エピソード

  • The Deadline Moved: What the EU AI Act Deferral Reveals About Boards

    The Deadline Moved: What the EU AI Act Deferral Reveals About Boards
    2026/08/09
    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 episode of The Board in the Machine, Mario Thomas, Chartered Director and Fellow of the Institute of Directors, examines what the deferral reveals about any Board whose AI governance was built around a regulatory date. A governance programme that exists because a deadline was coming is a compliance project, and when the date moved, the discipline moved with it. The instability is global: a proposed standards body in the United States covers only the frontier laboratories, the United Kingdom has asked existing regulators to govern at the point of use, and China regulates piece by piece while drafting a comprehensive law. No regime on offer covers everything a Board might deploy. Mario sets out what never moved: the directors' duties that predate the AI Act, the obligations that still apply from 2 August, and Minimum Lovable Governance as the anchor that holds under any regulatory timetable. He closes with three questions for the next Board agenda: which deployments would count as high-risk, where the organisation's ethical bar sits and who set it, and which obligations remain live regardless of the deferral. This episode is for directors, chief executives, and Boards who deploy AI systems today under law that applies today. Accountability is a condition of deployment, not a product of regulation. Read the full article at mariothomas.com
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    11 分
  • The Balancing Item: The AI Oversight Cost Your Business Case Never Priced

    The Balancing Item: The AI Oversight Cost Your Business Case Never Priced
    2026/07/26
    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 labour is real, and in most organisations it is absorbed silently by people on top of their existing roles. In this episode of The Board in the Machine, Mario Thomas, Chartered Director and Fellow of the Institute of Directors, examines the cost the True Investment Profile predicted and Boards have not priced. Research published in March 2026, surveying nearly one and a half thousand full-time workers at large American companies, gives the consequence a name: a distinct mental fatigue that attaches to heavy AI oversight loads, while workers using AI only to replace routine tasks report less burnout, not more. The burden is a work-design outcome, and work design is something Boards can govern. Mario sets out the governance response. Price the oversight in every business case. Design review into roles rather than on top of them. Set span-of-control expectations for human and AI working. Balance the portfolio between burnout-reducing replacement and fatigue-generating augmentation left unresourced. Watch the leading indicators of a control under strain: review backlogs, rubber-stamping rates, error escapes, and attrition in oversight-heavy roles. This episode is for directors, chief executives, and the Boards who attest to effective human oversight while the humans providing it are at cognitive capacity. The closing question is the one that matters: which of those attestations would survive an honest capacity audit? Read the full article at mariothomas.com
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    13 分
  • Governing the Redeployment Dividend: Turning Saved Hours Into Value

    Governing the Redeployment Dividend: Turning Saved Hours Into Value
    2026/07/19
    Teams that deploy AI save the equivalent of five hours per person per week, and most of that time is then spent on non-value-added tasks. The saving is real. What happens to it next is the problem, and it is a problem that belongs to the Board. In this episode of The Board in the Machine, Mario Thomas, Chartered Director and Fellow of the Institute of Directors, returns to the Redeployment Dividend, the argument that AI's real prize is releasing intellectual capital from undifferentiated work rather than cutting headcount. The evidence now shows what happens when that dividend is left ungoverned. Surveys across HR and sales find freed hours reabsorbed by the low-value work that was already there, and a study of nearly six thousand executives across four countries finds that while most firms now use AI, roughly nine in ten report no measurable impact on productivity at their own firm. The explanation is an accountability gap. AI owns the execution of the work. Managers own the workflow it sits within. But in most organisations, nobody owns whether the freed capacity creates value. Mario sets out a single governance discipline to close that gap: change the success metric from headcount to redirection, give redeployment an owner, redesign the work to receive the recovered hours, measure the downstream outcomes rather than the time saved, and govern what the organisation deliberately stops doing. This episode is for directors, chief executives, and the Boards who can already quote the hours their AI tools recover but cannot yet say where those hours went. Time saved is an input, never the outcome. Read the full article at mariothomas.com
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    12 分
  • AI and the CEO: Choosing the Bets That Matter

    AI and the CEO: Choosing the Bets That Matter
    2026/06/21
    Most chief executives report no revenue gain and no cost saving from AI, while a fifth of organisations capture nearly three quarters of the value. That gap, between spending on AI and choosing well, is the working condition of the modern chief executive. 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 chief executive's role, and what it leaves exactly where it was. The conditions of the work have changed. The duties have not. He sorts the role into the duties AI now presses hardest. The dominant one is strategic: under the noise, the chief executive must choose the few bets that matter, because the value is won or lost in that choice, not in how much is spent. The operational duty is to change how the company actually works, since there is no credit for buying AI, only for redesigning the work around it. The market duty is to signal adoption without overclaiming, a line where company law and the regulators now reach the individual personally. And beneath all of it sits the leadership work AI barely touches: setting the aspiration, holding the line, and bearing the accountability. This episode is for chief executives, chairs, and the boards and directors who hold them to account, working out where AI belongs in the role and where it does not. AI changes who builds and who signals. It does not change who answers. Read the full article at mariothomas.com
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    12 分
  • Not Everything Needs AI: The Questions That Come Before the Decision

    Not Everything Needs AI: The Questions That Come Before the Decision
    2026/07/12
    As much as a fifth of an enterprise application estate turns out to be no longer useful once someone finally asks who still uses it. The same is true of the work itself, and it is the reason the first question about AI is never which model to choose. In this episode of The Board in the Machine, Mario Thomas, Chartered Director and Fellow of the Institute of Directors, takes up the question left open by The Great Remaking. If the business is being remade around AI, what exactly gets remade, what gets remade with a model, and what is left alone. The mandate is settled. The judgement inside it is not. Asked at the IoD Chartered Director Conference how he decides which AI to use, he declined the premise. He starts with three questions instead: what are you trying to do and why, how do you do it today and does it still need doing, and only then, why do you think you need AI at all. The second is the one that does the real work, because it is where a business hears itself describe a piece of work that stopped making sense years ago. He works through a task from his own practice that looked like an AI job, was built as one, and returned a different answer almost every run, until a few lines of ordinary code did it correctly every time for a fraction of the cost. This episode is for directors, chief executives, and the Boards deciding where AI belongs and where it does not. Better business judgement produces better AI decisions. The willingness to say no is what makes every yes credible. Read the full article at mariothomas.com
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    8 分
  • 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