Tagged with: #governance

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.

Boards have always governed under conditions of incomplete information. What has changed is the volume and velocity of that information, and the speed at which AI systems now act upon it. Lagging indicators report on the past. Leading indicators signal what is likely to happen next. Predictive indicators model possible futures. But automated reasoning offers something different entirely: proof. Not a tighter estimate, but a formally verified property of the decision space itself. This article explains what automated reasoning is, where it already operates across regulated industries, and why it represents a new class of governance instrument for Boards.

The informational asymmetry between management and the Board has always been the central tension of governance. For AI, it is no longer manageable through existing structural checks; the distance is not merely larger than previous technology waves, it is qualitatively different. A director must be able to interrogate maturity claims, assess whether governance is operational or merely presentational, and identify which AI risks are personal development challenges and which are failures of oversight itself. The IoD has formally named the gap. This article defines what closing it actually requires: not technical fluency, but specific capacities for independent evaluation mapped against the governance obligations every director carries, and a diagnostic framework for identifying exactly where the work needs to start.

The part of AI value that is technological and replicable is also the part that standard progress measures capture best. Pilot counts, budget lines, and strategy documents say nothing about whether the essence of work is genuinely being remade, or whether the three compounding loops are operating. A Board that accepts those reports without probing them is not exercising oversight; it is ratifying a narrative the evidence shows is inflated. This article provides the diagnostic that does: probing questions structured around the data, talent, and process redesign loops, with an interpretive guide to what credible answers look like — and what their absence reveals.

AI is not remaking the four dimensions of the essence of work at the same speed, through the same mechanisms, or toward the same end state. Treating them as a single strategic question is the mistake most organisations are currently making. The organisations pulling ahead understand which dimensions are moving fastest in their sector, where redesign would produce the greatest compounding advantage, and what form of human value would survive in each case. This article goes dimension by dimension through the specific patterns of remaking that distinguish organisations building structural advantage from those still augmenting the status quo.

An AI agent that can only see the public internet is no more useful to an organisation’s business than a very expensive search engine. The intelligence is not the constraint. The connectivity is. Model Context Protocol — MCP — is the infrastructure standard that connects agents to the proprietary data, systems, and processes that constitute real competitive advantage. This article explains what MCP is, why the major enterprise vendors have already converged on it, and the governance questions Boards should be asking before their technology teams answer them by default.

In June 2024, I proposed that organisations would need to compensate workers whose expertise became embedded in corporate AI models. The rise of personal AI agents inverts that assumption entirely: individuals are already investing thousands annually in always-on agents that encode their professional judgement, domain expertise, and decision-making patterns — capability that belongs to them, not their employer. This article explores what happens when the most valuable AI in your organisation walks in with the employee and walks out when they leave, and why the IP boundaries, contractual frameworks, and talent strategies needed to navigate this shift don’t yet exist.

Consumers are voluntarily paying $3,650–9,125 annually for always-on AI agents — more than their combined entertainment subscriptions. When ChatGPT followed exactly this pipeline from consumer novelty to shadow enterprise adoption within three years, most organisations were caught unprepared. Agentic AI is now running the same cycle. This article examines the inference migration — the architectural shift from episodic queries to always-on agents, why the determinism objection is narrower than Boards assume, the shadow agentic AI wave already forming, and why governance frameworks established in 2026 will determine which organisations capture agentic value and which scramble to retrofit controls on adoption already underway.

Boards apply rigorous stewardship to physical assets: regular condition assessments, clear ownership, maintenance investment, impairment testing. Data assets — which increasingly drive competitive advantage — receive none of these disciplines. The gap isn’t technical; it’s governance. Accounting standards render data invisible on the balance sheet, so Boards govern it as though it doesn’t exist. This article argues for balance-sheet thinking applied to data, using the AI Centre of Excellence as the governance vehicle. For Boards, the question isn’t whether data belongs on the balance sheet; it’s whether you’ll steward it as if it does.

AI coding tools don’t close the expertise gap — they amplify it. Research shows senior developers capture twice the productivity gains of juniors, while a randomised controlled trial found experienced developers actually worked slower with AI than without, the hidden taxes of verification offsetting initial speed. This article explores the verification premium — and why Boards should ask not “can we use AI to write code cheaper?” but “do we have the verification capability to ensure AI-generated code creates value rather than debt?”