What I write about
Artificial Intelligence
My work on what AI makes possible, what organisations have to change to use it well, and what Boards remain accountable for.
My take
AI has been part of my work for long enough that I have become less interested in the technology itself and more interested in what happens when organisations try to use it. That is where most of my writing starts: what AI makes possible, what has to change around it, and what the Board needs to be able to see when the technology moves faster than the organisation adopting it.
I don’t think there is one path through AI adoption that every organisation needs to follow. Different functions move at different speeds, different uses justify different levels of investment and governance, and sometimes the right answer is not to use AI at all. My Remake framework is where I bring those differences into one view, without flattening them into a maturity score or yet another transformation roadmap.
The question I keep coming back to is what changes when AI becomes part of how the organisation actually works. That reaches strategy, people, operating models, value, ethics, risk, and accountability, and increasingly the boundaries between them. The work gathered here follows that question wherever it leads.
Latest writing (96)
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.
The Invisible Asset: Why Boards Should Govern Data Like It's on the Balance Sheet
Boards steward physical assets with condition checks and ownership, and govern data as if it did not exist. The gap is governance, not technology.
The Verification Premium: What Classical Training Reveals About AI Coding Costs
AI coding tools amplify the expertise gap rather than closing it: senior developers capture twice the gains. The verification premium is the cost nobody budgets.
The AI Talent Bifurcation: Are You Building Skills or Collecting Credentials?
Workers with real AI capability command premiums of 28-56%; those collecting credentials without it face a 29% penalty. The same split now reaches the Boardroom.
The Redeployment Dividend: Why AI Will Unleash Your People, Not Replace Them
AI's primary value is not headcount reduction but the intellectual capital it releases from undifferentiated work. Measure only the former and the dividend goes unclaimed.
Return-to-Work Briefing: Five Forces Reshaping the Board AI Agenda in 2026
Five forces shape the Board's AI agenda in 2026, led by AI embedding into the enterprise faster than it can be governed. None is distant.
The Year AI Grew Up: Five Inflections That Changed the Strategic Calculus in 2025
In 2025 Boards stopped asking what AI could do and started treating it as strategic infrastructure investment. Five connected inflections drove that shift.
The Return of Traditional AI: Organisations Are Rethinking Their LLM-First Strategies
Forty-two percent of companies abandoned most of their AI initiatives this year, often because generative AI was applied to problems traditional methods solve better.
A New Grid Actor: AI Infrastructure Is Becoming Energy Infrastructure
AI infrastructure operators building their own generation become grid actors rather than consumers, and that changes energy economics, nowhere more sharply than the UK.
The AI Maturity Mirage: Diagnosing the Gap Between Investment and Readiness
Boards overestimate AI maturity by counting tools and pilots rather than capability. Three patterns create the illusion, and each can be diagnosed before it misleads.
Minimum Lovable Governance: The AI Operating Principle Boards Should Use
Minimum lovable governance replaces episodic compliance with continuous, embedded oversight people actually want to use: guardrails that earn adoption rather than enforce it.
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Agentic AI Explained: From Human Control to Machine Autonomy
What changes when AI stops advising and starts acting: delegated authority, the governance question it raises, and what Boards should be asking before the shadow agentic wave arrives.
Board Briefings
For directors who want to go deeper, my Board Briefings bring the related writing, evidence, and practical questions together in one place.
AI Strategy
From accumulating pilots to a strategy the Board owns
AI Governance
Governing AI the Board cannot fully see, without strangling it
AI Transformation
Crossing from pilots to enterprise-scale change
Operating AI
Building the Centre of Excellence that scales adoption
The ideas that underpin my writing
Ideas I’ve named and matured writing about AI: 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.
Signals
My early reads on technologies and ideas that are starting to matter, 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.
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.
Knowledge Graphs
Knowledge graphs make relationships and provenance explicit, helping enterprise AI produce answers that are more consistent, traceable, and easier to govern.
Remake
The models, diagnostics, methodologies, and principles from the Remake Library that I use when working through AI strategy, adoption, governance, and value.
- Diagnostic AI CoE Simulator An interactive assessment tool operationalising the AI Stages of Adoption, objectively placing each business function within adoption stages using specific criteria rather than subjective self-assessment, revealing an organisation's multi-speed AI reality.
- Diagnostic AI Initiative Rubric A pilot evaluation tool that scores candidate initiatives across the five Well-Advised value priorities and Five Pillars capability building, recommending whether to prioritise, defer, pipeline, or develop them further.
- Diagnostic AI Amnesty Questionnaire The structured instrument for running an AI amnesty: ten sections capturing which tools employees actually use, the use cases they serve, the data they touch, the value already created, and the risks encountered, turning unknown unknowns into a governable inventory.
- Model AI Sovereignty Trilemma The proposition that organisations and jurisdictions can optimise their AI posture for trust, speed or control, but not all three simultaneously, forcing deliberate strategic positioning rather than attempting to serve every market at once.
- Model AI Stages of Adoption A framework describing five stages of the AI journey, Experimenting, Adopting, Optimising, Transforming and Scaling, plotted on an investment-value graph, recognising that different functions progress simultaneously at different paces.
- Diagnostic Five Pillars of AI Capability The five capability domains of an AI capability model that cut across every level of maturity: Governance and Accountability, Technical Infrastructure, Operational Excellence, Value Realisation and Lifecycle Management, and People, Culture and Adoption.
- Principle Well-Advised The framework of five strategic priorities, Innovation, Customer Value, Operational Excellence, Responsible Transformation, and Revenue, used to ensure AI investments create balanced value rather than narrow cost reduction.
- Diagnostic Well-Advised Assessment The measurement instrument of the Well-Advised principle: an assessment of the value an AI investment is actually realising across the five strategic priorities, returning a balanced-value reading rather than a single ROI number.
Questions Boards ask about Artificial Intelligence
Why do you think you need AI?
Not which AI, and not how much, but why any. Work with one right answer belongs with deterministic tools: a balance reconciles or it does not. A model is the right instrument for judgement, where the best available is a reasoned view of an uncertain situation. Handing a fixed question to a probabilistic tool licenses it to decide afresh on every run, and it will.
Where is AI changing the work itself in our organisation, and how fast?
The three states, augmenting, restructuring, and substituting, are a direction of travel, not fixed positions: work augmented today may be restructured within two years and substituted within five, and the pace differs by dimension. Placing each of thinking, deciding, creating, and delivering on that trajectory separately, with evidence rather than instinct, is the diagnostic that determines the strategic response. The dimension moving fastest in your sector is where the strategy needs the most precision.
What stage of our AI journey are we in?
Answer it per function, not for the organisation as a whole: the multi-speed reality means marketing may be Optimising while operations is still Experimenting. Placement uses evidence against the stage criteria, not self-assessment: what is actually deployed, governed, and delivering, function by function. The honest map is the starting point every subsequent decision builds on.
Are our people doing more valuable work, or simply work with AI tools?
The BCG comparison answers what the difference looks like: 67% of employees save more than an hour daily where workflows are redesigned around AI, against 49% where tools are deployed inside unchanged processes. The premium comes from the capability to redesign work. If the work itself is unchanged, the tools are decoration and the time savings are marginal.
Which decisions will AI increasingly make, and who bears the consequences when it does?
Deciding is the dimension with the tension no other shares: AI can assume the agency of assessing, recommending, and acting, but accountability does not transfer with it. An underwriter’s value is the signature, a Board’s value the commitment. As decision substance migrates to AI while sign-off structures stay unchanged, the question of who bears the consequence when it is wrong keeps its old answer, the humans at the top, while the difficulty of exercising that accountability grows.