Tagged with: #board-governance

On 2 August 2026, the EU AI Act’s rules for standalone high-risk systems were supposed to take effect. They will not. Six days before the deadline, the EU brought an amendment into force deferring those obligations to December 2027, because the standards and implementation machinery needed to make them workable were not ready. In this article, I set out what the deferral reveals about Boards which anchored AI governance to regulatory deadlines, and where the accountability that never moved actually sits.

Every AI business case counts the hours saved. Almost none counts the hours added: the reviewing, correcting, and supervising that AI outputs demand before anyone can rely on them. That oversight labour is real, regulation increasingly mandates it, and people absorb it silently on top of existing roles. BCG research published in March 2026 shows the consequence: a distinct mental fatigue attaching to heavy AI oversight loads, while workers using AI only to replace routine work report less burnout, not more. I argue this fatigue is a control cost Boards must price, and the remedy is governance, not resilience training.

In the Redeployment Dividend I argued that AI’s real prize is releasing intellectual capital from undifferentiated work, not cutting headcount. The evidence has now caught up with the argument, and it is uncomfortable. Teams that deploy AI save the equivalent of five hours per person per week, yet most of that time drains into low-value work, and nine in ten executives report no measurable productivity impact at their own firm. The saving is real; the value is not arriving. In this article, I argue that the dividend leaks because nobody owns it. AI owns execution and managers own the workflow, but unless the Board owns whether freed capacity creates value, the hours AI recovers will simply refill with the work that was already there.

The market now treats visible AI adoption as proof a company is driving forward through innovation, and the chief executive is the one expected to show it. Being seen to adopt, not misleading the market, and choosing well are three demands held at once. In this article, I argue that AI does not rewrite the chief executive’s duties; it changes the conditions under which they are discharged. The task is to choose the few bets that matter, change how the company works around them, and answer for them without delegating the accountability.

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 case for AI in the finance function is no longer in question. Commitment to it now runs well ahead of readiness, but accountability does not wait for that gap to close. The CFO answers for the integrity of the accounts and the stewardship of capital every reporting cycle, ready or not, and AI is already inside the work that produces both. In this article, I argue that AI changes how each of the CFO’s duties is carried out, not who signs for them, and I sort its effect into four groups: where it does the work, where it sharpens the judgement, where it operates out of sight, and where it changes almost nothing.

The strategic value of data is no longer in question. The next frontier is data whose meaning and relationships are explicit enough for machines to reason over rather than merely retrieve. Unstructured information must be interpreted by whoever consumes it, whether that is a person reading a report or an AI system generating an answer. Structured information makes explicit the relationships required for traceability, verification, and defensible reasoning at scale. In this article, I argue that ontologies and the knowledge graphs built upon them have moved from technical infrastructure into Board territory, because they increasingly determine what an organisation officially knows, what its AI systems can work with, and where durable advantage is created.

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.

The chair’s role was built for a stable world that no longer exists. The Board’s own work is being remade by AI tools that silently invite the substitution of director judgement, and the work the Board governs is being remade by operational AI deployments most directors cannot interrogate. This article works through how Cadbury, the FRC, and the IoD have set out chair responsibilities, none dispensable, all now requiring different execution. The principle that does not move is collective responsibility. The chair polices its boundary, actively, in both states.

Boards have always governed under incomplete information. What the four indicator types offer is not more information but a progressively higher quality of it. Lagging indicators establish what happened, leading indicators signal direction, predictive indicators model possible futures, and reasoned indicators prove what is certain. Applied in combination to a single decision, they represent maximum fidelity — everything knowable and made available before the judgement is made. This article explains why the distinction between a decision made with maximum fidelity and one made without it matters for every director around the table.