Artificial intelligence is increasingly reshaping global labor markets, forcing institutions to reassess how future workers are trained. Speaking on the role of AI in education, former Walgreens Boots Alliance CEO Roz Brewer has argued that higher education should not attempt to “AI-proof” itself, but instead embrace the technology as a core component of learning and workforce preparation. Her comments come as universities and policymakers grapple with how to adapt curricula to an economy where automation and machine learning are rapidly expanding.

Rethinking Education in the Age of AI

Brewer’s position reflects a growing view among business leaders that artificial intelligence should be embedded into education systems rather than treated as a disruptive force to be restricted. As AI tools become more widely used in research, business operations, and creative industries, students are increasingly expected to graduate with practical familiarity with these technologies.

The argument challenges traditional approaches that seek to limit AI usage in academic environments due to concerns around academic integrity or overreliance on automation. Instead, proponents suggest that controlled and structured integration of AI tools can enhance critical thinking, data literacy, and problem-solving skills.

This shift aligns with broader labor market trends, where employers across sectors are prioritizing candidates with hybrid skill sets combining domain expertise and digital fluency.

Labor Market Transformation and Skills Demand

The global economy is undergoing a structural shift driven by automation, machine learning, and generative AI systems. Industries ranging from healthcare, finance, logistics, and retail are increasingly integrating AI into core operations, altering the nature of entry-level and mid-career roles.

As a result, the demand for workers capable of interacting with AI systems is rising, while traditional task-based roles are being redefined or automated. This transition places pressure on education systems to move beyond static curricula and adopt more adaptive learning frameworks.

For countries such as Israel, where technology and innovation sectors play a central role in economic growth, the alignment between education and AI-driven industry needs is particularly significant. Universities and training institutions are increasingly evaluated on their ability to produce talent equipped for digital-first environments.

Strategic Implications for Higher Education Systems

The debate over AI in education is also raising strategic questions about institutional competitiveness. Universities that integrate AI into teaching methods, research capabilities, and administrative systems may be better positioned to attract students and industry partnerships.

At the same time, concerns remain around equitable access to AI tools, data privacy, and the risk of widening skill gaps between institutions with differing levels of technological resources. Policymakers are therefore balancing innovation with regulatory oversight to ensure responsible adoption.

The shift also has implications for lifelong learning models, as professionals increasingly require continuous upskilling to remain competitive in AI-enhanced work environments.

Looking ahead, the direction of higher education will likely depend on how effectively institutions integrate artificial intelligence into their core missions while maintaining academic rigor. Key risks include uneven adoption across regions, resistance from traditional academic structures, and uncertainty over assessment standards in AI-enabled learning environments. At the same time, successful integration could significantly enhance workforce readiness and strengthen alignment between education systems and evolving global economic demands.