Tech Features
How are leaders in the Middle East using AI to solve for supply chain issues
Attributed by Harsh Kumar, Chief Strategy Officer, Shipsy
The Middle East’s logistics sector is undergoing a fundamental change as industry leaders embrace AI to tackle region-specific challenges and build the foundation for autonomous supply chain operations. “In the wake of the fourth industrial revolution, governments and businesses across the Middle East are beginning to realise the shift globally towards AI and advanced technologies. We estimate that the Middle East is expected to accrue 2% of the total global benefits of AI in 2030. This is equivalent to US$320 billion,” highlights a PwC Middle East report.
When it comes to making supply chains autonomous, logistics leaders in the Middle East agree that there are some inherent challenges in the region that hinder growth and that they are working towards addressing the same.
Addressing the Middle East’s Obstacles to Autonomous Supply Chains
Inaccurate addresses remain one of the most critical pain points for Middle Eastern logistics operations, directly impacting productivity, costs, and customer experience. The region’s diverse linguistic landscape and inconsistent address systems have made last-mile delivery particularly challenging.
In the Middle East, inefficient address structure often results in packages and letters being addressed only with a recipient’s name, city, and country, lacking a specific delivery address. Courier services are typically provided with just a name and mobile number, requiring them to investigate and determine the intended delivery location. According to a report by Logistics Middle East, incorrect addresses can potentially impact more than $7.42 billion in eCommerce revenue in the Middle East.
“AI’s success and differentiation from any other technology before it, will depend on its ability to solve region-specific challenges. Unlike banking and financial services sectors, logistics and supply chain operations often deal with fragmented processes and disconnected systems. AI is uniquely positioned to bridge these gaps by harmonizing data, streamlining workflows and enhancing efficiency across the entire value chain all of which have a direct impact on operational productivity.” said Iyad Kamal, ex COO of Aramex.
Incorrect addresses also create another challenge of driver productivity and retention. With retail customer expectations rising and delivery times shortening, logistics providers will need to focus on making it easier for drivers to complete their work, get the right information at the right time to ensure they deliver a better customer experience.
The challenge compounds due to a flawed hypothesis in route optimization which does not take into consideration real-world variables when allocating deliveries creating delays and impacting driver productivity. Another critical problem that needs to be addressed is financial settlements. Validating data for settlements remains a heavily manual and time-intensive process. It will not be incorrect to say that only about 10% of invoices are accurately validated, as the human effort required is significant. This results in a higher risk of inaccuracies in settlement. AI agents can help here by analyzing delivery proofs against trip data and automatically calculate delay fees using GPS timestamps and contractual rates.
How leaders are moving from Guesswork to Data-Driven Precision
Resource allocation has traditionally relied on intuition, resulting in suboptimal vehicle utilization and excessive mileage. Digital Twin technology is changing this paradigm by enabling logistics providers to run scenario analyses and predict the impact of different allocation strategies before implementation.
Real-time incident management has also evolved beyond manual dashboard monitoring. Autonomous monitoring agents now continuously check operations against KPIs, detecting anomalies like delays or harsh braking incidents. When issues arise, these agents assess impact, proactively communicate updated ETAs to customers, and suggest rescheduling options, thereby drastically reducing resolution times.
Aujan Coca-Cola Beverages Company is leveraging Agentic Incident Management, AI-powered dynamic route optimisation and load balancing and Agentic Control Tower to enhance customer experience by ensuring ETA adherence and real-time visibility.
Fair compensation and equitable workload distribution emerged as critical for combating driver attrition, with leaders emphasizing that rewards must be immediate rather than deferred to maintain motivation. Customer-centric execution requires moving beyond basic data matching. AI-enabled semantic matching creates comprehensive customer profiles that preserve delivery preferences across different drivers and addresses, ensuring consistent service quality.
“Verifying every transaction and validating every invoice, continue to be a massive overhead for supply chain leaders even in 2025. Companies that can leverage AI to automate highly human-intensive processes will unlock velocity as an advantage, making it harder for their competition to catch up.” said Soham Chokshi, Co-Founder and CEO of Shipsy, while emphasizing AI’s role in logistics.
The Road Ahead
Logistics leaders in the Middle East envision autonomous, intelligent, and customer-centric supply chains powered by agentic AI that independently solves complex problems. However, the success of these systems hinges on a human-in-the-loop approach. Balancing algorithmic optimization with human expertise, such as local knowledge and driver preferences, is essential to address the region’s unique challenges, like inefficient address systems. By integrating continuous monitoring and predictive intervention, AI can shift operations from reactive to proactive, but human oversight ensures adaptability and accuracy. This synergy between AI capabilities and human insight drives resilient, efficient, and customer-focused logistics networks.
As the region’s logistics sector continues its digital transformation, these AI-driven foundations are positioning Middle Eastern supply chains at the forefront of global innovation in autonomous operations.
Tech Features
ENGINEERING INTELLIGENCE IN EDUCATION: PREPARING YOUNG WOMEN FOR FUTURE TECH LEADERSHIP

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.
Tech Features
FIVE WAYS UAE WORKFORCE PLANNING IS CHANGING IN 2026
The UAE is entering a more complex phase of workforce growth. Hiring momentum remains strong, with the country recording a Net Employment Outlook of 60% for Q2 2026, placing it among the strongest employment markets globally. Yet the main challenge for companies is whether their employment structures, immigration planning, compliance systems, and HR leadership can support growth at scale.
Aethra Advisory, a UAE-based global hiring strategy and mobility architecture firm, outlines five shifts companies should prepare for as compliance, immigration, and HR become more connected.
HR is becoming workforce architecture
HR can no longer be treated as an administrative function focused only on recruitment, onboarding, contracts, and employee relations. In 2026, HR leaders are expected to help design the workforce model itself. That includes where a company hires, which employment structures it uses, how talent moves across borders, and where compliance risk may appear. A hiring decision is now linked to visa eligibility, payroll structure, sponsorship, worker classification, relocation timelines, and long-term operating needs.
Many companies still hire first and address structure later. The consequences often emerge months afterwards, when employment models become costly, difficult to manage, or unable to support growth.
AI is entering recruitment and workforce planning
Companies are using AI to screen CVs, match candidates to roles, automate outreach, schedule interviews, assess skills, and generate workforce insights. Used well, it can make hiring faster and more consistent, especially in high-volume recruitment environments.
A 2025 field experiment involving around 37,000 applicants found that 54% of candidates assessed through an AI-assisted recruitment pipeline passed the final human interview, compared with 34% of candidates assessed through a traditional pipeline. However, AI does not replace human judgement. Companies still need clear hiring criteria, documented decision-making, oversight and an understanding of how recommendations are generated and reviewed.
Companies are moving into global talent systems
Many companies make the UAE a base for regional and international expansion due to its business-friendly policies and strategic location. Local companies are hiring across borders, global firms are entering the UAE, and leadership teams are being built across multiple jurisdictions. In fact, the cross-border workforce and migration solutions market is projected to reach $11.37 billion by 2033, growing at an annual rate of 11.8%.
For employers, hiring can no longer be treated as a local HR process. Companies must make deliberate decisions about how they enter new markets and engage talent. Some may use an Employer of Record to hire quickly, while others may establish a local entity to gain greater control. In some cases, relocating and sponsoring employees will be the right approach or engaging contractors or building a longer-term market entry structure may be more suitable. Each route carries different implications for cost, compliance, operational control, and future scalability.
Employment models are becoming more hybrid
As companies scale, informal arrangements become harder to manage. A single UAE business may now have locally sponsored employees, remote workers, consultants, contractors, relocating workers, etc. This gives companies more flexibility, but also creates operational risk when obligations are not understood from the start. Worker classification, payroll treatment, benefits, visa eligibility, contract terms, management control, and termination rules can vary depending on how a person is engaged. Employers need clear structures defining employment status, work location, applicable law, and how each relationship is governed.
Regulation is influencing hiring decisions
In the UAE, hiring depends on more than finding the right candidate. Companies need the right regulatory setup before they can move quickly. Licensing gaps, unclear sponsorship routes, incomplete documentation, or a mismatch between the role and the employment structure can still delay a strong hire.
This makes compliance and immigration planning an early hiring priority. Companies should understand the requirements before entering a market, confirming a hire, or committing to a relocation timeline.
Tech Features
Networks Must Evolve Before AI Can Scale
Rohit Chowdhary, Head of Advanced Consulting Services at Nokia, sat down with The Integrator to share insights into the company’s vision for enabling the AI supercycle. He outlined how Nokia’s end-to-end portfolio spans everything from AI-ready connectivity and energy-efficient 800G data centre networking to intelligent, self-optimising home Wi-Fi experiences powered by AI.
A key focus of the discussion was Nokia’s shift from strategic advisory to real-world execution through its dedicated Automation Excellence Practice, helping operators translate ambitious transformation roadmaps into measurable outcomes. The conversation also highlighted the growing importance of integrated, intelligent and secure networks that can support rising AI workloads, eliminate infrastructure bottlenecks and unlock tangible business value, while maintaining the highest standards of security, privacy and resilience
Could you begin by telling us about your role at Nokia and the journey that brought you here?
I lead Nokia’s Advanced Consulting Services business across Europe, the Middle East and Africa. My journey with Nokia spans nearly seventeen years, beginning at a time when consulting was largely focused on network transformation initiatives. Over the years, I have worked closely with operators around the world on transformation programmes, analytics adoption, customer experience management and digital modernization.
As the industry evolved, so did our consulting focus. Following the Nokia and Alcatel Lucent merger, we established what is today known as Advanced Consulting Services. The organization now spans several domains, including security, business monetization, cloud and technology transformation, autonomous operations, and data and AI.
More recently, we launched an Automation Excellence Practice. The idea was simple. Customers often appreciated our strategic blueprints but needed practical expertise to implement them. Today, we have specialized engineers who combine telecom expertise, AI capabilities and software development skills to turn strategic visions into real automation pipelines, AI-driven workflows and production-ready use cases. Our role is to help customers move from concept to measurable business outcomes.
Nokia is often associated with connectivity, but the company is increasingly talking about AI readiness. How does Nokia’s infrastructure portfolio support this transition?
AI is creating what we describe as an AI supercycle. It is transforming everything from data centres and cloud infrastructure to network architectures and edge computing. Supporting this shift requires a complete ecosystem rather than isolated technologies.
Nokia’s portfolio addresses this across multiple layers. On the network side, we continue to innovate in radio technologies, including AI-RAN capabilities developed alongside strategic partners such as Nvidia. We also have a strong optical networking and IP portfolio that enables the high-capacity connectivity required between data centres, edge locations and cloud environments.
One area that excites me is our innovation in data centre networking. We are introducing highly efficient coherent optical technologies and advanced switching platforms that significantly reduce infrastructure footprints while improving performance and energy efficiency. These innovations are becoming increasingly important as organizations invest in AI factories, AI grids and large-scale inference environments.
Beyond connectivity, we also provide intelligent automation layers through our autonomous networking platforms, enabling operators to manage complex, multi-vendor environments more efficiently and intelligently.
What are some of the biggest infrastructure bottlenecks you see operators and enterprises facing as AI adoption accelerates?
One of the biggest challenges is understanding that AI infrastructure is not just about compute power. Organizations often focus heavily on GPUs and processing capabilities, but connectivity can quickly become the limiting factor.
You can deploy the most powerful AI infrastructure available, but if the network cannot support the required data movement between racks, data centres and edge locations, performance suffers. This is where intelligent networking becomes critical.
At Nokia, we are helping customers design what we call AI-ready connectivity. This includes high-capacity optical networking, intelligent routing and the seamless interconnection of compute environments. As AI workloads become increasingly distributed, the ability to move data efficiently becomes just as important as the ability to process it.
On the consumer side, Nokia has been showcasing AI-driven Wi-Fi management capabilities. How does this improve the end-user experience?
The home network has become far more complex than it was a few years ago. Consumers expect flawless connectivity across multiple devices, applications and services.
Our AI-enabled Wi-Fi solutions continuously monitor network performance and user experience. They can identify coverage gaps, detect congestion, analyze interference patterns and even recommend or automatically implement corrective actions.
The goal is to create a self-optimizing network environment where many issues can be resolved autonomously before they impact the user. This reduces support requirements for service providers while delivering a more consistent and reliable experience for customers.
The Middle East is witnessing an unprecedented surge in data centre investments. How do you see this shaping Nokia’s opportunities in the region?
The Middle East has emerged as one of the most dynamic markets globally for AI infrastructure investments. Governments and enterprises are actively investing in sovereign AI capabilities, advanced data centres and digital ecosystems.
This creates significant opportunities, not only for Nokia but for the broader technology industry. The success of these initiatives depends on having secure, scalable and efficient connectivity between compute resources, cloud environments and end users.
Our role is to help customers build these foundations. Whether it is data centre interconnectivity, optical networking, intelligent routing or autonomous operations, Nokia’s technologies are designed to support the scale and performance requirements of AI-driven economies.
As data volumes continue to grow, security and data sovereignty are becoming increasingly important. How is Nokia addressing these concerns?
Security is deeply embedded into Nokia’s strategy and innovation roadmap. As a European technology company, trust, resilience and security have always been fundamental principles in how we design and operate our solutions.
While we continue to invest heavily in AI innovation, we are equally focused on strengthening security capabilities across our portfolio. This includes advanced network security architectures, AI-driven threat detection and preparations for future technologies such as quantum-safe networking.
We are actively engaged with industry bodies, standards organizations and ecosystem partners to help define the next generation of secure digital infrastructure. As AI becomes increasingly pervasive, security must evolve alongside it, and that is an area where Nokia continues to invest significantly.
Looking ahead, what excites you most about the future of AI-driven networks?
What excites me most is the convergence of AI, automation and connectivity. Networks are evolving from passive transport layers into intelligent platforms that can learn, adapt and optimize themselves.
The future will be defined by autonomous operations, AI-native networks and real-time decision-making at scale. Organizations that successfully combine these capabilities will unlock entirely new business models and levels of operational efficiency.
For us, the opportunity is not just about deploying technology. It is about helping customers transform the way they operate, innovate and create value in an increasingly AI-driven world.
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