AI Insights: NTT DATA’s Haider Amjed

Across the Middle East, large-scale transformation programmes are moving from strategy into execution. Investments in infrastructure, digital services, and national platforms are beginning to show up in day-to-day operations. Expectations are shifting toward delivery, consistency, and measurable impact. 

Enterprise systems are being pushed in ways they were not designed for even a few years ago. Growth now carries layers of complexity across markets, functions, and regulatory environments. Reliability becomes the real test, with systems expected to perform without interruption even as conditions change. 

“Real scaling in AI is no longer defined by the number of pilots or proofs of concept an organisation runs. It is defined by how deeply AI is embedded into core operations. In practice, this means AI becomes an operating layer that influences decisions in real time across supply chains, customer engagement, risk management, and back-office processes,” says Haider Amjed, Head of Technology for UAE, NTT Data. 

National programmes such as Saudi Vision 2030 and UAE AI Strategy are accelerating adoption, that level of integration is tied closely to outcomes. Organisations are expected to show how these systems contribute to revenue, efficiency, and service delivery within already complex environments. 

“In the Middle East, organisations that are scaling effectively treat AI as enterprise infrastructure that is standardised, governed, and integrated rather than as fragmented initiatives,” explains Amjed. 

Building for consistency 

Embedding AI into operations demands a different approach to how systems are built. Data needs to move without delay, models must perform consistently across environments, and governance has to be part of the design process rather than an afterthought. Teams that once operated separately are working in closer alignment as systems begin to span functions. 

“From NTT DATA’s perspective, real scaling is achieved when AI capabilities are industrialised through repeatable frameworks, supported by strong data foundations, and aligned with clear business outcomes. It is about moving from experimentation to execution at pace and at scale. A key differentiator is the shift toward platform-based thinking by building reusable AI services and components that can scale across use cases,” he says. 

The pattern is clearer in sectors where operations depend on precision. In energy, banking, and public services, systems run continuously and decisions carry immediate impact, leaving little tolerance for inconsistency. 

“In these sectors, AI is being embedded into predictive maintenance, intelligent mobility systems, digital government services, and real time financial risk management,” Amjed says. “Leading organisations are investing in unified and cloud enabled data platforms that eliminate silos and enable real time access. They are establishing AI governance models that balance innovation with control and creating cross functional operating models that integrate business and technology teams.” 

Designing within constraints 

Running systems at scale brings practical limits into view. Cost, energy consumption, and infrastructure demands begin to shape decisions, influencing how AI is designed and where it is applied. 

“Scaling AI without considering cost and sustainability is no longer viable. The computational demands of advanced models combined with rising energy costs are forcing organisations to rethink how AI is designed, deployed, and managed. In the Middle East, where sustainability is aligned with national priorities and net zero commitments, organisations are focusing on efficiency,” he says. 

Design choices are becoming more deliberate, with greater focus on where complexity adds value, how workloads are distributed, and how systems can be adjusted without starting over. 

“This includes optimising model architectures, using smaller or domain specific models where appropriate, and deploying workloads across hybrid environments to balance performance and cost. Responsible scaling also requires embedding sustainability into governance frameworks by tracking energy consumption, optimising data centre usage, and aligning AI initiatives with broader environmental goals,” Amjed adds. 

Control is increasingly tied to where and how systems operate. As organisations work across jurisdictions, data handling, infrastructure, and compliance requirements shape architecture from the outset. 

“Sovereign AI is becoming a defining factor in how organisations approach AI adoption, particularly in the Middle East where data sovereignty and national digital strategies are critical. It reflects the need to maintain control over data, infrastructure, and algorithms within defined jurisdictional boundaries,” he says. 

This is shaping enterprise decisions across the stack. Organisations are re-evaluating where data sits, how models are trained, and which partners they work with. Compliance is being built into the design, creating trusted environments where sensitive data can be used securely. 

“We see sovereign AI as an enabler of growth. By aligning AI strategies with local regulations and building architectures that support sovereignty, organisations can accelerate adoption while maintaining trust and compliance,” explains Amjed. 

Holding up under pressure 

As AI becomes part of critical operations, resilience is no longer separate from performance. Disruptions in data, models, or infrastructure directly affect business continuity, making system stability a central concern. 

“Resilience in the age of AI extends beyond traditional technology considerations. Organisations need a holistic approach to resilience, including scalable infrastructure, data integrity, and continuous monitoring and validation of models,” he says. 

The way systems respond to failure becomes as important as how they perform under normal conditions. Redundancy, fallback mechanisms, and human oversight are being built into the operating model. “This is particularly important in sectors such as energy, healthcare, and public services where continuity is essential,” Amjed adds. 

Expectations around leadership are shifting alongside these changes, with greater emphasis on ensuring systems remain aligned with business objectives and can operate at scale over time. 

“CIOs and CTOs are becoming architects of intelligent enterprises, responsible for driving transformation, aligning technology with business outcomes, and ensuring responsible use of AI. In the Middle East, leaders are expected to move faster while managing increasing complexity, requiring strong data literacy, governance, and the ability to operationalise innovation,” Amjed says.