Artificial Intelligence (AI)

Unclear ownership and weak third-party evidence can stall AI initiatives for months. This article explains why business-led accountability and structured evidence checklists are critical to smooth AI risk management and faster decision-making.

Germany's data regulator fined Vodafone €45 million partly for failing to vet a third-party partner, a 2026 DataGrail report found 64% of AI vendors hide their subprocessors, and a German court has ruled companies — not their AI vendors — are liable when the tool gets it wrong. Three real 2026 examples show why vendor assurances can't substitute for your own verification.

A NSW Reconstruction Authority contractor uploaded flood victims' personal data to ChatGPT in 2025, echoing Samsung's 2023 source-code leak. New 2026 survey data shows most staff still use unsanctioned AI tools — here's what boards should do about it.

ASIC and APRA now both expect a named accountable person behind every material AI use case, not a shared committee. This post looks at what Beware the Gap, APRA's April 2026 letter to industry, and a real AI hiring-platform ownership failure mean for boards before the regulator asks who was responsible.

The UK's new Critical Third Parties regime — covering AWS, Microsoft, Google Cloud and Oracle — signals that strong AI vendor oversight is becoming the new baseline for trust. Organisations that build this capability now, ahead of the curve, stand to gain faster vendor decisions, stronger customer confidence and far fewer surprises.

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.