Instead of trying to anticipate every potential harm, AI regulation should mandate open, internationally consistent audit trails, similar to financial transaction logs. This shifts the focus from pre-approval to post-hoc accountability, allowing regulators and the public to address harms as they emerge.

Leaders must resist the temptation to deploy the most powerful AI model simply for a competitive edge. The primary strategic question for any AI initiative should be defining the necessary level of trustworthiness for its specific task and establishing who is accountable if it fails, before deployment begins.

A fundamental divide exists between consumer and enterprise AI. While consumer products often reward novelty and creativity, enterprise applications are worthless without correctness. This requires building systems grounded in truth that can extract what is verifiably correct from complex organizations.

In regulated industries like finance, the primary barrier to full AI automation is often regulation, not just user trust. It is the technology provider's responsibility to prove AI's reliability and safety to regulators, much like the industry did to legitimize e-signatures over a decade ago.

For applications in banking, insurance, or healthcare, reliability is paramount. Startups that architect their systems from the ground up to prevent hallucinations will have a fundamental advantage over those trying to incrementally reduce errors in general-purpose models.

A key risk for AI in healthcare is its tendency to present information with unwarranted certainty, like an "overconfident intern who doesn't know what they don't know." To be safe, these systems must display "calibrated uncertainty," show their sources, and have clear accountability frameworks for when they are inevitably wrong.

Society holds AI in healthcare to a much higher standard than human practitioners, similar to the scrutiny faced by driverless cars. We demand AI be 10x better, not just marginally better, which slows adoption. This means AI will first roll out in controlled use cases or as a human-assisting tool, not for full autonomy.

When deploying AI for critical functions like pricing, operational safety is more important than algorithmic elegance. The ability to instantly roll back a model's decisions is the most crucial safety net. This makes a simpler, fully reversible system less risky and more valuable than a complex one that cannot be quickly controlled.

Treat accountability as an engineering problem. Implement a system that logs every significant AI action, decision path, and triggering input. This creates an auditable, attributable record, ensuring that in the event of an incident, the 'why' can be traced without ambiguity, much like a flight recorder after a crash.