Insights

The second day of Risk-!n Zurich had a different character from the first. Day one was largely about visibility and how organizations can see risk clearly enough in environments shaped by artificial intelligence, cyber acceleration, operational complexity, climate exposure and emerging technologies. Day two moved the discussion one step further. If organizations can see more, faster and with greater precision, what exactly are they supposed to do with that visibility?

In a recent LinkedIn post, I argued that the biggest barrier to effective governance is not technology, cost, standards, or even board interest. It is management's reluctance to provide boards with reliable information on uncertainty and performance linked to Mission Critical Objectives (MCOs), combined with boards' reluctance to insist on receiving that information. The reaction to that post reinforced my belief that this issue sits at the center of one of the most important, and least discussed, governance challenges facing organizations today.

Last week I argued that much of what is being marketed as agentic AI in GRC is not actually agentic. The market response was interesting because very few people challenged the core premise. Most practitioners already sense that something is off. They sit through the demonstrations and hear the language. They watch the AI summarize documents, answer questions, generate narratives, and produce recommendations. Then they leave wondering whether they just witnessed the future of GRC or a very polished presentation wrapped around capabilities that have existed in various forms for years.

The first day of Risk-!n Zurich featured discussions on business continuity, enterprise risk management, internal controls, cybersecurity, climate resilience, artificial intelligence and quantum computing. On paper, it looked like a conference agenda built around a broad collection of risk disciplines. In practice, many of the presentations were wrestling with the same question. How do organizations maintain visibility into risks that are moving faster than the governance structures designed to oversee them?

Beneficial ownership is one of the most established concepts in anti-money laundering compliance. It is also one of the most persistently misunderstood in practice. At onboarding, most financial institutions collect beneficial ownership declarations, identify individuals with controlling interests, and document ownership percentages as part of standard due diligence. On the surface, this appears to satisfy regulatory expectations.

It takes about a second. An analyst pastes a contract into a chat box and requests a summary. A recruiter drops a stack of résumés into a tool to rank them. A finance manager uploads a draft board deck and asks for a tighter narrative. The cursor blinks, the answer appears, and everyone moves on. Nothing felt risky. Nothing broke. Yet in that one second, something important happened that almost no one in the building noticed. Information left the organization and went somewhere it had never been.

There is a peculiar imbalance taking shape inside many organizations. Over the past two years, companies have assembled AI governance committees, drafted acceptable-use policies, updated risk registers, and launched internal working groups dedicated to understanding the implications of artificial intelligence. Compliance teams have studied emerging regulations. Privacy officers have debated data-sharing restrictions. Boards have asked increasingly pointed questions about oversight, accountability, and risk.