Artificial intelligence is rapidly reshaping how universities manage data, plan resources, and make financial decisions. But as AI adoption accelerates, so too does the need for strong governance, transparency, and trust.

In a recent Pilbara Group webinar, industry leaders from Intelligen, Snowflake, and Pilbara Group came together to explore what responsible, governed AI looks like in the context of higher education financial management—and how institutions can move forward with confidence.

Why AI Governance Matters More Than Ever

AI promises faster insights, better forecasting, and improved decision-making. Yet without proper guardrails, it can also introduce significant risk—from poor data quality and opaque models to hallucinated results and compliance issues.

A recurring theme throughout the webinar was clear: AI should not replace human judgment—it should augment it, with humans firmly “in the loop.” Governance is not a brake on innovation; it is the enabler that makes AI scalable, ethical, and trustworthy in regulated environments like higher education.

Laying the Foundations: Strategy Before Technology

Shaji Obeidullah, Head of AI and CX at Intelligen, emphasised that successful AI programs start with strategy—not tools. Too many organisations adopt “cool technology” first and attempt to retrofit strategy later, often with disappointing ROI.

Instead, Intelligen advocates:

• Strategy-led AI initiatives, aligned to institutional objectives (cost, revenue, or service outcomes)
• Early and proactive governance conversations, particularly in regulated sectors
• A strong data foundation, supported by analytics and AI to deliver measurable value

This approach ensures AI initiatives are purposeful, compliant, and sustainable.

Right-Sizing Data Governance and Quality

Effective governance is not one-size-fits-all. It must be proportionate to an institution’s maturity, risk appetite, and complexity.

Key governance principles discussed included:

• Strategic oversight using recognised standards
• Lifecycle governance across data creation, use, retention, and disposal
• Risk-proportionate controls based on sensitivity and criticality
• Responsible-by-design AI, embedding accountability and transparency
• Integration with existing business-as-usual processes to reduce friction

Importantly, data quality was framed as an ongoing discipline, not a one-off exercise. Trust in AI depends on consistent, explainable, and resilient data pipelines that can withstand audit, regulatory change, and operational disruption.

Ethics, Explainability, and Accountability in AI

AI ethics was another central focus. Rather than abstract principles, the discussion centred on practical mechanisms institutions can implement today:

• AI ethics charters aligned with recognised standards (such as government and UNESCO principles)
• Ethical impact assessments during design—not after deployment
• Explainable models that finance leaders can understand and defend
• Clear documentation of assumptions, inputs, and outputs
• Ongoing monitoring to detect bias, drift, or unintended outcomes

Accountability was reinforced through the idea of explicit ownership:

• Who owns the data?
• Who owns the model?
• Who owns the decisions informed by AI?

In many cases, responsibility ultimately sits with finance leadership, supported by cross-functional AI governance committees.

Making AI Safe and Accessible with Snowflake Intelligence

Harish Suresh, Senior Partner Solutions Engineer from Snowflake introduced how Snowflake is approaching agentic AI—AI systems that can answer questions directly from enterprise data using natural language.

A major challenge with generative AI is hallucination: confidently producing answers that are wrong or unverifiable. Snowflake addresses this through built-in governance features, including:

• Role-based access controls inherited directly from the data platform
• Transparent “show your working” reasoning in AI responses
• Grounding answers in verified data sources with citations
• Verified query repositories curated by subject matter experts
• Explicit instructions that allow AI to say “I don’t know” when data is missing

The result is AI that behaves more like a trusted analyst—and less like an unreliable oracle.

From Theory to Practice: Pilbara Benchmarks and Pilbara Intelligence

Adam Gallard, Chief Product Officer at Pilbara Group, demonstrated how these governance principles are being applied in practice through the Pilbara Benchmark Portal and Pilbara Intelligence.

The benchmark portal enables universities to:

• Securely compare their cost and activity metrics against the sector
• Control access using institutional single sign-on and row-level security
• Explore trends, outliers, and performance drivers across disciplines
• Interrogate benchmarking data using natural language queries powered by Snowflake Intelligence

Crucially, AI is not used as a black box. Users can see how conclusions are reached, what data was used, and how their institution compares—reinforcing trust and accountability.

Building Capability and Culture

Technology alone is not enough. The webinar closed with a strong emphasis on organisational readiness:

• AI and data literacy training tailored to different roles
• Cross-functional collaboration between finance, IT, and analytics
• Clear change management and communication strategies
• Success stories and incentives that encourage responsible adoption

The message was clear: AI maturity is as much about people and culture as it is about platforms.

Looking Ahead

AI is already transforming financial management in higher education—but its long-term success will depend on governance done well. Institutions that invest early in strategy, ethics, data quality, and accountability will be best placed to unlock AI’s benefits without compromising trust.

As this webinar demonstrated, governing AI is not about slowing down innovation. It’s about ensuring AI becomes a reliable, explainable, and enduring capability for the sector.

Watch the full Webinar Here: