Student expectations are rising, budgets are tightening, and traditional support models are struggling to keep up. In this session, we explore how further and higher education institutions are rethinking student support, moving from fragmented contact centres to connected, AI-enabled service models.
You’ll see how Microsoft technologies are being used to unify data, automate routine queries, and deliver faster, more personalised support across the entire student journey, without losing the human touch where it matters most.
You’ll also hear practical transformation stories from programmes including Nottingham College, alongside insights from our expert panel:
We hope you enjoyed this webinar. Please feel free to read and download the transcript below or get in touch if you have any questions about our exploration sessions.
Setting the Context: Why Student Support Must Change
The discussion opened by reframing contact centres not as technology platforms, but as student service operating models designed to solve institutional challenges.
Across further and higher education, institutions are shifting focus from recruitment and delivery to retention, continuation, and student outcomes.
Three key pressures are driving this:
Current models are often fragmented, with disconnected channels, limited visibility, and no consistent triage, particularly under peak demand such as enrolment.
The core issue is not just technical, but behavioural: when students cannot access support easily, they disengage or drop out.
From Contact Centre to Student Service Model
Modern student support must shift from reactive communication to proactive lifecycle engagement.
Rather than simply handling queries, institutions should support students across:
The real value comes from improving routing, reducing friction, and enabling faster resolution, not by increasing staffing levels, but by redesigning the operating model.
Technology is an enabler, not the starting point.
Microsoft’s Unified Platform Approach
The Microsoft perspective focused on delivering student support through a single, connected platform, enabling institutions to meet students where they are.
Using Microsoft technologies including:
Institutions can:
Key principle: AI is only as strong as the data behind it. Fragmented systems limit performance and increase cost.
Many universities currently spend heavily on maintaining disconnected systems rather than investing in innovation.
What Good Looks Like: A Connected Student Journey
A modern model connects every touchpoint into one ecosystem:
This reduces complexity and enables:
Importantly, institutions do not need to rip and replace existing systems, integration can sit on top of current infrastructure.
Live Demonstration Highlights
A live demo illustrated how a student query might flow through the system:
If the student cannot attend:
For complex issues (e.g. financial difficulty):
The system also demonstrated:
Human + AI Collaboration
A key theme was the balance between automation and safeguarding.
AI should not independently handle sensitive areas such as:
Instead, systems must:
This ensures both safety and trust in AI-enabled services.
Knowledge, Data, and AI Readiness
A key challenge is keeping knowledge bases accurate and current.
Approaches discussed included:
This avoids outdated or inconsistent responses and improves system reliability.
The importance of a single source of truth (“golden record”) was emphasised to support personalisation and accurate routing.
Personalisation and System Integration
Personalisation is enabled through:
Once data is unified, institutions can:
Q&A Highlights
Q: How is personalisation achieved?
Personalisation depends on unified data and a system of truth. Once student identity is resolved, systems can dynamically tailor responses and journeys.
Q: How do we keep knowledge up to date?
Knowledge can be:
Q: Do we need digital workers or agents?
AI agents can now handle orchestration more directly, reducing reliance on traditional Power Automate workflows. However, the right architecture depends on cost, scale, and organisational maturity.
Q: What about AI limitations and risk?
Sensitive scenarios must always be routed to humans. AI should be constrained through rules and escalation logic to avoid inappropriate responses.
Final Thoughts
The webinar concluded that institutions are moving towards a hybrid model of AI + human service delivery, where:
Successful adoption depends not on full automation, but on careful design, governance, and phased implementation aligned to institutional priorities.
We hope you enjoyed this webinar. Please feel free to explore our other videos or get in touch if you have any questions about our exploration sessions.