Business ownership

New Diligent Institute / Governance Institute of Australia data shows 61% of Australian boards restrict employee AI use while only 13% have an AI-literate director — proof that restriction and real governance are pulling apart. NIST's expanding AI Risk Management Framework and the EU AI Act's 2 August 2026 third-party accountability deadline show how structured, evidence-based workflows are what actually let AI adoption move faster, safely.

Grant Thornton's 2026 AI Impact Survey, the AICD/HTI Director's Guide to AI Governance, and a March 2026 Meta AI agent incident all point the same way: organisations scaling AI without a standardised, risk-based assessment framework can't explain or defend their decisions when it matters.

Grant Thornton's 2026 AI Impact Survey, Forrester and MIT research on failed AI pilots, and the EU AI Act's Article 26 deployer obligations all point to the same gap: boards are funding AI faster than they are assigning who owns it. Here's why business-led ownership, not another control layer, is what actually makes AI risk management work.

APRA, ASIC and the ACCC have all sharpened the rules on AI risk in 2026 — from APRA's step-change governance letter to ASIC's 'Year of Accountability' and the ACCC's doubled penalties for AI-washing. Here's why leaders should treat AI risk management as a business enabler, not a compliance checkbox.

Shadow AI is no longer hypothetical: a NSW government contractor uploaded flood victims' personal and health data to ChatGPT, while APRA and ASIC have both issued 2026 letters demanding stronger AI governance and cyber resilience. This post sets out what boards and risk leaders should be asking right now.

APRA's and ASICs AI governance letters have made one thing clear: named business ownership of AI use cases is now the regulatory minimum. Without it, your organisation is carrying unquantified executive risk.

AI initiatives often stall due to unclear ownership and generic vendor assurances. This article explains why business leaders must own AI risks from the start and demand practical, risk-based evidence from third parties to avoid costly delays.

AI risk management often focuses on defensive controls, but this mindset limits business value and slows innovation. Leaders must reframe AI risk as a tool to enable success, balancing risk and opportunity through clear ownership, tailored evidence, and ongoing assurance.