What Building My Own AI System Taught Me About Governance
Four lessons from building a personal AI knowledge system, for assessing AI strategies at board and executive level.
Perspectives on AI, governance, cybersecurity and digitalisation for boards and executive teams.
Four lessons from building a personal AI knowledge system, for assessing AI strategies at board and executive level.
Why consensus on a board can put the duty of care at risk, and what conflict-capable boards do differently to enable resilient dissent.
Why oversight of learning AI systems runs into structural limits, and the four pillars that carry an effective proxy oversight regime.
What the Hudson River incident and Swiss company law teach about accountability and voice in organisations, and why both leadership modes need to be named explicitly.
Why FINMA Guidance 02/2026 and 08/2024 put boards in front of two separate control loops that cannot be delegated to IT.
What robust studies on AI and jobs actually show, and the four workforce decisions boards and executive teams cannot delegate.
Why 'top risk' rarely turns into a decision in the boardroom, and how boards and executive teams can break the pattern of decision avoidance.
An AI system can change without anyone changing it. Why model drift is a governance duty, and how the board should operationalise it.
AI systems often cannot be fully audited. Why the board's supervisory duty must become proxy oversight, not the illusion of transparency.
As AI systems adapt in operation, accountability becomes a governance question: which controls must the board demand and document?