Govern for variability—not perfect predictability. Identify where probabilistic AI is used in consequential decisions and require management to define acceptable performance ranges, test unexpected inputs and monitor for inconsistent or anomalous outputs.
Shift from periodic review to continuous oversight. Traditional point-in-time controls are insufficient for AI systems that evolve through changing data, context and use. Confirm that management has continuous monitoring, clear risk indicators and escalation protocols for critical AI applications.
Build accountability around four practical questions. For each material AI system, ask: Where is it operating and what is the potential impact of failure? How will unusual inputs or outputs be detected? How will the organization intervene? What is the fail-safe plan if the system becomes unavailable or unreliable?
Design layered guardrails and human intervention from the start. No single control can eliminate AI risk. Expect management to combine technical controls, access restrictions, real-time monitoring, human review and fallback processes—and to redesign workflows rather than simply adding AI to existing processes.
Use pilots to discover failure—not only demonstrate ROI. Encourage controlled pilots, sandbox testing and intentional red-teaming to reveal how AI may circumvent rules or create unintended consequences. Pilot evaluations should include failure scenarios, monitoring and control costs, human capacity requirements and the full cost of ownership—not just efficiency gains.
Podden och tillhörande omslagsbild på den här sidan tillhör
BDO USA. Innehållet i podden är skapat av BDO USA och inte av,
eller tillsammans med, Poddtoppen.
