New research reveals the state of AI readiness in higher education

New Forrester research, commissioned by TechnologyOne, has surveyed more than 300 education leaders across Australia and New Zealand. The results have found AI ambition running well ahead of institutional readiness.

Only 21 per cent of education leaders believe their current systems can effectively support AI-driven capabilities. Investment tells a different story, as AI spending continues to accelerate across universities, TAFE and further education providers.

The urgency is understandable. Education leaders rank maintaining financial stability and sustainability (87 per cent), improving student experience (86 per cent) and strengthening recruitment and enrolment outcomes (83 per cent) as their top institutional priorities. They increasingly view AI as an enabler of all three.

The promise of AI collides with daily reality, though. Routine administrative tasks consume staff capacity at 58 per cent of institutions, and the systems those staff rely on were rarely designed for the intelligence now being layered on top of them.

The research points to four foundations that separate the institutions ready to scale AI from those still in pilot mode: connected data, modern platforms, clear governance and workforce capability. Each foundation offers a practical benchmark for leaders wanting to know where their institution stands.

Connected data

AI is only as good as the data beneath it, and the education leaders were candid about their starting point. Disconnected student data limits proactive, personalised support at 47 per cent of institutions, while limited real-time visibility restricts student support at a further 45 per cent.

The fix is not perfect data. A more realistic foundation is data that is connected, well governed and able to improve over time.

AI will inherit the data foundation that staff are currently working with. By connecting student data first, every new AI capability will draw on that same foundation. Otherwise, adding AI on top of disconnected systems will simply amplify the fragmentation.

Modern platforms

Only 10 per cent of institutions report having fully integrated core administrative and student management systems. Most are still relying on middleware, batch processes and manual workarounds. The required level of modernisation is slowed by budget constraints (49 per cent), staff capacity (46 per cent) and persistent data, customisation and integration issues (38 per cent each).

Faced with that complexity, many institutions opt for incremental extensions to legacy systems. It’s an understandable choice that reduces near-term risk, but it rarely delivers the adaptability AI needs at scale. Leaders know what modernisation should deliver instead:

• improved data visibility (59 per cent)
• a single unified view of each student (54 per cent)
• the ability to proactively identify and support at-risk students (53 per cent).

The test here is whether new AI capabilities can work inside core platforms and everyday workflows to improve productivity and decision making. If the AI tool sits outside workflows as another isolated tool, it becomes just another pilot.

Clear governance

Education leaders are right to approach this type of transformation with caution. Institutions hold years of personal, academic and financial information on every student, and the regulatory and reputational stakes of getting AI wrong are significant.

Only 12 per cent of institutions report having an integrated, cross-functional operating model with joint accountability for student outcomes, which leaves many AI decisions without a clear owner. Clear governance converts caution into confidence by defining

• who owns those decisions
• how models are monitored
• how compliance obligations are met before deployment scales.

This level of caution is also shaping buying decisions. When selecting technology partners, institutions anchor on strong cybersecurity and data protection (48 per cent), local regulatory compliance and understanding of education sector requirements (44 per cent), and seamless integration with existing systems (43 per cent).

Benchmarking governance and security posture ultimately comes down to speed and clarity. If an institution can’t approve a new AI use case tomorrow – with a documented path from proposal to production – the technology isn’t the blocker. The governance is.

Workforce capability

Fewer than one in three education leaders (29 per cent) believe their institution has the skills and resources needed to fully leverage existing system capabilities.

However, skills gaps shouldn’t delay AI programs needlessly. Institutions can build workforce capability in parallel with platform modernisation, with new in-house skillsets working as an accelerator once modern platforms are up and running.

Rather than benchmarking capabilities against current in-house skills, it’s more productive to focus on the alignment between skills development and the AI roadmap. To ensure they aren’t an afterthought of AI adoption, workforce development and change management plans should have the same level of visibility and accountability.

The AI dividend for higher education

The reward for strong foundations is substantial. More than half of education leaders (53 per cent) expect AI embedded in core platforms to lift administrative productivity by 60 per cent.

Almost two-thirds (65 per cent) expect similar gains in the speed of identifying at-risk students. Expectations of core system transformation run higher still, with 83 per cent anticipating significant improvements in proactive support for at-risk students.

The greatest potential sits in the areas closest to students:

• identifying those at risk of disengaging (51 per cent)
• managing student self-service enquiries (40 per cent)
• streamlining admissions processing (38 per cent).

These are the interactions that set the rhythm of campus life, which is why gains here translate directly into student experience. To achieve these gains, AI readiness becomes a measure of whether data, platforms and governance can scale an AI pilot into an everyday capability that positively impacts students.

AI readiness can be built

None of these four foundations requires perfection before AI starts delivering value. The research reveals that institutions best placed to scale AI are building platforms, data, governance and capability in parallel, rather than waiting for ideal conditions to arrive.

The question is no longer whether AI belongs in higher education. It’s which of these foundations needs attention first, and how quickly it can be strengthened.

Download the full Forrester report, Reshaping Student Success Through AI In ANZ Tertiary Education, to explore the findings and benchmark your institution’s AI readiness.

Do you have an idea for a story?
Email rcox@intermedia.com.au