The Board in the Machine
Podcast
Mostly the audio edition of the writing: articles on AI, governance, and the boardroom, read and expanded for the road. New episodes monthly.
Browse the episodes (39)
Minimum Lovable Governance: The AI Operating Principle Boards Should Use
17 minute · The audio edition of the article.
Episode notes
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.
AI governance is too often heavy where it should be light and light where it should be heavy: elaborate approval processes for low-risk experiments, minimal oversight of high-stakes autonomous systems, and policy documents nobody reads. The result is friction without assurance. Unloved governance gets routed around, and shadow AI shows how often.
In this episode of The Board in the Machine, Mario Thomas, Chartered Director and Fellow of the Institute of Directors, explains minimum lovable governance, a concept he has referenced across several articles, and why it makes AI governance work. It borrows Eric Ries’s progression from Minimum Viable Product to Minimum Lovable Product and applies it to oversight: the smallest system that achieves the necessary guardrails and that people actually want to use. UpGuard’s November 2025 report found more than 80 per cent of employees, and nearly 90 per cent of security professionals, using unapproved AI tools: governance that exists on paper but fails to govern.
Mario sets out what lovable means operationally: embedded in how work happens, continuous rather than episodic, proportionate to risk, and clear at the point of decision. He explains why the approach has only recently become viable, pointing to the EU AI Act’s risk-tiered architecture, ISO 42001 and ISO/IEC 42006:2025 as reference points, and AI-assisted governance making continuous oversight achievable while accountability stays with humans. He then works through five principles, each with a Board test question, such as whether an auditor calling tomorrow would be answered in seconds or weeks, and whether a marketing chatbot goes through the same approval process as a credit decisioning system.
This episode is for directors, chief executives, and the Boards whose governance reports say everything is under control while most of the organisation works outside it. The choice is to build governance people route around, or governance people want to use, and the outcomes will differ accordingly. Read the full article at mariothomas.com
Read the article →World Models: The Next Horizon in AI for Predictive Enterprise Intelligence
15 minute · The audio edition of the article.
Episode notes
World Models: The Next Horizon in AI for Predictive Enterprise Intelligence
World models move AI from reacting to anticipating: systems that simulate future scenarios, with aviation and finance already seeing the operational gains.
Current AI excels at analysing what has happened. World models, an emerging approach, build internal simulations of how reality works and predict what will happen next, moving AI from recognising patterns to anticipating futures. For Boards already tracking leading and lagging indicators, they promise the technical realisation of the predictive indicator.
In this episode of The Board in the Machine, Mario Thomas, Chartered Director and Fellow of the Institute of Directors, examines world models as the next horizon in predictive enterprise intelligence, drawing on Yann LeCun’s work on the Joint Embedding Predictive Architecture and on the gains current predictive AI already delivers: aviation pioneers reporting twenty to thirty per cent improvements in operational efficiency in BCG’s AI-First Airline report, and PwC’s 2025 finding that industries exposed to AI disruption achieve three times higher revenue per employee growth. He is candid about the barriers, which LeCun himself acknowledges as huge practical impediments: computational demands that may need quantum breakthroughs, vast real-time data requirements, and energy consumption that could make large-scale world models economically unviable.
Mario sets out grounded timelines, with narrow-domain world models perhaps two to three years away and enterprise-ready systems five to seven, and argues for a dual strategy: build foundational predictive capabilities with current AI now whilst monitoring world model developments for strategic timing. He imagines the transformation across aviation, financial services and manufacturing, then turns to governance: the ethical boundaries Boards must set when a simulation reveals a profitable but harmful path, the case for minimum lovable governance, transparency about the scenarios and assumptions behind each prediction, and risk management that simulates whole consequence chains rather than single events.
This episode is for directors and Boards preparing for predictive intelligence. Which operational predictions would transform the organisation’s competitive position when world models arrive? That answer determines where to build foundations now. Read the full article at mariothomas.com
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