Written by Dr Craig Hansen, New Zealand AI education expert, Ministry of Education PLD & AI Facilitator, and founder of the Summit Institute. This article provides executive reflection and context on managing AI strategy within higher education and tertiary institutions.
Leading a tertiary institution in higher education, I frequently grapple with the intense strategic tension between rapid technological disruption and institutional policy. The pace of generative AI development challenges our legacy structures, pulling our governance boards, academic registries, and faculty leaders into uncharted waters.
Recently, I read and digested a remarkable strategic white paper published by Elsevier (2026) titled “Your AI Strategy Checklist for Leading a Future-Ready University.” I found this framework incredibly helpful and highly practical for executive teams, senior educators, and institutional governors who need to establish comprehensive AI leadership in education. By shifting our role from passive consumers to active, ethical designers of learning environments, we can transform AI from a risk into a scalable asset.
To support my peers in the tertiary sector and across senior secondary leadership, I have synthesised and analysed the ten key strategic pillars of the Elsevier checklist, adding a localised lens on safe/responsible AI in schools and institutional governance.
1. Clarifying AI Vision and Purpose
Clarity of purpose is the foundation of any resilient digital transition. Rather than deploying tools ad-hoc, institutions must align AI integration with their core mission. This means defining *why* AI is being introduced—whether for student success, advanced research enablement, or operational efficiency—and setting macro-level success metrics that define “good” in your local context.
2. Assessing Institutional AI Readiness
Before launching institutional initiatives, we must evaluate our baseline digital maturity. This includes reviewing existing data privacy, security, and technology policies through an AI lens, ensuring our central infrastructure supports secure experimentation, and identifying faculty training needs. If you are developing proprietary, closed-loop AI systems, early stakeholders must be involved to avoid isolated design pitfalls.
3. Establishing Governance for Innovation and Trust
Robust AI governance in education requires balancing regulatory accountability with the flexibility to innovate. Tertiary and secondary institutions should establish cross-functional AI task forces with clear mandates. Your written ethics and transparency guidelines must directly connect with existing frameworks, such as academic freedom, research integrity, and student data governance.
4. Engaging the Academic Community with Intention
Co-design is pivotal. We must actively involve faculty boards, student unions, instructional designers, librarians, and IT leaders in drafting our operational roadmap. Ensuring that equity, accessibility, and diverse learner representation guide our digital procurement decisions from day one is a critical pillar of genuine inclusion.
5. Evaluating AI Tools Responsibly
Selection of digital tools must be intentional. Leaders should require absolute transparency from software vendors regarding their data training sources, privacy safeguards, and security boundaries. Crucially, human-in-the-loop oversight must remain central so that the professional judgment of our educators is never compromised by an automated algorithm.
6. Fostering Academic Integrity in an AI-Enabled World
Rather than relying on unreliable algorithmic detectors, we must adapt our assessment design to support responsible AI use. Academic integrity policies must explicitly define acceptable and unacceptable boundaries of use. In my own clinical practice, I advocate for integrating student AI literacy directly into orientation and core programmes, framing it as a crucial competency for the future of work.
7. Building AI Literacy and Capacity
To successfully navigate the transition, institutions must provide continuous, multi-dimensional professional development. Establishing internal communities of practice, partnering with external peer networks, and supporting early institutional champions ensure knowledge is distributed evenly rather than siloed within IT departments.
8. Incorporating Sustainability into AI Strategy
The environmental footprint of modern Large Language Models is an emerging, high-stakes concern. Leaders must understand and document the trade-offs between computational performance and environmental impact, requiring hosting vendors to provide transparency regarding their energy efficiency and optimization practices in alignment with broader ESG commitments.
9. Continuous Evolving and Adapting
Strategic AI leadership is an ongoing practice. Policies, user feedback, and risk management metrics must be reviewed recursively. By documenting successes and challenges openly, we can systematically address long-term gaps in institutional culture, infrastructure, and capability.
10. Keeping Core Institutional Values at the Centre
Every digital deployment must align with our commitment to equity, transparency, and scholastic excellence. Active bias monitoring, standardizing transparency about how and why systems are used, and maintaining a balanced, central governance model ensure our kura and universities evolve at pace without sacrificing intellectual independence.
Academic Reference (APA 7th Edition)
Elsevier. (2026). Your AI strategy checklist for leading a future-ready university. Elsevier B.V. https://www.elsevier.com/academic-and-government/ai-in-research-and-higher-education/developing-ai-leadership-in-higher-education
