Why graduates aren’t AI-ready: Six frictions revealed in new AWS-Pearson study on education to workforce gaps

The workplace is changing faster than higher education can keep up, putting student success at risk. As artificial intelligence (AI) redefines what employers expect from new hires, graduates are entering the workforce with a widening gap between what they learned within their higher education curriculum and what their new jobs require.

A new joint research report from Pearson and Amazon Web Services (AWS) examines this challenge. As a lifelong learning company, Pearson brings expertise in learning science and curriculum design. AWS brings insight into how AI is deployed across industries and institutions. Together, the two organizations developed the AI Readiness: Building the Bridge from Higher Education to Work report to understand where the transition from higher education to work breaks down and what institutions and both public- and private-sector employers can do about it.

Conducted in collaboration with independent research firm PSB Insights, the study draws on survey data from over 2,700 learners, higher education leaders, and employers, and qualitative interviews with higher education leaders across six countries: the United States, the United Kingdom, the Kingdom of Saudi Arabia, Brazil, Malaysia, and Vietnam.

The findings reveal that AI readiness is not a single skills gap waiting to be filled. It is a systemic breakdown in the transition from learning to work, driven by six frictions that compound on one another.

Six frictions stalling the learning-to-work transition

Dr. Vincent Liardi, global marketing lead for thought leadership in higher education at Pearson, noted that the six frictions were not assumed at the outset. They surfaced from the data itself.

“We really let the data lead us to where the friction points were,” said Liardi. “We truly have an evidence-based friction framework.”

The research identifies six structural barriers that reinforce each other across the education-to-workforce pipeline: pace, connection, capability, governance, experience, and skills. Institutions have made real progress in giving students access to AI tools, but the study found that access alone does not close the gap. The depth of AI learning has not kept pace with the breadth of tools available, and faculty readiness remains one of the most significant barriers to student preparedness. Higher education leaders and students also view AI readiness differently; a perception gap explored further in the full findings.

Additionally, more than half of employers surveyed said they cannot find graduates with the right AI skills. The full study explores how each friction contributes to this gap and what it takes to close it, including a portrait of the research’s “Optimal AI-Ready Graduate,” defined not by tool proficiency alone but by an integrated set of capabilities spanning applied judgment, ethical reasoning, and collaboration.

A diagnostic framework to help leaders act

Liardi described one of the key messages of the research this way: “AI-ready graduates don’t emerge by chance,” he said. “Readiness is built where learning and work connect.”

To help institutions strengthen that connection, the study provides a friction framework designed as a practical tool for leaders to identify which of the six frictions are most acute in their own context and then prioritize interventions. That framework inverts each friction into a desired outcome.

A well-positioned institution, according to the research, is agile in its curriculum, connected to industry, equipped with AI-capable faculty, governed by clear and enabling policy, structured to deliver applied experience, and producing graduates with the compound skills employers require.

Employers have a role to play in reaching that outcome, too. The report encourages organizations to move beyond thinking of themselves as passive consumers of talent and instead co-produce AI-ready graduates through shared governance, co-designed curricula, structured feedback loops, and clearer job posting language about AI-readiness expectations.

Students can also take ownership of their readiness. The report recommends building a digital portfolio of AI-integrated projects rather than relying solely on a resume, cover letter, or degree to demonstrate competency. One finding from the study underscores why that matters. When employers were given a forced choice between two hypothetical candidates—one with strong AI skills but no degree and one with a degree but limited AI experience—the degree’s advantage was just four percentage points.

The report closes with the Friction Framework Self-Assessment, which provides higher education leaders a set of questions as a starting point for evaluating where their institution stands today.

Maryclaire Abowd, senior business development manager for education and research at AWS, said higher education leaders have been looking for exactly this kind of evidence-based support. “Leaders have been asking for guidance on how to actually move forward,” said Abowd. “This research is designed to give them that.”

From global findings to localized action plans

This global report is the first phase of a broader research initiative. Pearson and AWS will release six country-specific reports in the coming months, offering localized findings and recommendations tailored to each market examined. These reports will give institutional leaders a more granular view of the frictions shaping AI readiness in their own region.

The data, the diagnostic framework, and the full set of recommendations are available now. Read the complete AI Readiness: Building the Bridge from Higher Education to Work report to assess where your institution or organization stands and identify where to act first.

Learn more about Amazon’s Future Ready 2030 initiative and how AWS Training and Certification are equipping early-career professionals with essential skills in the AI era. To learn how AWS supports researchers and educators, connect with an AWS for Education expert.

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