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.

By: Bindesh Vijayan, Chief Technology Officer at Myndlab

There is a line that gets repeated at every tech conference in Dubai, in every government briefing, and across most pitch decks: the UAE is building the future. Artificial intelligence is projected to contribute $96 billion to the UAE’s GDP by 2031, according to PwC and corroborated by the UAE’s own National AI Strategy. The country has invested AED 543 billion in AI since 2024 alone, as confirmed by Omar Sultan Al Olama, the UAE’s Minister of State for Artificial Intelligence. And according to Microsoft’s AI Diffusion Report for Q1 2026, the UAE has become the first country in the world to cross the 70 percent threshold for AI tool adoption among its working-age population. 

These are not vanity metrics. They reflect a deliberate national strategy that has positioned the UAE as one of the world’s most ambitious AI markets and laid the foundations for long-term technological leadership. Yet despite that progress, a disconnect is emerging between the country’s AI ambitions and the day-to-day reality of the people building products within the ecosystem.

The Gap Between AI Infrastructure and AI Adoption

Much of the discussion around AI in the UAE has focused on infrastructure, whether that is sovereign AI models, data center investments, national strategies, or the capital required to support them. These are all essential components of a successful AI ecosystem. However, infrastructure alone does not create products. Founders, developers, and businesses still need the tooling layer that sits between AI capability and real-world execution.

This is precisely the challenge a new generation of AI-native development platforms is trying to solve: embedding software engineering best practices directly into the building process so that users can focus on the product rather than mastering prompt engineering.

One of the clearest examples of this challenge is language. Arabic is spoken by more than 400 million people across 22 countries. Yet developers across the region still rely heavily on tools that were primarily designed for English-speaking users. Researchers at Nature Middle East have previously highlighted how the relative lack of robust Arabic language models continues to create limitations around linguistic nuance, dialects, and cultural context.

At the same time, the developer tools, AI coding assistants, and product-building platforms that define the modern software stack were largely built around Western markets and workflows. They assume a particular type of user, a particular language, and a particular development environment. For many builders in the GCC, those assumptions become a source of friction that compounds throughout the product development lifecycle.

A founder in Dubai building a fintech product for Emirati consumers has to work through documentation written in English, prompts that perform better in English, and interfaces that treat right-to-left text as an afterthought.

The challenge is not that these tools fail outright. Rather, they introduce small points of friction throughout the development process that compound over time, affecting productivity, iteration cycles, and ultimately product delivery. Over time, that friction compounds across teams, product cycles, and entire businesses, becoming the difference between shipping and not shipping.

We’ve Seen This Before

This pattern plays out clearly in payments, an industry where many founders across the region have spent much of their careers. The UAE has built a sophisticated financial infrastructure, but for years, the tooling that sat on top of that infrastructure, the APIs, developer documentation, and integration frameworks, was largely oriented toward Western payment methods, Western card schemes, and Western compliance frameworks. Local founders had to build workarounds. Some of those workarounds were innovative, but workarounds are not a strategy. More often than not, they are a sign that the underlying stack was never designed for the people using it.

The same lesson applies to AI. Infrastructure creates possibilities, but it does not automatically create innovation. Innovation happens when builders can move quickly, efficiently, and confidently on top of that infrastructure. If the tools developers use every day are not designed for the realities of this market, then the UAE’s AI ambitions risk being partially realized by people working around their environment rather than with it.

What Comes Next

There is a real opportunity here to address the gap between the infrastructure the UAE has built and the tools its founders, developers, and businesses actually need.

The UAE has already demonstrated that it can build AI infrastructure at scale. It has invested heavily in research, talent, adoption, and national AI initiatives, creating one of the most ambitious AI ecosystems anywhere in the world.

The next phase of that strategy is not simply building larger models or attracting more capital. It is ensuring that the people responsible for creating products, launching companies, and deploying AI solutions have the tools they need to succeed. It also means reducing dependence on a small number of external AI providers. As AI becomes embedded in critical business and government workflows, questions around privacy, data governance, and long-term resilience become increasingly important. Building capable regional AI ecosystems is not simply about innovation; it is about ensuring that organisations can deploy AI with greater control, confidence, and sovereignty.

The countries that win the next decade of technology are not necessarily the ones that spend the most money. They are the ones where the people doing the building have the right tools for the job.

Infrastructure creates possibility. Tooling turns possibility into innovation. The next phase of the UAE’s AI story will be defined by how effectively it enables the people doing the building.

Dr Esraa Khatab, Assistant Professor at the School of Mathematical and Computer Sciences, Heriot-Watt University Dubai

As we celebrate International Women in Engineering Day (INWED), attention is increasingly focused on how to prepare young women not only to participate in engineering but to lead its future. In a world shaped by artificial intelligence, sustainability challenges, and rapid digital transformation, education must go beyond technical instruction. It must cultivate what we can call engineering intelligence, a combination of technical expertise, problem-solving ability, creativity, and leadership confidence.

For young women, this preparation is most effective when education is intentionally designed to inspire, support, and position them as future innovators and decision-makers.

Inspiring Young Women Through Meaningful Learning

Engaging young women in engineering begins with making learning relevant and purposeful. When engineering is connected to real-world challenges, such as improving healthcare systems, designing sustainable cities, or developing climate solutions, it resonates strongly with students who are motivated by impact.

Project-based learning plays a key role here. When young women work on designing smart applications, building prototypes, or solving community challenges, they begin to see themselves as capable engineers contributing to society. Thes experiences move engineering from an abstract concept to a meaningful pathway where their ideas matter.

Initiatives such as the UAE’s “One Million Arab Coders” and international programs like “Girls Who Code” have successfully introduced thousands of young women to coding, AI, and digital innovation. These initiatives are powerful not just because of the skills they teach, but because they create an early sense of belonging in technology-driven environments.

Mentorship: Unlocking Potential and Building Confidence

For young women, mentorship is a transformative element of engineering education. It provides not only guidance but also reassurance, helping students navigate academic and career pathways with clarity and confidence.

Connecting young women with mentors, whether through universities, industry partnerships, or outreach programs, offers them valuable insights into emerging fields such as artificial intelligence, robotics, and renewable energy. These relationships make career paths more tangible and achievable.

In classroom settings, mentorship can be embedded into learning through project collaborations and industry engagement. When young women receive feedback from

professionals, present their ideas, and engage in real-world problem-solving, they begin to develop both confidence and professional identity.

Mentorship also nurtures leadership. By observing and interacting with experienced professionals, young women gain exposure to decision-making, teamwork, and innovation processes, essential components of future tech leadership.

Expanding Opportunities Through STEM Outreach

STEM outreach initiatives are vital in reaching young women early and sustaining their interest in engineering pathways. Programs that focus on hands-on, creative engagement, such as robotics competitions, coding bootcamps, and innovation labs, are particularly effective in building confidence and curiosity.

These initiatives create safe and encouraging environments where young women can experiment, take risks, and learn collaboratively. Importantly, they shift the narrative from simply learning technology to actively creating it.

Digital platforms have further expanded opportunities for young women in engineering. Virtual labs such as “MIT OpenCourseWare” and interactive simulations (e.g., PhET) allow learners to experiment and build practical skills remotely, with research showing strong gains in engagement and motivation. Online hackathons, including initiatives like the “UAE InnovAIte AI” Hackathon, provide young women with collaborative spaces to design real-world solutions using emerging technologies. At the same time, AI-powered tools such as “Khan Academy’s Khanmigo” offer personalized guidance, helping learners build confidence through continuous, self-paced support.

Together, these platforms create flexible and inclusive pathways that enable young women to actively engage, experiment, and grow within today’s rapidly evolving technological landscape. By introducing young women to emerging technologies early, outreach programs help them build familiarity and confidence in fields that will define the future of work.

Encouraging Young Women to Lead in Emerging Fields

Emerging engineering domains, such as artificial intelligence, smart systems, biotechnology, and sustainable energy, offer significant opportunities for innovation and leadership. Encouraging young women to explore these areas requires intentional effort within education systems.

This can be achieved through:

• Early integration of advanced topics: Introducing AI, data science, and sustainability concepts at foundational levels.
• Interdisciplinary approaches: Encouraging young women to apply engineering skills in healthcare, environmental science, and social innovation.
• Experiential learning: Providing opportunities for internships, research projects, and innovation challenges in emerging fields.

These experiences allow young women to build not only technical expertise but also the confidence to navigate complex, real-world challenges. They begin to see themselves as contributors to cutting-edge developments, rather than observers.

Building Confidence and Leadership Identity

For young women to thrive in engineering, education must also focus on building confidence and leadership skills. This includes creating environments where their voices are heard, their ideas are valued, and their contributions are recognized.

Encouraging young women to lead team projects, present their work, and participate in competitions helps them develop essential soft skills such as communication, collaboration, and critical thinking.

Representation also plays an important role. Highlighting the achievements of women engineers and innovators, both globally and within local communities, reinforces the message that leadership in engineering is both attainable and expected.

Importantly, leadership development should be embedded into the learning journey. Innovation challenges, entrepreneurship programs, and community-based projects provide platforms for young women to take initiative and drive impact.

Looking Ahead: Empowering Young Women to Shape the Future

The future of engineering will be defined by those who can think creatively, solve complex problems, and lead with vision. Preparing young women for this future is not just about education, it is about empowerment.

By combining meaningful learning experiences, strong mentorship, expanded outreach, and opportunities in emerging technologies, we can create an ecosystem where young women thrive as engineers and leaders.

As we celebrate INWED, the focus is clear: to ensure that young women are equipped not only with skills, but with the confidence and ambition to lead. When this happens, they do more than contribute to technological advancement, they shape it.