Ayman Idrees is a higher education technology executive and thought leader with more than 26 years of experience leading digital transformation, enterprise modernization, and innovation strategy across U.S. and international institutions. As Chief Information Officer at Wor-Wic Community College, he focuses on reimagining how technology, AI, and digital ecosystems can transform the student experience and drive institutional agility. Prior to Wor-Wic, he served as Executive Director of Digital Transformation at UAE University, helping advance the institution into a Tier-1 research university through large-scale modernization initiatives. His work centers on the intersection of technology, service innovation, AI strategy, and the future of higher education.
Most Higher education institutions are using AI to do the wrong thing better.
They are automating processes, accelerating workflows, and reducing costs. But in doing so, they are optimizing a model that is already losing relevance. The real shift is not about efficiency. It is about experiences.
Across industries, expectations have fundamentally changed. People no longer evaluate organizations based on the services they provide, but on how well those services adapt to their needs, context, and goals. The competitive advantage has shifted from delivering services to designing experiences.
AI has the potential to accelerate this shift but only if leaders use it to rethink the model, not just improve it.
Higher education is undergoing a notable shift in expectations and operating models. This shift is not driven solely by technological advancement, but by changes in how students engage with institutions and how value in education is perceived.
For many years, colleges and universities have been organized around the delivery of services, including instruction, advising, enrollment management, and student support. These services have historically been optimized for efficiency, scale, and consistency, often structured around linear academic pathways. This model aligned with institutional priorities of access expansion and standardized delivery. However, evolving student expectations are challenging the sufficiency of this approach.
Building on the conceptual foundation of the experience economy introduced by B. Joseph Pine II and James H. Gilmore, institutions are increasingly moving toward the intentional design of experiences rather than discrete services. In this framing, value is created not only through what
institutions provide, but through how students engage with, interpret, and progress through their educational journey.
This shift is closely associated with the growing recognition that student pathways are no longer linear. Students frequently navigate higher education while balancing employment, family obligations, financial constraints, and changing academic or career goals. As a result, enrollment patterns are more fluid, and engagement with institutional services occurs across multiple channels and timeframes.
In this context, the distinction between services and experiences becomes operationally significant. Services are typically transactional and episodic in nature. Experiences, by contrast, are intentionally designed and continuously shaped across multiple institutional touchpoints. They are contextual, adaptive, and influenced by how students interact with the institution over time. Each interaction, whether digital or in person, contributes to a cumulative perception of institutional value and support for student success.
Artificial intelligence and modern data platforms are increasingly central to enabling this transition. Without these capabilities, personalization at scale remains limited by structural and operational constraints. AI-driven systems allow institutions to analyze patterns of engagement, anticipate student needs, and deliver more timely and context-aware interventions. When combined with integrated data ecosystems, these capabilities support more coordinated and adaptive institutional responses.
At the operational level, this shift can be observed in student support services. Traditionally, IT support models have been structured around ticket-based systems in which users submit requests and await resolution. While effective in managing workload and tracking service delivery, this model reflects a transactional service orientation. The introduction of real-time support channels, self-service tools, and integrated communication platforms has begun to alter this dynamic. These changes not only improve response efficiency but also shift expectations toward continuous and interactive engagement.
This evolution has broader implications for institutional technology leadership. The CIO role is increasingly evolving from operational system stewardship to strategic experience design. The challenge is no longer simply implementing systems but designing the digital ecosystem that shapes how students experience the institution. This requires a shift from thinking in terms of systems and functions to thinking in terms of journeys and outcomes.
Three priorities emerge.
First, institutions must move from fragmented systems to connected ecosystems. Student experiences break down when data and processes are siloed across admissions, academic systems, and support services. Integration becomes a strategic requirement, not just a technical one.
Second, data must become a trusted, shared foundation. Personalization depends on a holistic understanding of the student, not isolated transactions. This requires strong governance, data quality, and clarity around how data is used.
Third, AI must be applied with intent. Not every process needs automation. The focus should be on moments that matter, where timely intervention, personalization, or anticipation can significantly improve student outcomes.
A further development in this evolution is the movement from personalization toward co-creation. In this model, students are not solely recipients of institutional services but active participants in shaping their learning pathways. This requires systems that support flexibility, modularity, and responsiveness to individual academic and professional trajectories.
However, the adoption of these approaches also introduces important considerations. Issues related to equity, access, data privacy, and algorithmic transparency must be addressed to ensure that technological advancement does not inadvertently reinforce existing disparities. The effectiveness of AI-enabled systems in higher education is therefore closely linked to the strength of institutional governance and ethical oversight.
The conversation around artificial intelligence in higher education often focuses on tools and capabilities. However, the more important question is what these tools are enabling. If the goal is efficiency, institutions will optimize existing models. If the goal is impact, they must reimagine the model entirely.
Institutions that are likely to lead in this environment are those that move beyond incremental improvement and toward intentional experience design. In this context, technology serves not as an end in itself, but as an enabler of more adaptive, responsive, and student-centered educational ecosystems. The implications extend beyond operational effectiveness to the broader question of how institutions define and deliver educational value in an evolving digital landscape.
AI is not the transformation.
The student experience is.