AI Insights: Ankabut’s Adli Dehelia

Education systems across the region are being reworked around digital platforms, data, and more responsive learning models. AI is increasingly part of that foundation, influencing how institutions deliver teaching, support research, and manage operations. 

“Real AI scale in education is defined by operationalisation across the institution, not isolated use cases,” says Adli Dehelia, Executive Director for Service Development, Ankabut. “It becomes a core capability embedded into learning platforms, research environments, and administrative systems. We are seeing adaptive learning at scale, AI-assisted teaching, and data-led decision making becoming standard practice.” 

In practice, this is showing up in how institutions judge progress. Adaptive learning models are being used across programmes, teaching is supported by AI tools, and decisions are increasingly informed by data. The conversation has turned to what these systems deliver. “The real indicator of scale is measurable impact, whether that is improved student outcomes, more efficient operations, or enhanced research productivity. Institutions that succeed are those that move beyond experimentation and treat AI as a sustained, enterprise-wide capability.” 

Embedding AI into the core 

Getting to that point has required a more deliberate approach. Earlier efforts often remained separate from the systems that run the institution. Many did not extend beyond initial trials. That is changing as AI is brought into platforms that already support learning and research. 

“Rather than running isolated pilots, they are integrating AI into core platforms such as learning management systems and research infrastructure,” he explains. “This shift is supported by stronger governance, clearer ownership between academic and IT functions, and investment in scalable platforms. Many are also adopting a product mindset, where AI capabilities are continuously refined and embedded into day-to-day operations. The transition is less about technology and more about structured execution and accountability.” 

Ownership is becoming clearer between academic teams and IT, and there is more structure around how these systems are managed. The work does not stop at deployment. It is maintained and adjusted over time. 

Infrastructure and access 

As AI becomes part of routine activity, the systems behind it have to keep up. Institutions are investing in cloud environments, connectivity, and platforms that can work together without disruption. Reliability has become as important as capability. 

“Digital infrastructure is fundamental to scaling AI effectively,” he says. “Without resilient, secure, and high-performance platforms, AI cannot deliver consistent or equitable outcomes. Institutions are increasingly investing in cloud-enabled environments, high-speed connectivity, and interoperable systems that support seamless access to AI-driven services.” 

Access comes into focus here. Infrastructure determines whether AI services are available across institutions and student groups in a consistent way. In this region, those investments are closely tied to broader national priorities. 

“Just as importantly, infrastructure plays a key role in addressing access gaps across institutions and student groups. In this region, infrastructure investment is closely linked to national digital agendas, making it both a technical enabler and a strategic priority for inclusive growth.” 

Integration, risk, and security 

As more systems come into place, the risk of fragmentation becomes harder to ignore. Platforms that do not connect well can limit visibility and create inefficiencies. 

“Fragmentation creates inefficiencies, limits scalability, and increases security risks,” he says. “When AI and digital services operate in silos, institutions struggle to generate consistent insights and deliver seamless user experiences. A more effective approach is to build a composable and interoperable ecosystem where systems can integrate and evolve together.” 

Integration also supports governance and long-term scalability, allowing institutions to expand AI capabilities without introducing unnecessary complexity. 

The security environment is becoming more complex at the same time. Education environments are open by design, which makes them more exposed as threats become more targeted. 

“We are seeing more advanced phishing, AI-generated social engineering, and increased targeting of research data and intellectual property,” he says. “The growing number of connected systems also expands the attack surface. This requires a shift towards continuous monitoring, intelligence-led security, and greater awareness across users, supported by strong governance frameworks.” 

Leadership and accountability 

Expectations of leadership are shifting alongside these changes. The CIO’s role now extends beyond infrastructure and operations. 

“AI is fundamentally redefining the role of the CIO from a technology custodian to a strategic transformation leader,” he says. “Today’s CIO is expected to drive innovation, enable data-driven decision-making, and ensure that AI initiatives align with institutional goals. They are also increasingly accountable for governance, security, and ethical AI adoption.” 

The role brings closer engagement with academic leadership and a broader responsibility for how technology shapes institutional outcomes. “In essence, they are becoming orchestrators of a complex digital ecosystem, balancing agility with resilience and innovation with responsibility.” 

What success looks like 

Measures of success are also changing. The scale of deployment carries less weight than the outcomes it produces. 

“Success will be defined by measurable outcomes rather than the scale of deployment,” he says. “Institutions that demonstrate clear improvements in learning outcomes, research impact, and operational efficiency will stand out. Equally important is the ability to scale AI in a responsible and inclusive way, ensuring data protection and alignment with regional priorities.” 

The definition of progress is shifting alongside this. “Ultimately, success will be reflected in how effectively AI contributes to building future-ready talent and advancing the region’s knowledge economy.”