The Role of AI in System Review, ‎Change, and Development of ‎Performance and Reward Management ‎in Australian Higher Education

Artificial Intelligence (AI) is rapidly transforming organisational systems, with higher education institutions in Australia standing at a critical juncture. Performance and reward management systems, historically shaped by compliance, policy, and traditional HR frameworks, are now being challenged by the demand for greater agility, transparency, and personalisation.

This paper explores the strategic role of AI in reviewing existing systems, driving change, and developing future-focused performance and reward management frameworks. Importantly, it examines how the Enterprise Bargaining Agreement (EBA)—a defining feature of the industrial relations landscape in Australian universities—both constrains and enables the role of AI in performance and reward reform.

The Australian higher education sector is facing unprecedented pressures: global competition, regulatory compliance, funding constraints, and changing workforce expectations. Traditional performance management systems—often criticised for being bureaucratic, inflexible, and retrospective—are misaligned with the needs of a dynamic academic and professional workforce.

AI offers new capabilities for system review, change enablement, and development of innovative frameworks, giving universities new tools to enhance fairness, responsiveness, and alignment with institutional priorities.

AI and Performance Management and Reward - Australian Higher Education Sector

The Case for AI in Performance and Reward Management

There are several drivers behind the growing case for AI adoption in performance and reward management. Academic work has become increasingly complex, spanning teaching, research, administration, and community engagement. AI can model and analyse these dimensions with greater sophistication, offering institutions deeper insight into how academic contributions are made.

There is also a pressing need for equity and fairness. AI tools can help reduce unconscious bias in promotion and reward processes by applying consistent data-driven criteria. At the same time, staff across the sector expect greater clarity in how performance is assessed and rewarded. AI-powered dashboards and real-time feedback loops can help meet these expectations for transparency.

Retention and engagement of academic staff have also become more critical as universities compete globally for talent. AI-informed systems can provide personalised career pathways and development opportunities, aligning institutional goals with individual aspirations.

AI in System Review

AI can support a comprehensive review of existing performance and reward systems. It is capable of uncovering redundancies in processes, inconsistencies in performance measurement across faculties and units, and gaps in recognising contributions such as pastoral care or interdisciplinary collaboration.

Advanced analytics and natural language processing make it possible to review large datasets, policy documents, and staff feedback at scale. These insights can inform evidence-based reforms that better reflect the diversity of academic and professional contributions within the sector.

The Role of Enterprise Bargaining Agreements (EBAs)

Enterprise Bargaining Agreements are central to shaping employment conditions, performance management, and reward structures in Australian universities. Negotiated between institutions and unions, EBAs define pay, workload allocation, career progression, dispute resolution, and professional development frameworks. They are the backbone of employment relations in the sector and thus cannot be overlooked in any discussion of performance management reform.

At the same time, EBAs can constrain how AI is applied. Because EBAs often mandate uniform processes across faculties, they may restrict flexibility for AI-driven personalisation. Workload models are codified into standardised formulas that limit adaptive performance measures. Similarly, pay and reward structures are tightly linked to collective agreements, leaving little scope for purely individualised, AI-driven recognition.

However, AI also creates opportunities within the EBA framework. Evidence-based insights can help inform negotiations, particularly around issues such as workload equity, career progression, and pay disparities. AI can also assist institutions in monitoring compliance with EBAs, ensuring workload allocation and reward systems remain fair and transparent.

Moreover, predictive analytics can support innovation by helping universities and unions anticipate future workforce needs and design EBAs that align with long-term sectoral trends.

AI as a Catalyst for Change

AI has the potential to act as a catalyst for change, but it cannot drive reform in isolation. For successful adoption, it must be embedded within governance structures and supported by cultural change initiatives. Academic leaders need to retain decision-making authority to preserve professional judgement, ensuring AI remains a tool rather than a replacement for human oversight.

For staff, trust in AI-enabled systems must be built through transparency, consultation, and training. Importantly, AI-enabled performance management must remain compliant with EBAs, while also providing valuable data that can inform future negotiations and industrial relations strategies.

Developing AI-Enabled Performance and Reward Frameworks

The future of performance and reward management in higher education will require systems that are flexible, fair, and future ready. AI can play a significant role in shaping such frameworks. By enabling the collection and analysis of holistic performance metrics that recognise teaching quality, research impact, engagement, and innovation, AI can provide a more accurate and balanced view of staff contributions.

Reward systems may also evolve to become more dynamic, offering recognition for achievements in real time rather than relying solely on incremental pay increases. Benchmarking tools powered by AI could allow institutions to compare their practices with national and global standards. At the same time, adaptive systems could help recalibrate metrics and policies in response to sectoral change.

Risks and Considerations

While AI offers significant opportunities, it also presents risks that must be carefully managed. Algorithms may entrench bias if they are trained on flawed datasets. Transparency will be essential, as staff and unions will demand to understand how AI-informed decisions are made. Privacy and security are also critical considerations, given the sensitivity of staff performance data.

From an industrial relations perspective, misalignment between AI-enabled systems and EBAs could create disputes, highlighting the need for close engagement with unions and careful compliance with negotiated agreements.

Governance and Policy Recommendations

To integrate AI effectively into performance and reward management, higher education institutions should establish robust governance frameworks that include oversight of compliance with EBAs. Cross-functional task forces involving HR, IT, academic leaders, compliance officers, and unions should be established to guide implementation.

Institutions should also leverage AI insights to inform EBA negotiations, ensuring that agreements reflect workforce realities and future trends. Pilot programs, developed in partnership with unions, provide a practical pathway for testing AI-enabled systems before wider deployment. Transparent communication with staff will be essential for building trust and ensuring successful adoption.

AI has the potential to reshape performance and reward management in Australian higher education by enhancing transparency, fairness, and agility. Its role in system review, change, and framework development offers universities an unprecedented opportunity to align institutional goals with staff aspirations.

Success will depend on strong governance, human oversight, and cultural readiness. By working within the framework of EBAs and using AI to inform bargaining and workforce planning, universities can transform performance management from a compliance-driven obligation into a strategic enabler of academic excellence and workforce sustainability.