Tagged with: #regulation

Every position in the AI Sovereignty Trilemma carries a cost, but only one is shown to a Board before it is paid. The visible cost is that sovereign capability is dearer, which is where most sovereignty conversations stop. The hidden cost belongs to the convenient alternative, frontier capability bought cheaply and governed elsewhere, and it stayed invisible only because the control it surrenders had never been tested. On 12 June a directive tested it, forcing a provider to withdraw two frontier models from every customer overnight. In this article, I argue that model availability is a continuity risk a Board must own, and that the task is not to solve the Trilemma but to know which cost the organisation is paying, and to have chosen it.

The UK regime now requires four safeguards for any significant decision taken solely by automated processing: information, representations, human intervention, contestability. On the page these are procedural rights. In practice they all depend on something the law does not name: whether the organisation can interrogate its own decisions well enough for the safeguards to work. For a rule-based system, that capability is built in. For a probabilistic system, it is not, and most Boards have approved those systems without ever asking whether it exists. The first contestability request is when the gap surfaces.

In 2025 Boardrooms saw a collective shift in how they thought about AI’s role. What they spent 2023 and 2024 reacting to became a question of strategic investment in organisational infrastructure. They moved from “what can it do?” and “should we use it?” to “how do we navigate competing pressures and make this core to how we operate?” In this article, I examine the five interconnected inflections that drove this shift — and what they mean for Boards entering 2026.

Minimum lovable governance marks a shift from episodic compliance scrambles to continuous, embedded oversight that people actually want to use. In this article I explain how governance can achieve necessary guardrails whilst earning adoption rather than resistance — like an arbour that guides growth without constraining it. For Boards, minimum lovable governance presents a practical path: the operating principle that makes AI governance work when traditional approaches simply get routed around.

AI governance is fragmenting into incompatible systems — Europe prioritising trust through transparency, America pursuing speed through scale, China maintaining control through integration — forcing Boards to choose rather than compromise. In this article, I explore the sovereignty trilemma and present three strategic stances for navigating these landscapes without fracturing your strategy.

The EU’s General-Purpose AI (GPAI) Code of Practice, effective August 2025, signals a new era of regulatory divergence. While the EU sets transparency and systemic risk guardrails, the U.S. accelerates through deregulation. For Boards, the challenge isn’t choosing sides but mastering dual-track governance — turning regulatory complexity into strategic advantage.

Last week in Washington DC, I hosted a dinner where Professor Ajay Agrawal presented data showing that individual AI training clusters will soon require more electricity than entire nations currently generate. Whilst the UK government announces plans to become an AI-first economy, the mathematics are stark: UK businesses face energy costs four times higher than US competitors, creating dangerous dependencies on foreign AI infrastructure.

In my previous article, Transforming the Board: Using Decision Analytics for Strategic Advantage, I introduced the concept of AI-powered decision analytics as a transformative approach to board decision-making. I explored how these capabilities can help directors move beyond traditional backward-looking metrics to embrace predictive indicators that model potential futures and enhance strategic decision-making.

The EU AI Act, which came into force on August 1, 2024, establishes significant penalties for non-compliance, including fines of up to €35 million or 7% of global annual turnover for serious violations. As regulatory frameworks for artificial intelligence rapidly evolve worldwide, Boards face a new imperative: navigating complex compliance requirements while maintaining the innovation speed necessary to compete.