『The Board in the Machine』のカバーアート

The Board in the Machine

The Board in the Machine

著者： Mario Thomas
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The Board in the Machine is the audio version of Mario Thomas's articles published at mariothomas.com, written for Boards and executives navigating the governance and strategic implications of AI and emerging technology.© 2026 Mario Thomas マネジメント マネジメント・リーダーシップ 経済学
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  • The AI Sovereignty Trilemma: When a Frontier Model Vanishes and Reality Bites

    The AI Sovereignty Trilemma: When a Frontier Model Vanishes and Reality Bites
    2026/06/14
    On 12 June 2026, a single government directive forced a leading AI provider to withdraw two frontier models from every customer overnight, including organisations the order was never aimed at. For anyone who had built a process on those models, the capability did not degrade. It disappeared. In this episode of The Board in the Machine, Mario Thomas — Chartered Director and Fellow of the Institute of Directors — takes the AI Sovereignty Trilemma he set out last year and shows it resolving from a structural argument into a dated, documented event. He separates the visible cost of sovereignty, which is that sovereign capability is dearer, from the hidden cost of the convenient alternative, paid in lost control and invisible until it is tested. He explains why a compelled model recall is not an outage but the removal of a capability by a party the Board has no standing to appeal to, and sets out the questions a Board should be able to answer without a special exercise: which deployments depend on a single model, what the fallback is, and whether it would survive the specific event. This episode is for directors, chairs, and executives who need to know where the same exposure sits in their own organisation, and whether they chose it or defaulted into it. Read the full article at mariothomas.com
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    11 分
  • AI and the CFO: Standing Behind the Numbers the Machine Produces

    AI and the CFO: Standing Behind the Numbers the Machine Produces
    2026/06/07
    Only a fifth of finance leaders judge their function ready for AI, yet most already treat it as central to how finance will work. That gap, between commitment and readiness, is the working condition of the modern CFO. In this episode of The Board in the Machine, Mario Thomas — Chartered Director and Fellow of the Institute of Directors — works through what AI changes about the finance chief's role, and what it leaves exactly where it was. The doing of the work can move to a machine. The accountability for it cannot. He sorts AI's effect on the role into four honest groups: the routine numbers work where a machine does the heavy lifting but a human still signs; the forecasting and capital decisions where AI sharpens the judgement without making the call; the shadow AI spreading across the business that the finance function cannot yet see; and the core of going concern, audit, and attestation that AI barely touches. Under the Companies Act and the FRC's 2024 Code, the signature on the accounts stays human. This episode is for CFOs, chairs, audit committee members, and the directors who rely on them, working out where AI belongs in the finance function and where it does not. AI changes who produces the numbers. It does not change who signs for them. Read the full article at mariothomas.com
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    13 分
  • Ontologies and Knowledge Graphs: Why Structure is the Next Data Frontier

    Ontologies and Knowledge Graphs: Why Structure is the Next Data Frontier
    2026/05/31
    Most organisations have made their data reliable. Far fewer have made it explain itself, and that distinction is becoming the one that separates organisations that can reason with AI from those that can only retrieve with it. In this episode of The Board in the Machine, Mario Thomas — Chartered Director and Fellow of the Institute of Directors — argues that the next frontier in creating durable AI value is structure: the ontologies and knowledge graphs that make the relationships between an organisation's customers, contracts, suppliers, and decisions explicit enough for a machine to reason over rather than merely summarise. Drawing on his own experience building an early knowledge graph from a regional newspaper archive in 1998, he shows why data quality and data structure answer two different questions, why the definitions encoded in a knowledge graph now carry the weight a chart of accounts has always carried, and why scalable proof under the FRC's 2024 Code and the Data (Use and Access) Act 2025 depends on structure rather than quality. This episode is for directors, chairs, and executives working out why their AI programmes stall, and what their data strategy assumes about structure. Read the full article at mariothomas.com
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    17 分
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