By Amjad Alqaqaa – Vice President – MEAI

Power sectors around the world are undergoing rapid transformation. Digital technologies, advanced materials, and the shift towards lower-carbon energy are reshaping how power plants and critical infrastructure are designed, operated, and maintained. Yet one persistent challenge continues to hold the sector back: a shortage of people with the right engineering and technical skills.

As the UAE continues to advance its ambitions as a leading hub for innovation and technology, there is an increasing need to strengthen and future-proof STEM capabilities to keep pace with evolving industry demands. According to a report by STEM workforce consultancy SThree, 40% of STEM professionals in the UAE believe that upskilling and reskilling are the most effective ways to boost productivity and competitiveness. While more than a third (32%) point to skills shortages as a barrier to productivity, highlighting a clear gap between workforce capabilities and industry needs.

Additionally, data from the Hays 2026 US Salary & Hiring Trends Guide indicates that companies in the UAE are starting to slow down recruitment and instead are investing in the skills of their existing workforce, with around 42% of employers prioritising upskilling over hiring.

Research from LinkedIn also suggests demand for green skills is rising much faster than supply, highlighting a widening gap between the skills needed for the energy transition and the talent currently available in the workforce.

For power generation companies, this is more than a recruitment issue. Skills shortages can impact equipment reliability, delay maintenance programmes, and slow the deployment of new technologies. In a sector where uptime, safety, and efficiency are critical, having the right expertise in place is essential.

At the same time, interest in STEM subjects among young people has fallen in recent years.  This weakens the future talent pipeline. This means companies must do more to attract and develop STEM talent.

Showing young people what engineering looks like today

One of the challenges is perception. Many young people still associate engineering with traditional industrial roles, rather than the highly advanced, technology-driven careers available today.

Today’s engineers work with advanced digital tools, automation systems, and real-time monitoring technologies. In the power sector, they help keep turbines, pumps, and other critical systems running efficiently. They also work on challenges linked to sustainability, energy efficiency, and emissions reduction.

To address this gap, employers must play a more active role in educating emerging talent about the career opportunities in the sector. That means working more closely with schools, colleges, and universities to showcase the wide range of careers available across engineering and energy.

Partnerships between industry and academia play an important role here. For example, John Crane works closely with the University of Sheffield to support research and PhD programmes in areas such as materials science and engineering. Collaborations like this help connect academic research with real industrial challenges and encourage more students to consider careers in engineering.

These partnerships also help ensure that new research translates into practical solutions that can support industries such as power generation.

Why apprenticeships matter

Alongside academic pathways, apprenticeships are another key way to attract new talent into engineering.

They offer a practical, accessible route into engineering, allowing individuals to gain hands-on experience while working towards recognised qualifications. For employers, apprenticeships provide an opportunity to develop skills aligned to real operational needs, from maintenance and reliability engineering to digital and software capabilities.

But apprenticeships are not only for new recruits. They can also help people who are already in work develop new skills. Programmes linked to areas such as leadership, project management, and digital technologies allow employees to adapt as roles change and technology evolves.

This matters because the skills challenge is not only about bringing new people into the sector. It is also about helping the existing workforce build the capabilities needed for the future.

Building the right skills through training partnerships

Developing a skilled workforce requires more than internal programmes alone. Strong partnerships with external training providers are essential to ensure employees gain the specialist knowledge needed in highly technical environments.

Working with a network of training providers enables organisations to deliver structured learning alongside on-the-job experience. This approach ensures that training remains aligned with real operational challenges, including maintaining equipment reliability, improving efficiency, and meeting evolving safety standards.

Reaching a broader talent pool

Engineering companies need to widen their outreach and look beyond traditional recruitment channels. This includes engaging with students earlier and encouraging people from different backgrounds to consider technical careers.

In addition, requalification programmes are increasingly important in some regions. For example, in the Czech Republic, targeted requalification initiatives are helping individuals transition from other industries into engineering roles, providing a practical route to address skills shortages while bringing valuable experience into the sector.

Ensuring training programmes cater to a wide range of people with varying levels of experience can upskill new and existing workers and build a healthier talent pipeline. Providing that support is an investment that helps create a stronger, more resilient workforce in the long term.

Building the workforce of the future

The power sector plays a central role in driving the global energy transition. In the Middle East, this transition is expected to drive demand for a wide range of engineering roles, particularly in renewable energy, grid infrastructure, and related technologies, highlighting the need for targeted training and workforce development programmes to equip both new entrants and existing workers with relevant technical skills.

Engineers and technicians will be needed to maintain power plants, improve equipment performance, and develop new energy technologies. But these goals will only be possible if the industry has access to the right skills.

To achieve this, companies must think differently about talent. Strengthening collaboration with educators, improving outreach to diverse talent, and offering practical training routes such as apprenticeships all play an important role in addressing the STEM skills gap.

Apprenticeships alone will not solve the skills gap. But when combined with research partnerships and targeted workforce development, they can play a major role in rebuilding the STEM talent pipeline. By investing in people and skills today, the power sector can build the workforce it needs to support a more reliable and sustainable energy system for the future.

by Dr Ekaterina Abramova, Adjunct Assistant Professor of Management Science and Operations at London Business School

The AI revolution is not on the horizon; it is already transforming how we work, solve everyday problems, and interact both with one another and with technology. From generative models to agentic systems capable of disrupting entire industries, artificial intelligence has advanced at a pace that few institutions, businesses, or governments are fully prepared for. What once felt like a distant technological possibility has become a structural force shaping labour markets and economies. As a result, one of the most pressing questions facing societies is no longer whether AI will change the world, but whether it will make it fairer. Increasingly the answer depends not only on the technology itself, but on the choices organisations and governments make about how its benefits are shared.

AI has the potential to unlock unprecedented prosperity. Yet history shows that technological revolutions rarely distribute their rewards evenly. Without deliberate intervention, the benefits of AI risk concentrating in the hands of a small number of large technology firms, highly skilled professionals and capital owners. This pattern has already emerged in earlier waves of digital transformation, where wealth and opportunity accumulated disproportionately in regions best positioned to adapt. For AI to foster equality rather than widen disparity, policymakers must treat inclusion as an ex-ante design principle rather than an ex-post correction.

The first crucial step for achieving fairness is improving the data that AI systems rely upon. Algorithms are only as representative as the information used to train them. When datasets exclude marginalised or underrepresented communities, AI risks reinforcing existing biases. Organisations and governments developing AI algorithms should prioritise collecting data from communities historically overlooked in policy design, such as rural populations, low-income groups, minority communities and those outside the formal labour markets. More inclusive datasets lead to fairer systems, more effective public services and policy decisions that better reflect the realities of entire populations, rather than just their most visible segments.

Another equally important aspect is how governments distribute the productivity gains and wealth generated by AI into broader societal benefits. Different regions are experimenting with alternative approaches. In parts of the Middle East, including the United Arab Emirates, economic gains from technological advancement are often channelled through state-led investment strategies rather than relying solely on traditional taxation and redistribution mechanisms. While VAT and other taxes exist, governments often reinvest a significant share of national income derived from natural resources and state-owned enterprises directly into infrastructure, public services, education and economic diversification. This approach builds long-term national capability by funding human capital development, strengthening digital infrastructure and fostering new sectors that create employment and opportunity.

Such strategies highlight an important principle: AI benefits do not need to be redistributed after inequality has emerged. They can be embedded in development strategies from the outset. By investing in education, digital skills and access to technology, governments expand the number of people able to participate in the AI ecosystem rather than merely compensate those left behind. China, for example, has made substantial investments in AI education and research capacity, recognising human capital as central to technological leadership. Every year 100,000 selected teenagers are funnelled into elite science talent streams across top high schools. These “genius classes” systematically train students to excel in international maths, physics, chemistry, biology and computer science competitions.

The pace of the AI revolution makes this challenge more urgent than previous technological transitions. Earlier industrial transformations unfolded over decades, allowing societies time to adapt institutions and labour markets. AI development in recent years has gained pace. Breakthroughs that once took years are now emerging within months, with new capabilities rapidly spreading across sectors from healthcare diagnostics and financial analysis to logistics and defence industries. This acceleration has been further intensified by the present-day AI race to achieve Artificial General Intelligence (AGI), amid a widespread belief that the first government to reach this milestone will gain a decisive strategic advantage. Organisations at the forefront of AI development are reluctant to slow for fear of falling behind geopolitical or commercial rivals. Meanwhile, many governments are hesitant to introduce AI regulation, concerned that premature constraints could hinder innovation and weaken their competitiveness in the pursuit of AI leadership.

However, the path forward requires a global perspective. While governments should encourage innovation, they must also recognise that AI technology will diffuse across borders. Hence governments worldwide should collaborate towards a global AI governing body, or at the very least, agree on minimum safety and fairness standards for AI deployment. The EU AI Act provides an important foundation by identifying unacceptably high-risk AI applications that should be prohibited. When forming such regulatory frameworks, governments should seek guidance from leading AI scientists to ensure they fully understand where the principal risks originate. Indeed, many prominent experts in the field argue that regulation is failing to keep pace with AI innovation.

Allowing AI technology to evolve without placing guardrails in place early risks embedding structural inequalities, particularly in labour markets, education access and capital distribution. Ultimately, the debate about AI and inequality is not primarily about algorithms; it is about governance. Technology reflects the priorities of the societies that deploy it. If policymakers treat AI purely as an engine of leadership and economic growth, its benefits will likely accrue to those already best positioned to capture them. But if AI development is guided by a clear commitment to inclusion through better data, wider access and sustained investment in human capital, it has the potential to expand opportunity on a global scale. As AI reshapes labour markets, workers will need opportunities to develop capabilities that complement intelligent systems rather than compete directly with them. Access to AI infrastructure, computing resources, data and digital connectivity must not be confined to a small group of corporations or wealthy regions.

The direction of the AI revolution is not predetermined. The question is not whether AI will transform our world, but whether governments and institutions will act quickly and thoughtfully enough to ensure that its benefits are broadly shared. In the race to build increasingly powerful systems, equal attention must be given to building the social and economic frameworks that will ensure the future is genuinely fair.

By Alfred Manasseh, COO & Co-Founder of Shaffra

Across the GCC, AI is becoming more operational. The conversation has moved beyond whether organisations are testing AI and toward how deeply these systems are being embedded into daily work. McKinsey’s finding that 84% of GCC organisations have adopted AI in at least one business function shows the region’s strong momentum, but the more important shift is where this technology is now creating measurable value.

AI is beginning to operate inside real enterprise workflows, where productivity, cost, speed, service quality, and governance can be measured. This practical shift means AI is being judged less by novelty and more by whether it can reduce manual work, improve response times, and support better execution across organisations.

Where AI is being deployed

AI deployment is gaining traction in structured, high-volume functions where it can remove this coordination burden and give employees more capacity for skilled output. Asana’s research has found that around 60% of time is spent on “work about work,” such as chasing updates, attending unnecessary meetings, and switching between tools.

Customer service teams are using AI for automated query handling, routing, escalation management, and multilingual support. Operations teams are applying AI to order processing, workflow coordination, and SLA monitoring.

In HR, AI is supporting CV screening, interview scheduling, and onboarding orchestration. In finance, it is being used for invoice processing, reconciliation, and anomaly detection. Sales teams are also applying AI to lead qualification, follow-ups, CRM hygiene, and pipeline updates.

Regional governments are also preparing the workforce for this reality. Digital Dubai recently launched the AI Workforce Transformation Program, known as AI+, to help train 50,000 government employees for an AI-ready workforce.

Three phases of AI workforce evolution

AI use across the workforce can be understood in three phases. First, AI acts as an assistant through copilots, chat interfaces, summarisation, drafting, search, and advisory tools that improve individual productivity. Second, AI becomes an operator, completing defined tasks across CRM, HR, finance, customer service, and operations systems within controlled boundaries. Third, AI develops into a workforce layer, where systems are assigned roles, KPIs, access rights, escalation pathways, and governance controls. At this stage, Autonomous AI Teams operate as governed digital employees, helping structure, assign, monitor, and improve work.

How mature AI deployments operate

AI is not replacing entire jobs. It is restructuring work by taking over repetitive tasks within roles. Human teams are shifting toward oversight, exception handling, decision-making, escalation management, and quality control.

Autonomous AI Teams operate as coordinated systems rather than standalone models. They support humans through role-based actions with defined responsibilities, structured access to enterprise systems, clear decision boundaries, controlled autonomy levels, human escalation pathways, performance metrics, auditability, and governance.

From tools to workforce infrastructure

Before scaling autonomous AI systems, executives need clear visibility into decision-making, accountability, risk controls, and human intervention points. Trust grows when productivity gains are measurable and governance is visible. IBM research shows that 77% of UAE senior leaders have already seen significant productivity gains from AI, which reflects growing confidence in its operational value.

Across Shaffra deployments, Autonomous AI Teams have contributed to more than 2 million manual work hours saved monthly across operational workflows. Organisations have reported up to 80% reductions in operational costs, customer service teams can manage up to five times more queries, and HR recruitment cycles that previously took weeks can be reduced to hours.

The future workforce layer

The GCC has a strong appetite for AI adoption, but many organisations still need to redesign workflows and overcome fragmented legacy systems before AI teams can function as part of daily operations. Research showing that 94% of UAE data leaders lack complete visibility into AI decision-making processes reinforces why explainability, governance, and workflow design must develop alongside deployment.

The next phase of AI is about building a governed workforce layer where humans and Autonomous AI Teams execute together with clarity, accountability, and valuable impact.