AI strategy and readiness
AI Strategy and Governance
The Artificial Intelligence Steering Committee (AISC) has been established to leverage our collective expertise and resources to review our AI readiness, create a Monash AI strategy for our education, research and operations, and coordinate action to realise the immense potential of AI while mitigating the risks. The AISC is led by Dr Behrooz Hassani-Mahmooei, Chief Analytics Officer.
The AISC and its sub-committees are coordinating and overseeing key streams of work. These include the deployment of new technologies, ensuring AI literacy for all staff and students, and working closely with the Academic Board to ensure consistency and efficiency in decision-making on all relevant academic matters in learning and teaching, research, and research training.
The AISC is currently leading the development of the Monash AI Strategy and Utilisation Plan. You can contact the AISC via this form.
Current AI Initiatives
Explore our innovative projects in teaching, assessment and graduate research that position Monash to stay aligned with rapidly evolving AI technologies.
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Authentic Teaching and Learning through Adaptive Simulations (ATLAS)
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The ATLAS platform is “a revolutionary digital ecosystem that offers immersive, simulated professional experiences tailored to individual learning trajectories“ by using “Large Language Models, artificial intelligence, and intricate agent personas to simulate real-world professional environments.”
Graduate Research AI Capability and Engagement Strategy (GRAICE)
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GRAICE is a strategic plan that will support Graduate Research students and supervisors by ensuring access to the resources, training, information and tools required to best utilise AI in their research in a responsible manner (staff only).
Programmatic Assessment and AI Review (PAAIR)
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The Programmatic Assessment and AI Review (PAAIR) project involves collaboration between faculties and the DVC-E portfolio to strategically integrate AI and secure assessments within a coordinated framework of programmatic assessment activities, preparing students and disciplines for the Age of AI (staff only).
Monash AI Readiness Framework
The Monash AI Readiness Framework is designed to gauge and benchmark an institution's capacity to implement AI technologies into their processes to enable digital transformation. The framework facilitates self-assessment across six key pillars and more than twenty dimensions, assessing an institution’s current sociotechnical AI status and the potential value-adding of deploying emerging AI technology.
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Strategy & Alignment
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Strategy and Alignment in AI readiness emphasises the strategic integration of AI within the institution’s broader objectives. This pillar focuses on developing an ambitious vision for AI adoption, defining a utilisation plan for implementation, and establishing measurable outcome indicators to track progress while effectively managing risks and opportunities.
- Ambition and Risk Appetite: This dimension considers the institution’s level of ambition and risk tolerance in adopting AI. It gauges the institution’s desired level of AI integration, from beginners to innovators of AI technologies. Additionally, the dimension considers the amount and type of risk the institution is willing to accept to achieve its AI objectives. The clarification of ambition and risk appetite enables the institution to establish a strategic vision and direction for AI implementation.
- Strategic Objectives & Business Opportunities: This dimension focuses on the strategic alignment of AI initiatives with the broader institutional values, vision, and goals. It examines the specific objectives the institution aims to achieve through AI adoption. Additionally, this dimension considers the institution’s ability to identify and leverage business opportunities presented by AI to drive innovation and enhance its competitive advantage.
- Utilisation Plan: This dimension gauges the institution’s preparedness to effectively assess, select, and integrate AI solutions within institutional operations. It considers whether a strategic plan exists to identify where AI can add value by automating or augmenting existing workloads. Furthermore, it focuses on the engagement of the institution with internal and external experts for consultation or collaboration in developing a plan that ensures the effective and efficient use of AI solutions.
- Monitoring (KPIs & Horizon Scanning): This dimension explores the institution’s ability to track the progress and impact of its AI initiatives. It considers the establishment of key performance indicators to ensure the effectiveness, value, and outcomes of AI projects are measurable and quantifiable. This dimension also examines the institution’s commitment to horizon scanning to actively monitor emerging AI technologies and best practices to inform AI strategy and maintain a competitive edge.
Governance
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Governance in AI readiness relies on policies and procedures to set out institutional expectations and guide the responsible use and management of AI technologies. This pillar focuses on defining the scope of responsibility and developing ethical guidelines, risk management strategies, and decision-making processes to ensure internal accountability, transparency, and compliance with relevant external regulations.
- Responsible Use Principles: This dimension focuses on the institution's commitment to ethical practices and responsible application of AI. It considers the establishment of clear ethical guidelines and responsible use principles for the development and deployment of AI technologies, as well as the awareness and understanding of these guidelines within the institution. Prioritising ethical considerations can foster trust in AI systems, promote fairness, and minimise potential harms.
- Internal Policies: This dimension considers the institution’s ability to establish and maintain robust internal policies that govern the development, deployment, and use of AI. It gauges the awareness of these policies within divisions, as well as the institution's ability to adapt them to keep pace with evolving AI technologies and practices. This ensures ongoing compliance with relevant ethical and legal standards, fostering a culture of ethical and responsible AI innovation.
- External Laws/Regulations: This dimension considers the institution’s ability to understand and comply with the evolving legal and regulatory landscape surrounding AI. It observes the institution’s awareness of relevant external guidance, laws, and obligations to ensure that all AI deployments align with these requirements. The prioritisation of legal and regulatory compliance enables the institution to mitigate potential risks and uphold ethical standards.
- Decision-Making: This dimension focuses on the institution’s ability to establish transparent and well-defined decision-making processes for AI development and deployment. It examines mechanisms for identifying key stakeholders, clearly defining their roles and responsibilities, and establishing procedures for consultation, review, and approval of AI initiatives. Additionally, it appraises decision-making criteria to ensure that AI implementation is data-driven, adheres to ethical standards, and aligns with strategic objectives.
- Risk Management: This dimension focuses on the institution’s ability to assess the risks associated with AI programs throughout their lifecycle. It also examines the institution’s awareness and understanding of potential risks stemming from the failure to adapt to AI challenges and opportunities. Additionally, this dimension considers the frequency of risk assessments and the continuous monitoring of AI systems for emerging threats. Implementing robust risk management practices enables the institution to proactively address challenges and minimise potential harms.
Operational Model
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Operational Model in AI readiness considers how AI initiatives are prioritised, funded, and managed. This pillar focuses on developing a criterion for selecting AI initiatives, allocating budgets and investments, establishing controls and approval mechanisms, and defining roles and responsibilities to translate the AI strategy into actionable steps.
- Triage & Prioritisation: This dimension considers the institution’s ability to effectively triage and prioritise AI initiatives. It examines the presence of a systematic process for evaluating AI initiatives based on their potential impact, feasibility, strategic alignment, risk assessment, and resource requirements. Additionally, it explores mechanisms in place to regularly review project priorities to align with evolving AI-related objectives, ensuring resources are allocated to the most promising and impactful initiatives.
- Budget & Investment: This dimension focuses on the institution’s capacity to effectively allocate and manage financial resources for AI initiatives. It examines the development of a comprehensive budget to deploy or pilot AI products, including resource allocation for infrastructure, talent acquisition, and maintenance of AI systems. The dimension also considers funding sources, investment strategies, and the institution's approach to monitoring the return on investment of AI projects to ensure that initiatives are well-funded, sustainable, and aligned with strategic objectives.
- Controls & Approvals: This dimension considers the institution’s capacity to establish and maintain robust controls and approval mechanisms for AI initiatives. It examines structured approval processes in place to ensure rigorous review and adherence to quality, safety, and ethical compliance standards. Additionally, it explores institutional awareness of approval processes to access and deploy AI products, as well as the control mechanisms implemented to monitor AI projects. The presence of appropriate safeguards and proactive oversight enables the institution to proactively mitigate risks, maintain accountability, and align initiatives with broader strategic objectives.
- Roles & Responsibilities: This dimension considers the institution’s clarity in defining and assigning roles and responsibilities for AI initiatives. It explores the established mechanisms for identifying key stakeholders and communicating responsibilities to establish internal accountability. This dimension also examines how the institution fosters collaboration and coordination between different divisions involved in AI projects, promoting a cohesive and streamlined approach to AI implementation.
Technology
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Technology in AI readiness considers the institution’s technological capacity to support AI initiatives. This pillar focuses on building a solid foundation of technological infrastructure, computing power, data management systems, AI platforms, and robust privacy and security measures to ensure the successful deployment and scaling of AI solutions.
- Infrastructure: This dimension gauges the institution’s ability to provide the technological foundation necessary for successful AI implementation. It examines the adequacy of existing hardware (computing power, storage), software (AI tools), and network infrastructure to meet the demands of AI development and deployment. Additionally, it explores the institution's technical expertise for maintaining and optimising AI systems, as well as strategies to ensure the infrastructure remains scalable and adaptable to meet future AI needs.
- Data: This dimension considers the institution’s capacity to effectively manage and leverage data for AI initiatives. It appraises the institution’s existing data architecture, as well as ensuring deployed AI tools have sufficient credits/quotas that can accommodate the demands of AI projects. It also examines the quality, availability, format, and volume of data required for training AI models. Additionally, this dimension considers the processes for data cleaning, preprocessing, and quality control to ensure data accuracy and consistency for deployment in AI applications.
- Platforms: This dimension considers the institution’s ability to effectively select, integrate, and monitor the use of suitable AI platforms. It explores the processes for selecting AI tools and platforms based on their functionality, performance, and compatibility with existing systems. Additionally, it considers how the institution develops, integrates, utilises, and updates AI solutions and how their impact is measured to optimise their potential for supporting innovation and enhancing outcomes.
- Privacy & Security: This dimension appraises the institution’s preparedness to safeguard sensitive data and AI systems from unauthorised access, breaches, and misuse. It examines the institution’s awareness and understanding of AI-specific privacy risks and cybersecurity threats to ensure that robust safeguards are in place to minimise potential vulnerabilities. The prioritisation of privacy and security ensures compliance with data protection regulations, fosters trust among AI adopters, and promotes the responsible and ethical use of AI tools.
People in AI readiness relies on the depth of AI knowledge and the proficiency in AI literacy, skills, and talent of the institution’s community. This pillar focuses on developing AI competencies through ongoing training investments, attracting and retaining AI talent, and fostering a culture of innovation and experimentation with AI.
- AI Literacy: This dimension examines the current state of AI literacy within the institution and gauges the proficiency of staff and students in adopting AI technologies. It considers the institution’s strategies for promoting AI literacy, the training programs offered to build foundational AI knowledge, and the mechanisms in place to measure the effectiveness of these initiatives. The institution’s commitment to AI literacy empowers its community to fully benefit from the potential of AI.
- Development & Training: This dimension gauges the institution’s commitment to equipping its workforce with the necessary skills to utilise and leverage AI technologies. It considers the availability of training programs that encompass the technical, practical, ethical, and regulatory aspects of AI. The dimension also explores whether the institution fosters a culture of continuous learning, providing opportunities for upskilling and professional development. Investment in training and development can empower individuals to effectively and ethically utilise AI tools, driving innovation and informed decision-making.
- Talent: This dimension gauges the institution's ability to attract, develop, and retain AI talent. It explores strategies for recruiting AI professionals, nurturing AI talent through professional development opportunities, and retaining AI experts by cultivating a supportive environment that encourages innovation and collaboration. The dimension also examines the establishment of specialised AI roles to ensure the institution has the necessary expertise and capability to advance AI innovation.
- Innovation Capacity: This dimension examines the institution’s capacity to cultivate an environment that supports AI innovation and experimentation. It considers the extent to which exploration and application of AI is actively encouraged or incentivised within the institution. A strong innovation capacity is critical for the institution to fully harness the potential of AI to support advancements and breakthroughs across research, education, and operations.
Adoption & Scaling
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Adoption and Scaling in AI readiness considers how institutions effectively utilise, integrate, and scale AI solutions. This pillar focuses on engaging and communicating with stakeholders, managing change while fostering a positive AI culture, and ensuring equitable access to AI resources across the institution.
- Guidance, Engagement & Communication: This dimension focuses on the institution’s ability to provide clear guidance, engaging information, and open communication channels regarding AI initiatives. It examines the establishment of effective communication channels and the strategies to engage stakeholders in AI initiatives. A transparent and collaborative approach ensures that the institution’s community is informed, empowered, and actively involved in the adoption of AI.
- Change Management: This dimension gauges the institution’s ability to manage cultural and organisational changes that accompany AI implementation. It considers the presence of change management practices that facilitate a smooth transition to AI deployment and utilisation. This includes strategies to address resistance to AI, manage evolving roles and responsibilities, and provide training and support for staff to adjust to technological changes. Proactive change management ensures seamless, efficient, and sustainable AI integration, preparing the institution to adapt effectively.
- Culture (Trust & Stigma): This dimension considers the institution’s progress in promoting a culture that embraces AI. It explores the level of trust and openness towards AI within the institution, the willingness of staff to embrace AI tools, and any challenges related to promoting acceptance of AI. The development of a positive AI culture is critical for fostering trust, reducing stigma, and ensuring successful utilisation of AI across the institution.
- Access & Equity: This dimension considers the institution’s commitment to ensuring equitable access and utilisation of AI technologies across its diverse community. It focuses on the measures in place to mitigate biases in AI data and outputs, and to address disparities in the access and outcomes of AI initiatives. The prioritisation of equitable AI access and use fosters a more inclusive environment.
