AI Governance

Corporate America has found a new way to signal its ambition: hiring software. Companies are giving artificial intelligence (AI) agents names, titles, and places on the organizational chart, and press releases celebrate the arrival of the “first AI employee” as though a person had walked through the door. According to MIT Technology Review’s James O’Donnell, nearly a third of the 1,261 managers surveyed in a recent Boston University study said their companies already frame AI agents as employees, and 23 percent list them on org charts.

For South Korean AI developers, some useful data has come with an awkward choice. Get fresh consent from the people behind it, strip away enough identifying information to satisfy privacy requirements, or find another legal basis for using information that may have been collected for an entirely different reason. None is especially convenient when the object is to train an AI model on large and varied datasets.

In a recent LinkedIn post, I asked why so many organizations are trying to assess AI as a standalone risk. I think that question gets to the heart of what is going wrong with much of the discussion around AI governance. There is no shortage of people trying to work out how organizations should govern AI. New frameworks are appearing, risk taxonomies are being built, internal audit teams are developing programs, and familiar questions are being asked about bias, security, compliance, privacy, and hallucinations.

Antimatter propulsion remains far beyond today's engineering reach. But AI may help turn the unknown into a development roadmap. The governance question is whether we can control the research before it outpaces us. A question that sounds like science fiction reveals a very practical governance problem: What happens when artificial intelligence accelerates a high-consequence technology faster than our institutions can regulate it?

Imagine the boardroom in 2036. Artificial intelligence has become part of how the company operates. It tests capital choices, monitors risk, compares strategic alternatives and makes thousands of decisions within limits established by management and the board.

The Austrian Financial Market Authority (FMA) has issued an update to the ordinance regarding additional provisions for capital investment risks in premium-subsidized future provision (PZV-ZRV). This regulation establishes specific mathematical formulas and parameters for calculating maximum losses and guarantee amounts related to unhedged equity portions in these specific insurance contracts.

On Aug. 2, the European Commission's AI Office, working alongside national authorities, began enforcing key provisions of the AI Act. The same date also marked the start of new transparency obligations requiring certain AI systems to disclose when users are interacting with artificial intelligence rather than a human being. AI-generated or AI-altered content must now carry machine-readable markers that make it easier to detect, while deepfakes (images, audio, or video) must be clearly labeled.