Could universities provide students with the right tools? Would institutions invest in AI platforms? Could learners gain exposure to technologies that are rapidly reshaping workplaces around the world?

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Those questions are increasingly being answered.

A more pressing challenge is emerging instead: what happens after access is achieved?

Across industries, employers are discovering that familiarity with AI is not the same as workplace readiness. Students may know how to use AI-powered tools, experiment with generative models, and understand the fundamentals of machine learning. Yet many organizations are finding that translating that knowledge into practical decision-making, collaboration, judgment, and problem-solving remains a far more complex task.

The Global Shift From AI Literacy to AI Readiness

New research from Pearson and Amazon Web Services (AWS) suggests that this gap between exposure and execution may become one of the defining workforce challenges of the AI era.

The report, AI Readiness: Building the Bridge from Higher Education to Work, surveyed more than 2,700 learners, employers and higher education leaders across six countries, including Saudi Arabia, the United States, the United Kingdom, Brazil, Vietnam and Malaysia. While each market faces unique circumstances, the findings point toward a common reality: AI readiness is increasingly determined not by whether people can access technology, but by whether they can apply it effectively in real-world environments.

That distinction matters because the pace of workplace transformation is accelerating.

Artificial intelligence is reshaping everything from software development and financial services to healthcare, logistics and customer experience. Entry-level roles are evolving faster than many educational systems can adapt. Skills that once remained relevant for years are being redefined in shorter cycles. Employers are looking for graduates who can navigate ambiguity, evaluate AI-generated outputs and work alongside intelligent systems rather than simply operate them.

In that context, readiness becomes less about technical literacy alone and more about capability.

Why Workplace Experience Is Becoming the Missing Link

The Saudi Arabian findings offer an interesting glimpse into what happens when educational institutions, employers and policymakers attempt to align around that objective.

The Kingdom has spent years positioning itself as a global technology and innovation hub through Vision 2030, investing heavily in digital infrastructure, workforce transformation and skills development. According to the research, 88% of higher education leaders in Saudi Arabia describe AI investment at their institutions as significant or moderate.

That investment appears to be generating measurable confidence among employers.

Approximately 90% of Saudi employers say graduate workplace readiness is much or somewhat better than it was five years ago. By comparison, the cross-market average stands at 60%.

The data also reveals strong engagement between educational institutions and industry. Nearly all Saudi higher education leaders surveyed report regular interaction with employers, creating feedback loops that help align learning outcomes with workforce needs.

On paper, these are indicators many countries would welcome.

Yet the research identifies a more nuanced challenge beneath the positive headlines.

While learners report strong access to AI tools and educational resources, one-third say they want more practical, workplace-oriented opportunities to apply those skills.

The finding reflects a broader global issue that extends far beyond Saudi Arabia.

In many universities around the world, AI education has expanded rapidly. Students are learning how large language models function, experimenting with AI-assisted workflows and gaining exposure to emerging technologies. What remains harder to replicate inside a classroom is the messy reality of professional environments where technology must be integrated into business objectives, team dynamics, compliance requirements and human decision-making.

Knowing how to generate an AI-powered report is one thing.

Knowing when to trust it, challenge it, modify it or reject it altogether is something different.

That distinction increasingly separates AI literacy from AI readiness.

The report argues that readiness is created where education and work intersect. It is built through structured opportunities to practice, apply and demonstrate capabilities in environments that mirror real-world conditions.

This is why many employers are placing growing emphasis on internships, industry partnerships, project-based learning and experiential education. As AI becomes more accessible, competitive advantage is shifting toward the ability to apply technology thoughtfully and responsibly rather than simply access it.

The Skills Employers Still Value in an AI Economy

The research introduces what it calls the AI Readiness Friction Framework, designed to identify obstacles that prevent educational investments from translating into workforce outcomes.

Rather than focusing solely on technology adoption, the framework examines the barriers that emerge across the entire education-to-employment journey.

Some of those barriers are structural.

Curriculum development often moves more slowly than technological innovation. Universities may require months or years to introduce new programmes while AI capabilities evolve in weeks.

Others relate to communication.

Even institutions with strong academic programmes can struggle to maintain continuous dialogue with employers, creating gaps between what students learn and what organizations actually need.

There are also challenges around governance and responsible use.

As AI tools become commonplace, organizations increasingly need graduates who understand not only how to use these systems but also how to use them ethically, securely and transparently. Without clear guidance, informal or "shadow AI" practices can emerge, introducing risks that follow graduates into the workplace.

Perhaps most importantly, there remains a persistent gap between theoretical understanding and demonstrated competence.

Many graduates can explain AI concepts. Fewer have had repeated opportunities to solve business problems using AI in collaborative, real-world settings.

This challenge is becoming increasingly visible in labour markets worldwide.

In the United States, employers continue to report demand for workers who combine technical proficiency with critical thinking, adaptability and communication skills. Similar trends are appearing across Europe, Asia and the Middle East. As AI automates routine tasks, the value of uniquely human capabilities becomes more pronounced rather than less.

The future workforce, in other words, may not be defined by people who know the most about AI.

It may be defined by people who know how to work effectively with it.

That perspective helps explain why educational institutions are increasingly rethinking how skills are taught, assessed and credentialed. Traditional models built around static knowledge acquisition are being challenged by a labour market that rewards continuous learning and practical adaptability.

The question facing universities is no longer whether AI belongs in the classroom.

It is how to ensure that AI education translates into meaningful workplace outcomes.

Building the Bridge Between Education and Work

According to Tony Lteif, Global Revenue Officer, English Language Learning and Saudi Country Ambassador for Pearson, the foundation for that transition already exists.

"Saudi Arabia has created strong momentum for AI readiness by placing skills, education and workforce alignment at the centre of its national agenda. With AI talent development now a clear priority and large-scale capability-building already underway, the opportunity is to translate this ambition into practical, workplace-ready graduate skills. Pearson and AWS are working together to bridge the gap between higher education and employers, supporting institutions with learning, assessment, and credentials that prepare Saudi talent for an AI-enabled economy."

AWS sees a similar opportunity.

"This AI readiness research with Pearson reveals that our primary opportunity is to help translate AI tool engagement into real workplace capability. 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," said Kim Majerus, Vice President of Global Education and U.S. State & Local Government at Amazon Web Services.

Their comments reflect a growing consensus emerging across education, business and government circles.

The next phase of AI readiness will not be measured by how many people can access AI technologies. It will be measured by how effectively individuals can apply those technologies to create value, solve problems and make better decisions.

For universities, employers and policymakers alike, the challenge is becoming clear.

The race to build an AI-ready workforce is no longer about teaching people what AI is.

It is about helping them prove what they can do with it.

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