AI is reshaping jobs — academia must keep pace

We are witnessing an era-defining revolution. Artificial Intelligence (AI) is no longer confined to the labs of elite research institutions; it’s rapidly transforming industries, redefining job markets, and reshaping the way we live and work. AI tools have given the world a glimpse of AI’s power to automate tasks once thought uniquely human: writing, coding, customer support, even decision-making. As AI continues to evolve, its impact on economies and societies will only intensify. Yet, amid this transformation, a critical gap is widening, the gap between academic preparation and industry demand.

Today’s job market is not waiting for tomorrow’s graduates to catch up. A growing number of roles are being automated or transformed. Routine jobs in finance, law, journalism, and customer service are being reimagined with AI at the centre. A recent World Economic Forum report predicts that by 2025, 85 million jobs may be displaced by AI, while 97 million new roles may emerge, but these new roles will require very different skills. This is where our academic institutions must step up.

Universities and colleges can no longer operate in silos. They must actively collaborate with industry leaders to ensure that what is taught in classrooms reflects real-world demands. Curricula must be agile and responsive, incorporating practical training in AI, data literacy, ethical technology use, and interdisciplinary problem-solving.

Job market trends

It is also crucial that we rethink how students choose their academic paths. With AI poised to redefine entire sectors, academic advisors must guide students not just based on traditional interest areas, but also on forward-looking data about job market trends. Students deserve transparency about where opportunities will lie in the AI-powered future. Moreover, the responsibility doesn’t rest solely with students or employers. Academic institutions must champion this change. They must embed AI literacy into all disciplines, invest in research that translates to industrial applications, and foster a culture of lifelong learning to keep graduates relevant in a fast-changing world.

To bridge the gap between academia and the rapidly evolving AI job market, universities must align their curricula with real-world industry requirements. This means collaborating with AI companies to co-design courses that cover applied machine learning, MLOps, AI ethics, and domain-specific AI applications. Alongside traditional degree programmes, institutions should offer short-term, industry-recognised micro-credentials and certifications so that students graduate with both theoretical grounding and proof of practical skills. Capstone projects, developed in partnership with companies, can give students the opportunity to solve actual business challenges, ensuring they are workplace-ready from day one.

Research and innovation

Collaboration should extend beyond teaching into the realm of research and innovation. Establishing shared industry–academia research labs would foster cutting-edge AI advancements. Academic researchers should be encouraged to tackle pressing industry challenges, such as improving AI robustness, scalability, and energy efficiency. Stronger technology transfer offices can help commercialise this research through patents, startups, and licensing agreements, ensuring that academic breakthroughs have tangible societal and economic impact.

Another powerful way to close the gap is through talent exchange between academia and industry. Bringing in adjunct faculty from AI firms can provide students with up-to-date, applied perspectives. Similarly, offering professors sabbaticals within industry would help them understand operational realities and emerging challenges. Mandatory internships or apprenticeships for AI students can ensure that practical exposure complements academic learning, equipping graduates with both knowledge and hands-on experience.

Access to real-world data and computational resources is essential for meaningful AI education and research. Industry partners can contribute open datasets, such as Google’s Open Images or Meta’s AI datasets, for academic use. Cloud service providers like AWS, Google, and Microsoft can offer cloud compute grants to support student and faculty projects. Establishing testbed environments would allow academic teams to validate their AI solutions under near-real industry conditions, accelerating the readiness of research outputs for deployment.

Intellectual property agreements

Clear, well-defined intellectual property agreements are essential to give both academia and industry the confidence to collaborate without fear of legal disputes. Equally important is the need to reform academic promotion and tenure policies so that faculty are recognised and rewarded for impactful industry engagement, technology transfer, and applied problem-solving, rather than being evaluated solely on the number of published research papers. Such changes would encourage more researchers to work on projects with direct societal and economic benefits, ultimately accelerating innovation.

Creating spaces for regular interaction between academia and industry is equally important. Joint AI hackathons and competitions can inspire creative solutions and foster talent discovery. Conferences and workshops should integrate applied AI tracks and industry panels alongside academic sessions. Mentorship programs where experienced industry professionals guide PhD students can also help young researchers navigate decisions between academic and applied career paths.

AI-focused entrepreneurship

Finally, universities can play a vital role in driving innovation by actively fostering AI-focused entrepreneurship. By partnering with leading accelerators, they can provide the mentorship, networking, and business expertise needed for research ideas to evolve into viable startups. Dedicated venture funding streams for university-born AI companies would further accelerate this process, enabling promising projects to move from the lab to the market more quickly and with greater impact.

Examples of successful academia–industry partnerships already exist: DeepMind works with Oxford and MIT on fundamental AI research; IBM’s AI Horizons Network co-develops solutions with multiple universities; and Carnegie Mellon has partnered with Argo AI to advance autonomous vehicle technology. However, challenges remain, including cultural differences between academia’s focus on publishing and industry’s emphasis on return on investment, as well as reconciling short-term business needs with long-term research objectives. Data privacy and proprietary restrictions also require secure frameworks, such as federated learning, to enable meaningful collaboration without compromising confidentiality.

The Gulf region, with its ambitious digital transformation agendas and visionary leadership, has a unique opportunity to lead this realignment. By building bridges between academia and industry, we can ensure that our future workforce is not just AI-ready, but AI-resilient. The AI revolution is here. It’s time our education system caught up.

Dr Sanaa Kaddoura is an Associate Professor at Zayed University, Abu Dhabi, specialising in artificial intelligence and machine learning applications in cybersecurity, sustainability, and public sector innovation.