What I write about
The Boardroom
My work on the decisions Boards must own, the judgement they must exercise, and the accountability they cannot delegate.
My take
The Boards I meet are rarely short of information. What is harder to see is whether a decision has reached them while it is still open, or after the framing, the options, and the preferred answer have already been settled elsewhere. That is where most of my work here starts: with the conditions directors need to exercise judgement rather than ratify a conclusion.
I don’t think directors need to become technologists to govern technology well. They do need enough capability to test the case management presents, distinguish evidence from confidence, and know when agency has moved without accountability moving with it. That work reaches the whole Board: the chair protecting collective accountability, the company secretary preserving the conditions for judgement, and every director bringing a view they have formed for themselves.
The question I keep coming back to is whether the Board and the people transforming the work are looking at the same picture. My Remake framework is where I connect agency for the work with accountability for the change, from the decision to begin through to the outcome the Board must stand behind. The work gathered here follows that boundary across director capability, Board information, strategy, risk, and accountability.
Latest writing (92)
The Reasoning Gap: The Capability the Law Now Demands of Boards
UK law now requires four safeguards for solely automated decisions. Most Boards have approved probabilistic systems that cannot deliver them in operation.
AI and the Chair: Governing the Board Through The Great Remaking
Existing chair responsibilities now require different execution as AI remakes both the Board's own work and the work the Board governs.
The Appreciating Ledger: When AI Capital Outgrows the CFO's Rulebook
AI capital appreciates, accumulates, and crosses functions. The four indicator types let CFOs see what conventional project-ROI models structurally cannot.
Maximum Fidelity: How Four Indicator Types Strengthen Board Decisions
Four indicator types give boards progressively higher decision fidelity: lagging, leading, predictive, and reasoned. Together they represent the most accountable governance instrument available.
AI and the Director: A Practical Playbook for Governing What You Can't Fully See
Directorial AI literacy is not technical fluency. It is four specific capacities that let directors interrogate maturity claims, assess real governance, and exercise independent judgement.
The Great Remaking: The Questions Boards Should Be Asking About Their AI Position
Pilot counts and budget lines cannot tell a Board whether work is being remade. These questions, built on the data, talent and process loops, can.
The Great Remaking: Why Fast Following Does Not Work When the Gap Compounds
Every previous technology wave rewarded fast followers. The Great Remaking does not: the advantage is operational accumulation that cannot be bought and compounds with time.
The Great Remaking: How the Four Dimensions of Work Are Transforming
AI is remaking thinking, deciding, creating and delivering at different speeds and towards different ends. Treating them as one question is most organisations' mistake.
MCP Explained: The Agent Infrastructure Standard Boards Need to Understand
An agent that sees only the public internet is an expensive search engine. MCP connects agents to the proprietary systems that constitute advantage.
The Great Remaking: AI and the Race to Transform the Very Essence of Work
Five technology revolutions changed organisations; none restructured the essence of work. AI does, and redesign beats bolt-on by four times in shareholder returns.
The Personal Agent Economy: When Your Best AI Isn't On Your Balance Sheet
Individuals now own AI agents that encode their judgement and expertise, capability that belongs to them rather than their employer. The assumption has inverted.
The Inference Migration: What Consumer Agents Mean for Enterprise AI's Next Phase
Consumers are already paying for always-on AI agents. That consumer-to-enterprise pipeline is the one ChatGPT ran, and it is running again.
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The ideas that underpin my writing
Ideas I’ve named and matured writing about Board: what each one means, and where it started.
Accountability Gap
When an organisation delegates work to AI without building the capability to verify it, leaving people answerable for outputs no one has actually checked. For a Board, no delegation to AI should be approved without also approving who checks the output and how, because accountability without a verification step is accountability in name only.
Adaptive Localisation
A strategy of running different AI approaches in different markets, tuned to each market's regulatory, cultural, or competitive conditions, deliberately trading consistency for regional advantage. For a Board, choosing this stance means accepting real complexity costs and being ready to answer why the organisation treats one market's rules differently to another's.
AI Capability Bifurcation
The split between workers who build genuine capability to verify and apply judgement to AI outputs, who command a premium, and those who merely accumulate credentials or tool exposure, who face an earnings penalty. For a Board, the same divide runs through the boardroom itself: familiarity with AI tools is not the same as the capability to challenge what the organisation actually does with them.
Board Briefings
For directors who want to go deeper, my Board Briefings bring the related writing, evidence, and practical questions together in one place.
The Board in the Machine
Signals
My early reads on technologies and ideas that are starting to reach the Board agenda, while the picture is still forming.
Agentic AI
Generative models are being given goals, tools, and the authority to act. The Board question is where to transfer agency, and under what limits.
Automated Reasoning
Formal logic and mathematical proof, industrialised. For some precisely specified, high-consequence controls, testing is no longer the strongest assurance available.
Embodied AI
AI that acts in the physical world, from factory humanoids to autonomous machines. The liability, capital, and workforce questions are already Board-level.
Remake
The models, diagnostics, methodologies, and principles from the Remake Library that I use when working through Board judgement, director capability, and the line between agency and accountability.
- Principle Minimum Lovable Governance Governance embedded in how work happens: proportionate to risk, continuous rather than episodic, and used because it works.
- Model Six Board Concerns An interconnected lens of six concerns, Strategic Alignment, Ethical and Legal Responsibility, Financial and Operational Impact, Risk Management, Stakeholder Confidence and Safeguarding Innovation, that must be orchestrated together so AI discussion does not collapse into risk management alone.
Questions Boards ask about The Boardroom
Can I evaluate what I am being asked to oversee?
It asks whether your oversight is real or nominal. A credible yes means you can test management’s claim of strong AI governance with a question of your own, distinguish well-governed AI from well-presented AI, and recognise a deployment drifting from its specification. If any answer is no, the development priority is clear: oversight without the capacity to evaluate is oversight in name only.
Is my view genuinely mine?
It asks where your position on AI was formed. If it is constructed entirely from management briefings you cannot interrogate, it is endorsement rather than judgement. A credible yes rests on independent information: your own resources and sources of insight on AI, per the IoD’s Recommendation 10, and knowing the strategy has changed materially before a journalist asks.
Are the assurances the organisation provides its Board based on probability, or proof?
Board assurance often rests on testing, sampling, and statistical confidence. The useful test is whether an assurance covers the complete, formally specified population or only the cases examined. Where a Board-approved constraint has been encoded and exhaustively verified, the answer is proof. Elsewhere it remains probability, however polished the presentation.
How many of our decisions are truly human, and how many are AI with sign-off?
The question probes the migration I call the change in decision architecture: nominal authority stays with people while the cognitive content of the decision shifts to the machine. Deloitte’s data shows 60% of executives now regularly use AI to support decisions while only 5% of organisations rate themselves mature in governing them, so most Boards cannot currently answer this with precision. A credible answer names the decision classes where AI recommendations dominate, and shows that accountability structures were redesigned when the decision substance moved.
What happens when nobody is accountable?
The work does not start. A remaking proceeds only where there are people with the skills and authority to do the work and accountability for the change that reaches the Board, and the gate tests both together. Accountability cannot be handed to a project team and still mean anything: a sponsor who fades after kickoff is not accountability, and a Board that is briefed but not answerable is not oversight. A change with no one truly answerable fails often enough that the framework refuses to start one.