Intelligence for Business or the University of Buffalo’s BS in AI and Geospatial Analytics.

The second is the integration of AI across all majors. Through AI policies, committees, workshops, and course requirements, universities can ensure that students are exposed to AI regardless of their field. Some leaders have committed to formal university-wide plans to place AI across the curriculum, such as the University of Florida’s plan to embed AI literacy and applied skills in all undergraduate programs or Purdue’s new policy that students must show AI working competency in order to graduate.

While the changes we’re seeing universities make are promising, they’re not keeping pace with what employers need.

Curriculum changes filter through many layers of bureaucracy before they materialize, and even then, syllabi change from semester to semester and professor to professor. This is illustrated in the rise of new AI-related majors–despite the rapid increase seen over the last few years, only about a third of the U.S. News Top 100 National Universities offer one at the Bachelor’s level. The plans outlined above to integrate AI across majors will also move slowly–the University of Florida hopes to reach its goal by 2029, while Purdue’s AI requirement to graduate will only begin with the Class of 2030.

At most schools, AI skills are only formally taught in broad terms through optional workshops (e.g., WashU) and first-year GenEd courses (e.g., Iowa State’s first-year Introduction to Research course). Deeper engagement requires students to actively opt in through elective coursework.

While many top computer science programs have been quicker to pivot, recruiting on these campuses has become highly competitive as employers across the country seek to attract AI-ready employees. Campuses like Carnegie Mellon University, where Computer Science students are required to complete Artificial Intelligence electives, are dominated by high-prestige employer brands and compensation. The school’s 2025 graduating class of computer science students, whose top employers were Amazon, Meta, and Google, earned a median of $140,000/year in their first job out of college, with a sixth of these students reporting salaries of $200,000 or greater.

Where, then, do employers find AI-ready talent?

Traditional school selection–built on relevant majors, degree completions data, and prestige rankings–wasn’t designed for a market that is changing so rapidly. Instead, employers must go granular.

Because AI has yet to be standardized across institutional systems, the key question is one of finding communities and spaces where students exhibit personal interest in AI, especially if that interest is shown via AI’s application in projects.

When finding students interested in researching AI itself, university AI institutes are a key starting point. Nearly every major university has established some form of AI research hub, with some even having individual hubs for specific topics, like Purdue’s Supply Chain AI Consortium. These hubs serve as a key way to find students who are deeply interested in cutting-edge academic research initiatives around AI.

Competitive events are another useful way to reach technical talent that is enthusiastic about applying AI skills. Events like UC Berkeley’s AI Hackathon gather hundreds of students to complete projects solving example challenges with AI and other related skills.

Both of these sources, though, primarily gather students with tech and engineering backgrounds–what about students in business and other fields? This is where student clubs prove invaluable.

On-campus student organizations are critical to any precision sourcing strategy because they allow you to identify not only specific skills you’re seeking, but also the context in which they’re being applied. Organizations combine cutting-edge tech skills with a broad range of student interests, from business to healthcare to aerospace engineering. 

By evaluating schools granularly, hiring teams ensure school lists include the niche talent types demanded by the business.

Veris Insight’s AI TalentScout is helping hundreds of Fortune 1000 early careers teams engage technical talent communities. By searching using a taxonomy of tech skills and the fields in which students are applying them, recruiting teams can instantly generate a list of communities that are innovating across tech skills, including AI/ML, blockchain, cloud engineering, and more, at over 100 top U.S. universities.

When the time comes to audit your school lists, pulling a list of these communities across high-priority skills allows recruiting teams to evaluate whether students are genuinely excited in organizing around and applying AI.

What This Means for Early Careers Teams 

With tech skills expanding rapidly and business leaders raising the bar for basic technical proficiency, school strategies have to evolve to keep pace with the market. Degree offerings and rankings are lagging indicators. The employers who will win in today’s talent market are the ones looking where others aren’t, like student organizations, project-based labs, and hackathons, where students are already building with AI.