
The IBRS Artificial Intelligence Maturity Assessment Framework
AI maturity models are vital for a successful strategy, providing a clear framework for investment, risk management, and ROI.

AI maturity models are vital for a successful strategy, providing a clear framework for investment, risk management, and ROI.

To build an effective cyber security strategy, organisations must address human manipulation and physical access risks, not just technical controls and training.

When Australian government agencies undergo amalgamation during machinery of government (MOG) changes, IT and people integration present significant challenges that require careful planning and execution.

Figma’s successful IPO signals a robust design software market, potentially encouraging competitors to also go public soon.

Adobe Firefly’s evolution into a generative video hub boosts efficiency, but organisations must balance this with robust governance and skill development to avoid a deluge of poor-quality, off-brand content.

ERP databases are rapidly integrating AI indexing like vector and graph search. This will enable ‘invisible AI’ within core business applications, but adoption may be slow due to migration and skill challenges.

Generative AI will revolutionise how businesses handle data, offering natural language interfaces for queries and merging data and information governance.

Regardless of their current data maturity levels, enterprises often encounter challenges in fully engaging their entire workforce in data-driven decision-making. Senior executives are seeking ways to effectively leverage generative AI to implement enterprise-wide business intelligence, enabling every team to access real-time operational data and derive actionable insights.

This advisory paper offers a tech maturity model for higher education, from basic to optimising, helping institutions benchmark and prioritise tech investments.