A new global study finds that misalignments between higher education and employer expectations are slowing the development of an AI-ready workforce, as colleges struggle to translate learning into practical, job-ready skills.
The report, released by Pearson and Amazon Web Services on April 13, draws on more than 2,700 survey responses from learners, higher education leaders, and employers across six countries, offering a broad look at how AI readiness is developing along the education-to-work pipeline.
Among the most striking findings, 53% of employers said their primary challenge is finding graduates with the right AI skills, underscoring a significant gap between workforce demand and available talent.
At the same time, 78% of higher education leaders said they believe their institutions are meeting employer expectations, revealing a significant disconnect in how preparedness is perceived.
The gap is further highlighted by student outcomes, with just 14% of current graduates reporting a high level of proficiency in applying AI tools within a professional workflow.
Researchers said the issue is not a lack of interest or access to AI tools, but rather a breakdown at the point where learning must translate into real-world capability, particularly as AI continues to reshape entry-level roles and shorten the lifespan of technical skills.
“This AI readiness research with Pearson reveals that our primary opportunity is to help translate AI tool engagement into real workplace capability,” said Kim Majerus, vice president of global education and U.S. state and local government at AWS.
“AWS is committed to working alongside our education partners to ensure every learner develops AI literacy, in addition to the judgment, adaptability, and hands-on experience employers need,” Majerus added.
“It is clear that basic AI literacy is no longer enough,” said Tom ap Simon, president of higher education and virtual learning at Pearson. “Schools that lead in AI readiness today will shape the future of workforce readiness tomorrow,” he continued.
“Building an AI-ready workforce depends on structured, shared systems that amplify human skills and connect curriculum to real work,” Simon stated.
To address these challenges, the report introduces an “AI Readiness Friction Framework,” identifying six key barriers that hinder progress from education to employment.
These include pace friction, where academic institutions struggle to keep up with rapid AI-driven workplace changes, and connection friction, which reflects weak feedback loops between educators and employers that limit alignment on skills needs.
Additional challenges include: capability friction, tied to uneven faculty expertise in AI; governance friction, stemming from a lack of clear guidance on responsible AI use; and experience friction, where students lack opportunities to apply AI tools in real-world contexts.
The final barrier, skills friction, highlights the mismatch between what graduates can demonstrate and the judgment, adaptability, and collaboration employers expect, with the report emphasizing that stronger coordination between education providers and industry will be critical to closing these gaps and building a workforce prepared for AI-driven jobs.
