Scaffold AI Literacy, Boost Student Employability

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• Entry-level hiring is structurally changing as AI assumes many junior tasks, making it harder for new graduates to gain early career experience.
• Two types of occupations are being affected differently: Growth jobs require students to demonstrate higher-level competence earlier, while mastery jobs require them to use AI to accelerate their learning.
• When business schools address AI literacy throughout their programs, they enable students to develop good judgment, gain skills, and demonstrate the AI proficiency that employers now expect.

 
Generative AI (GenAI) has not eliminated many jobs outright, but it has structurally shifted entry-level hiring. Large language models (LLMs) now excel at junior tasks such as conducting preliminary research, composing first drafts, performing basic analysis, and writing professional communications. Consequently, the early rungs of many professional career ladders are disappearing, jeopardizing the employability of new college graduates on a global scale.

While employers still require human judgment and expertise, they are hiring fewer newcomers into the apprentice-level positions that used to develop these areas. This strategy may be shortsighted, because it makes it more difficult for employers to fill their talent pipelines over the long term. But it also presents a growing challenge to entry-level job seekers now.

To get hired, college students need to learn how to use AI earlier. As more business schools integrate career readiness earlier into their curricula, they’re finding that AI literacy naturally fits beginning in Year 1.

When schools scaffold learning objectives related to AI literacy throughout the undergraduate degree path, students can build employer-ready portfolios and artifacts as they achieve learning milestones. Students and alumni gain not only a competitive hiring advantage, but also the ability to adapt to the AI-impacted job market as it continues to evolve.

Disruption to the Learning Curve

Researchers from the nonprofit think tank Burning Glass Institute (BGI) and the Harvard Business School’s Managing the Future of Work project have identified how AI reshapes learning curves differently, depending on the job role. For their July 2025 report “The Expertise Upheaval,” researchers used data from labor market analytics firm Lightcast to analyze 99 percent of U.S. job openings from the past two years.

Their analysis identifies two archetypes of knowledge occupations, each with its own learning curve when it comes to AI. One requires growth over time, and the other requires more immediate mastery of relevant skills and knowledge.

AI can compress time-to-baseline proficiency in more complex domains, so that newer employees often apply skills similar to the skills of their more senior, experienced colleagues.

Workers in growth roles (such as marketing specialists and project managers) build expertise progressively as they assume “more nuanced and judgment-heavy responsibilities.” Unfortunately, their junior, generalist tasks increasingly overlap with LLM strengths. As a result, entry points are narrowing as teams “flatten,” while less experienced workers lack the benefit of on-the-job collaboration and exposure to “tricks of the trade.”

Workers in mastery roles (such accountants, underwriters, and business information technology professionals) require more front‑loaded knowledge and “mastery of a body of codified technical knowledge at the start.” For these individuals, AI can compress time-to-baseline proficiency in more complex domains. After a sharp, steep beginning, this learning curve flattens out. Newer employees often apply skills similar to the skills of their more senior, experienced colleagues.

Differences Between Growth and Mastery Roles

As employers adopt skills-based hiring, four-year degrees may no longer be required for mastery roles due to the compressed time required to train these workers, depending on occupation and industry. In contrast, growth roles are 51 percent more likely to require college degrees than mastery roles, based on the Lightcast job openings data.

Below are the top ten occupations in the U.S. (by total U.S. employment) where more than 50 percent of job postings require bachelor’s degrees, as outlined in the BGI report. This analysis excluded occupations that have licensing or specialized degree requirements:

As the above table shows, entry-level hiring for business school graduates—in fields such as project management, marketing, finance, and accounting—is particularly affected. Most employers are no longer willing to invest as much in entry-level training in these areas as they once did because, frankly, they don’t have to. They can rely on AI tools and more experienced lateral hires.,

AI Literacy for Different Career Paths

Because the first career ladder rungs are disappearing for growth roles, students have to develop career-ready AI literacy from co-curricular activities, class projects, and internships. Only then will they be able to prove to employers that they can operate at the next, higher rung of the career ladder immediately after graduation. 

In mastery roles, employers will expect students to demonstrate that they can use AI to acquire and augment domain knowledge quickly. That means students will need to provide concrete examples of problem-solving, critical thinking, and data visualization to show that no matter what their majors, they can apply AI skills to achieve basic proficiency quickly in any role.

Students have to prove to employers that they can operate at the next, higher rung of the career ladder immediately after graduation.

For example, with the right skill portfolio, a general business administration major can qualify to apply to be a data warehousing analyst. In the past, that position would have required a computer science or technical degree.

The takeaway for business faculty: We must know how GenAI is changing the learning curve and hiring patterns in different professional careers. Only then can we prepare today’s students to be career-ready at graduation.

Changes Are Structural, Not Cyclical

BGI analyzed longitudinal Lightcast data on millions of job openings, so we know its findings do not represent a temporary trend. “No Country for Young Grads,” another BGI report released in July 2025, indicates that the number of growth job postings requiring fewer than three years’ experience has fallen since 2022, even as demand for senior talent holds steady or grows.

The steepest declines in job openings are occurring where roles are highly exposed to LLMs. Unemployment and layoffs have risen fastest for 22- to 27-year-old degree holders in business, finance, and technology. These are signs of structural, not cyclical, change.

Data does show limits to AI’s displacement of human work. Humans still prefer working with humans—at least when it comes to certain tasks. In its 2025 Automation/AI Survey, the Society for Human Resource Management (SHRM) finds that most jobs—both blue- and white-collar—cannot be fully automated or replaced by generative AI.

Based on responses from more than 20,000 workers, SHRM estimates that 15.1 percent of all jobs in the U.S. have already had at least 50 percent of their associated tasks automated, and 7.8 percent of jobs now have at least 50 percent of their associated tasks done using GenAI.

Despite this trend, 63.3 percent of jobs face nontechnical barriers to full automation. The largest barrier is client preference for human interaction. For instance, while we see ads for “robo-advisors” to handle investors’ retirement savings, most people trust and prefer to work with human financial advisors.

According to SHRM’s employer research and playbooks, the hiring message across the entire job market is consistent: Organizations are redesigning work at the task level, and early‑career hires must meet employers’ new expectations from their first day on the job.

Related Global Trends

These structural shifts in early-career hiring and demand for student AI literacy extend far beyond the United States. Global employment data collected in 2024–2025 indicates that learning curve disruption is similar around the world. That said, the level of disruption varies based on how a country’s occupations are distributed and how quickly government regulations allow employers to pivot their hiring.

In Europe, the Americas, Middle East, and Asia-Pacific regions, entry-level hiring has been reduced significantly, anywhere from 20 percent to 70 percent. The white-collar roles that have been impacted range from junior software developers and customer service staff to finance and legal professionals.

More jobs require increasing levels of AI skills, a trend playing out in job data reports from the World Bank, International Labour Organization, and regional work trend surveys from large private industry players such as Microsoft. In Australia and New Zealand, entry-level roles are being restructured instead of eliminated, based on demand for new skills.

Baseline competencies for AI-ready graduates include the ability to use AI responsibly, verify and improve AI outputs, and demonstrate value with client-ready deliverables.

Globally, schools must teach students how to use new AI tools effectively, in ways that are tailored to their academic disciplines. Moreover, students must learn to use AI to support their personal learning goals and have opportunities to reflect on how AI can enhance their critical thinking and learning.

Employers cannot provide the same level of learning support that business schools can. As schools integrate AI into their programs, they should do so in ways that put students, not the technology, first.

Implications for Business Schools

To increase college student employability, schools should ensure that faculty know about AI-driven shifts in entry-level hiring. After all, faculty are one of the biggest influences on students’ career choices because they are the ones that students seek out and trust most for career advice, according to a Fall 2024 report from the National Association of Colleges and Employers (NACE).

In addition, schools should evaluate their curricula to ensure that students develop competencies that now form the baseline for AI-ready graduates. These competencies include the ability to:

• Use AI responsibly.
• Verify and improve AI outputs with domain judgment.
• Demonstrate value to cohort teams or employers with artifacts such as drafts, analyses, and client‑ready deliverables.

With 25 years of expertise in the future of work, Career Key helps AACSB-accredited schools deliver on these student outcomes using PathAdvisor. This interoperable learning tool, along with its related digital course material, offers faculty several benefits, including:

• Short training on essential advising concepts and employment trends.
• Pre-configured student LMS assignments within PathAdvisor, Year 1 through Year 4.
• New AI learning objectives aligned with NACE career competencies including career and self-development, critical thinking, and technology.

Many business schools now integrate Career Key’s 5- to 10-minute assignments into their undergraduate curricula. These include the McCoy College of Business at Texas State University in San Marcos, the Culverhouse College of Business at the University of Alabama in Tuscaloosa, and the Carlson School of Management at the University of Minnesota in Minneapolis.

How to Scaffold AI Literacy Year Over Year

Here is an example of an undergraduate course sequence:

A Tool to Fill Curriculum Gaps

At a time when it’s growing more challenging to stay on top of today’s employability trends, Career Key can act as an expert resource for faculty. Our tool also helps students refine their AI literacy skills, which reduces their anxiety about an uncertain job market and puts them at ease knowing that their education investments will pay off.

With faculty’s help and guidance, students can know that they are on the right track to secure professional jobs in which they will thrive.

To learn more about how faculty at other institutions are using PathAdvisor in their courses, as well as how they are incorporating AI literacy into their teaching, download our Faculty Guide to Curriculum Integration.