Practical insights on AI governance, responsible adoption, workflow design, and regulation for high-trust organisations.
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AI Governance & Regulation
Responsible AI needs legitimate goals, bounded authority, decision-specific evidence and a real way to challenge and correct outcomes, not an approval click.
Read articleArticle 50 is now applicable. Learn which AI interactions and outputs need machine-readable marking or human-facing disclosure, by whom, and when.
Europe now has a published competence standard for professional AI ethicists. Here is how to turn the role into credible governance and evidence.
EN 18286:2026 gives high-risk AI providers a European QMS reference for the AI Act. Connect obligations, controls and evidence without new bureaucracy.
Turn AI and agent changes into owned decisions, completed actions and evidence that controls work, with reassessment before materially changed operation.
Connect AI literacy to the tasks people perform, the evidence they must check and the current EU AI Act duty to support its development.
Give reviewers the evidence and authority to approve, challenge or stop AI and agent actions before they affect people or records.
A practical AI-use policy connects approved tasks, data boundaries, review and escalation. Test whether the team can apply it to real work.
Decide whether an AI supplier fits the work, total cost and agent permissions, with demonstrable controls, usable evidence and an exit route.
Moving from an AI pilot to daily work requires evidence about ownership, review, data and complete effort. Define those conditions before expanding access.
Assess Swiss models such as Apertus by the actual service, data flows, legal coverage and tested tasks, and follow the Swiss AI rules now being drafted.
Classify legal AI by its intended purpose and role under Article 6 and Annex III, then identify the applicable obligations and dates.
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