Great expectations: Rethinking student support for the AI age

Students’ expectations for services and support are shifting quickly. Their benchmarks are no longer defined by traditional higher education models, but by the immediacy and usability of consumer technology across sectors. The rapid adoption of AI in everyday life is accelerating this change, setting new norms for digital, integrated, personalised, and rapid support. Consumers demonstrate a strong preference for self‑service, personalisation and speed. A Gartner survey found that 38 per cent of Gen Z and millennial customers would give up on resolving a service issue if self-service is unavailable. McKinsey research found 71 per cent of consumers now expect highly personalised interactions throughout the customer journey.

This evolution places new pressures (and new possibilities) on student services provided by universities: the systems, supports, and touchpoints that enable students to thrive academically, personally, and professionally. 

Today’s students expect support that is fast, personalized, continuously available, and seamlessly integrated across channels. Students want to solve problems quickly, on their own terms, through the channel of their choice. They expect to be seen, known, and supported. 

AI is rapidly changing how students access information, receive support, and make decisions. But for universities, the disruption goes beyond faster processes or automation. It fundamentally alters how value is created and delivered across the student experience. 

Yet many universities are playing catch-up, operating with systems designed for a different era. This disconnect poses a significant strategic risk. Universities that fail to adapt risk delivering fragmented, transactional experiences that fall short of expectations shaped by AI elsewhere in students’ lives. AI capabilities already embedded in student-support platforms now make hyper-personalized nudges, predictive risk alerts, and round-the-clock conversational support routine, and the pace of improvement is accelerating. Universities that remain tied to legacy systems will quickly appear slow, generic, and out of step.

This gap is also an opportunity: those that deliver coherent, responsive, and personalized services stand to gain in student satisfaction, reputation, retention, and long-term trust. Those who act decisively will be able to effectively reposition student services as a strategic platform for connection, belonging, and progression in an AI-transformed world. Leaders in this space will offer a more seamless and personalized student experience and stand out in an increasingly competitive market in which experience shapes choice. This is especially important as funding models come under pressure from shifting international student flows, and as employers place greater emphasis on skills over degrees. 

This divergence between risk and opportunity highlights a critical choice. Institutions can continue to react to disruption with piecemeal fixes, or they can use AI as a platform to reposition student services for long-term advantage. The universities moving ahead are not only addressing today’s service pressures but also laying the groundwork for new models of connection, belonging, and progression. To keep up, and to differentiate themselves in an era of AI-transformed student services, institutions should focus on three priorities:

• Be agile and deliver incremental improvements in the short term
• Rethink the student services model from the ground up
• Prepare for continual evolution through a forward-looking design agenda

AI is already reshaping student services at pace. Rather than waiting for a perfect solution, the most effective approach is to start somewhere with the tools already at hand. By applying conversational assistants, predictive analytics, and workflow automation in focused areas, institutions can deliver small wins, test what does and doesn’t work, and learn in a low-risk, low-cost environment. These early gains will ease service pressures and build the evidence base for more ambitious changes. Institutions should be pragmatic and leverage opportunities to use AI tools and platforms already available to improve efficiency and responsiveness in student services today.

We are already seeing this shift towards incremental improvements in our work with clients. At one university, faculty faced student frustration over difficulty interpreting complex academic regulations. Put another way, they struggled to access timely, clear information to support their progress. Rather than have students navigate dozens of dense policy documents, the university used already available tools to add a conversational AI layer to course and program regulations allowing students to ask questions about program credits, possible transfers and extensions in plain language, and receive tailored answers. This incremental improvement increased clarity and access without requiring structural overhaul, and it has also begun to shift the university’s mindset by demonstrating what becomes possible once initial success is achieved.

Institutions are already piloting AI-driven support tools. While such early deployments demonstrate AI’s potential to enhance service delivery, they often expose weaknesses embedded in current support models. Introducing AI into legacy systems can magnify persistent problems, such as siloed service delivery, ambiguous ownership, and inconsistent user experiences. AI solutions can surface inconsistent handoffs between automated systems and human staff, fragmented data management, and limited integration across multiple service channels. For universities at the beginning of their AI journey, investing early in data readiness and service coordination is the essential first step. Without more integrated design, AI risks amplifying dysfunction. 

Universities should view early deployments as important steps, but the bigger opportunity lies in treating AI as a catalyst for rethinking student services from the ground up.

Georgia State University’s “Pounce” chatbot illustrates this. It began with enrolment-stage interventions like nudging students to complete financial aid and registration tasks but was later expanded into large first-year courses with high drop-out rates. In these courses, the chatbot sends personalised reminders about assignments and deadlines, flags risks of disengagement, and offers encouragement. This is not simply layering a tool onto teaching; it redefines how the institution monitors student progress and delivers academic support in real-time. 

The next phase requires reimagining how services are designed, governed, and delivered, with AI embedded as a foundational operating context rather than an optional overlay. Over the next decade, AI will drive student services toward hyper-personalisation, round-the-clock responsiveness, and more anticipatory design. Institutions will be expected to deliver support that feels seamless and human, even when machine led. 

Designing for an AI-enabled future is not a one-time shift. Because models, tools, and student expectations evolve quickly, leaders must reimagine how services are structured and experienced through ongoing iteration and adaptation. This requires adopting an agile, iterative approach that centres on human experience, continuous learning, and a commitment to improvement through feedback. A forward-looking design agenda includes:

• Designing journeys that AI cannot fragment. Ensure that AI interactions connect seamlessly across enrolment, academic, wellness, and career supports. Prevent duplication or conflicting advice by structuring services around the student journey rather than the organisational chart.
• Building ecosystems where AI is one channel among many. Position AI assistants alongside self-service portals, human staff, and peer supports. Design so that these channels reinforce one another and allow easy movement between machine-led and human-led interactions.
• Creating adaptive pathways with guardrails. Use AI to tailor nudges, resources, and schedules in real time, but embed escalation, transparency, and fairness. Keep services compassionate and consistent while still benefiting from personalisation at scale.
• Embedding ethical intelligence into design choices. Develop clear, transparent standards for data use, consent, and explainability. Proactively engage students in setting ethical boundaries so that AI-driven services earn and sustain trust.
• Preserving human value. Deploy AI to improve service and preserve the emotional texture of student support. Institutions must remain human-centred in how they foster belonging, making empathy and connection core to digital and physical experiences.
• Establishing structured feedback and improvement mechanisms. Measure impact, capture feedback from students and staff, and act on insights quickly. Make feedback and rapid response a routine part of service delivery so student support remains relevant and effective.

These shifts represent a fundamental redefinition of how institutions create value in students’ lives, requiring continuous, human-centred design and agile adaptation, with AI as a foundational enabler rather than an add-on.

This new chapter places AI-related decisions squarely with institutional leaders. The question is not, “What is our AI strategy for student services?” but “How must we rethink service delivery to realise the transformative benefit of AI?”

This distinction matters. Institutions that treat AI as a standalone strategy may invest in tools but leave underlying service models untouched. They risk duplicating effort, confusing students, and reinforcing silos. A chatbot layered onto a fragmented advising system does not solve the experience gap, it amplifies it.

To avoid this trap, leaders must:

• Reposition student services for a higher bar of differentiation. Many institutions already frame student services as a differentiator, but AI redefines what competitive differentiation looks like. Services once considered leading (such as 24/7 support or automated reminders) are becoming baseline expectations. Institutions must now ask how their AI-enabled services are distinctive, aligned to their mission, and harder to replicate.
• Take ownership of trust and governance. Many institutions are delegating “AI strategy” to IT, innovation offices, or digital units. But decisions about trust, security, and privacy (e.g. how student data is used, how bias is managed, and how transparency is assured) must sit at the executive table. Senior leadership is critical to set institutional guardrails, align investments, and reassure students and staff that AI will be used responsibly.
• Think institutionally, not functionally. Services must be coordinated across units and touchpoints to support full student journeys, not delivered through disconnected systems. Leaders must break through silos and develop a coordinated vision for how AI supports the full student journey.
• Build capacity for AI-enabled service transformation. Enabling transformation requires investment in workforce capacity, capability, and new roles at the intersection of service design, data ethics, and student success. Adoption is equally vital. Leaders must invest in change management to ensure staff and students use, trust, and benefit from AI-enabled services.
• Stay anchored in values. Student services automation is not new, but AI has the potential to increasingly shape decisions, content, and proactive outreach. Leaders must ensure these capabilities remain human-centred, inclusive, and aligned with institutional values of trust and belonging. 

The next phase of student experience will be designed, not deployed. AI is not merely a new technology, but also, as we have written before, a new context.  For student services, this means going beyond digital upgrades to a wholesale reimagining of purpose, delivery, and leadership. Institutions that lead this reinvention will come to define student expectations. To succeed, student services in the AI age must be deliberately built to be more connected, more responsive, and more human than what came before.

Get in touch to discuss how your university can embrace AI to more effectively meet student expectations.

Connect with Aryeh Ansel and Dylan Houghton on LinkedIn.

This is the second article in our series on university strategy in the age of AI. You can read the first part here.