Insidehighered iconInsidehigheredSep 29, 2026 ~7 min source read

Career Prep When No One Knows What’s Next

Colleges are shifting toward work-based learning, durable skills, AI-native programs and organizational changes to help graduates land jobs amid an uncertain labor market and new accountability rules.

Career Prep When No One Knows What’s Next

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Institutions are emphasizing experiential learning and durable skills while adding AI-native programs, reorganizing leadership, and strengthening employer engagement.

Capacity constraints limit expansion of work-integrated learning: 69% of provosts cite staff capacity as a primary barrier, and only 20% report a coherent institutional vision for how AI will change teaching.

# Why colleges are under pressure

# What institutions are changing Higher education leaders report shifting tactics on several fronts:

  • Experiential learning and work-integrated experiences. Colleges are doubling down on internships, co-ops and employer-aligned projects as the most direct pathways to employment.
  • Durable skills development. Institutions are reinforcing cross-cutting competencies—communication, problem solving, teamwork—that employers still value across changing technical requirements.
  • AI-native programs. New curricula and programs that assume AI as part of the workforce baseline are emerging alongside traditional majors.
  • Organizational realignment. Some institutions are elevating career services into senior leadership roles to link academic planning and market signals more directly.
  • Entrepreneurship requirements and ecosystem-building. Schools are encouraging students to develop venture and networking skills and are working to strengthen regional employer ecosystems.

Inside Higher Ed's 2026 survey of chief academic officers found that 47 percent of provosts now view preparing students for an AI-shaped workforce as a central organizing principle for academic planning. Yet capacity and clarity lag: 69 percent of provosts identify staff capacity as the primary barrier to expanding work-integrated learning, and only 20 percent agree their institution has a coherent vision for how AI will change what to teach and how to teach it.

Campus leaders and advisors describe conflicting evidence about AI's labor-market effects and point to broader economic and policy forces—such as tariff shifts—that make hiring patterns more volatile. For tuition-dependent colleges, leaders warn that graduate outcomes are existential: when employability underpins a school's value proposition, weak outcomes threaten institutional viability.

# An example of an operational change Wake Forest University moved career services into the president's cabinet years ago. Its vice president for personal and career development argues that senior-level placement matters because it gives career leaders direct access to deans and vice presidents as peers. That positioning helps career teams influence curricular decisions and ensure marketplace realities inform academic planning rather than waiting for post-hoc adjustment.

# Barriers institutions must address Expanding effective work-integrated learning is limited not primarily by employer interest or faculty buy-in, but by institutional capacity to design, staff and scale these experiences. Other challenges include inconsistent measures of AI's labor impact and the speed at which programs can be redesigned without sacrificing academic quality.

# Practical implications for students and institutions Students should seek programs that combine relevant technical coursework with structured employer interaction and explicit training in transferable skills. Institutions should prioritize staffing and organizational design that connects career strategy with academic governance, invest in scaling employer partnerships, and develop clearer, institution-wide plans for integrating AI into curricula and career readiness.

# Bottom line Colleges are adopting a combination of established practices—experiential learning and skills training—and newer approaches, such as AI-native programming and senior-level career leadership, to reduce the mismatch between graduate preparation and an unsettled labor market. Progress depends on capacity, clearer institutional vision for AI, and deeper, sustained engagement with employers and regional ecosystems.

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