The Board in the Machine

Only 33% of boards feel equipped to govern AI, yet AI systems are making millions of decisions per second on their behalf. The gap between the volume of information boards must process and the quality of assurance they receive has never been wider.

In this episode of The Board in the Machine, Mario Thomas — Chartered Director and Fellow of the Institute of Directors — explains what automated reasoning is and why it matters for board-level governance. He distinguishes it clearly from AI and predictive analytics, showing how formal logic and mathematical proof offer boards a categorically different quality of input: not better confidence, but certainty.

Drawing on verified applications in aviation (DO-178C/DO-333), financial services (Basel III/IV compliance), cybersecurity (TLS 1.3 verification), and emerging work in computable contracts, the episode sets out where proof is already replacing probability in regulated industries. Listeners will come away understanding what reasoned indicators are, where automated reasoning applies to governance questions they already own, and how to ask whether their organisation's assurances rest on sampling or completeness.

If you sit on a board, chair an audit or risk committee, or advise directors on AI governance, this episode gives you the vocabulary and the framework to ask a question most boards have not yet considered: are your organisation's assurances based on probability, or proof?

Read the full article at mariothomas.com.

Two-thirds of boards report limited to no knowledge or experience with AI. Yet most governance guidance stops at naming the gap. This episode defines what closes it.

In this episode of The Board in the Machine, Mario Thomas — Chartered Director and Fellow of the Institute of Directors — sets out what directorial AI literacy actually consists of. Not technical fluency. Not a two-day awareness course. But four specific capacities that allow a director to interrogate maturity claims, assess whether governance is operational or merely presentational, and exercise genuinely independent judgement on AI decisions they cannot fully see.

Drawing on the IoD's 2025 NEDs Reimagined Commission and Deloitte's Global Board Survey, Mario maps those capacities against the three governance obligations every non-executive carries — and gives you the diagnostic questions to apply at your next Board meeting.

If you recognise AI as important but haven't yet translated that into specific literacy development, this episode is where to start.

Read the full article at mariothomas.com.

Pilot counts, AI budget lines, the existence of an AI strategy, and chief AI officer appointments measure whether money is being spent and projects are running. None of them tells a Board whether the organisation is remaking its work, which is the only thing that determines competitive position when the essence of work itself is changing.

In this episode of The Board in the Machine, Mario Thomas, Chartered Director and Fellow of the Institute of Directors, closes The Great Remaking series with a diagnostic built on the three compounding loops introduced in the earlier parts: data, talent, and process redesign. He sets out why the usual Board questions fail, measuring the algorithm and technology layers that BCG's research puts at thirty per cent of AI value while the people component that carries seventy per cent goes unprobed, and why only five per cent of organisations report substantial financial gains from AI while thirty-nine per cent describe it as in production at scale.

Mario then works through the probing questions for each loop and the interpretive guide to what credible answers sound like. Which specific workflow has been redesigned, not augmented, around AI in the past eighteen months, and what data does it now generate? Are people developing AI capability by doing redesigned work or mainly through training programmes, and what would the organisation actually lose if its most AI-capable people left tomorrow? How many fundamental restructurings of work has the organisation completed in two years, and what has it learned about redesign itself?

This episode is for directors and the Boards whose AI conversations still run on activity metrics. The diagnostic is not a governance checklist; it is a way of telling the difference between organisations genuinely building the loops and those accumulating deployments that look like progress. Read the full article at mariothomas.com

Every previous technology wave rewarded fast followers: let early movers absorb the risk, observe what works, then acquire or build the equivalent at lower cost. The Great Remaking does not, because the source of advantage is not a product that can be studied and replicated. It is operational accumulation, built only through time spent doing the work differently.

In this episode of The Board in the Machine, Mario Thomas, Chartered Director and Fellow of the Institute of Directors, examines why the gap between organisations redesigning their work around AI and those still augmenting it keeps widening, and why a late mover cannot invest its way out. BCG finds that only 10% of AI value comes from algorithms, 20% from the technology to implement them, and 70% from redesigning the people component, so 70% of the gap cannot be closed by purchasing better technology. The Stanford HAI AI Index 2025 records frontier-level inference costs falling 280-fold between 2022 and 2024, shifting advantage from which model an organisation accesses to what it has built around it.

Mario then works through the three self-reinforcing loops. The data loop: AI-integrated workflows generate contextualised, AI-refined data that exists only where work has been redesigned. The talent loop: people in genuinely redesigned workflows develop tacit competencies about when to trust and when to challenge AI outputs, a divergence PwC's 2025 Global AI Jobs Barometer captures in a 56% wage premium for AI skills. The process redesign loop: each redesign cycle builds institutional capability for redesign itself. The three interact, and the leaders' loops accelerate precisely as late movers begin to move.

This episode is for directors and the Boards that still deploy fast-follower logic as a reason not to move first. The question that maps onto the loops is not how much AI the organisation is deploying, but whether it is building any of them. Read the full article at mariothomas.com

AI is not remaking the four dimensions of work, thinking, deciding, creating, and delivering, at the same speed, through the same mechanisms, or toward the same end state. Treating them as one strategic question is most organisations' mistake. A generic approach across all four is not a strategy; it is an assumption.

In this episode of The Board in the Machine, Mario Thomas, Chartered Director and Fellow of the Institute of Directors, makes the case for the asymmetry at the heart of The Great Remaking. He traces each dimension along the trajectory from augmentation through restructuring to substitution. Thinking work is already at scale on the restructuring curve. Creating has moved from augmentation to restructuring in three years. Deciding is structurally different, because AI can increasingly assume the agency while accountability does not transfer with it. Delivering is furthest from substitution but accelerating on cost through embodied AI, with Goldman Sachs Research reporting humanoid manufacturing costs down 40 per cent year-on-year.

Mario works through the evidence dimension by dimension. McKinsey's November 2025 Global Survey found high performers 2.8 times more likely to have redesigned their workflows. HBR's January 2026 executive survey puts 39 per cent of organisations at AI in production at scale, up from 5 per cent two years earlier. Deloitte's March 2026 Global Human Capital Trends report finds 60 per cent of executives regularly use AI to support decisions while only 5 per cent of organisations consider themselves leading in AI-augmented decision maturity. The human residual differs in each: judgement in thinking, the willingness to bear consequences in deciding, originality and taste in creating, adaptability and trust in delivering.

This episode is for directors and Boards who need to know which dimensions are moving fastest in their sector, where redesign would compound soonest, and how many of the organisation's decisions are substantively human rather than AI recommendations with human sign-off. Read the full article at mariothomas.com

An AI agent that can see only the public internet is no more useful to a business than a very expensive search engine. Generic inputs produce generic outputs. The intelligence is not the constraint; the connectivity is.

In this episode of The Board in the Machine, Mario Thomas, Chartered Director and Fellow of the Institute of Directors, explains Model Context Protocol, the infrastructure standard that connects agents to the proprietary systems that constitute competitive advantage. He uses the USB analogy: one standardised interface replacing a bespoke integration for every agent and system, with an MCP server acting as an auditable control point between AI behaviour and core systems. Open-sourced in late 2024, MCP has since been adopted or committed to by Salesforce, SAP, Google, and Microsoft. Deloitte's 2026 State of AI in the Enterprise report finds that 74% of organisations plan to deploy autonomous agents within two years, yet only 21% have mature governance for them.

Mario then works through the systems agents cannot see: vendor-built MCP servers for modern SaaS platforms, buildable connectivity for recent internal systems, and the legacy platforms that hold the most critical data with no near-term route to connectivity. He extends the governance question from data access to data lifecycle, setting out the inference channel problem: an agent's outputs carry intelligence derived from source data without that data's access controls. He cites IBM's 2025 Cost of a Data Breach Report, in which 97% of organisations suffering an AI-related security incident lacked proper AI access controls, and proposes a rapid visibility audit plus four Board questions on strategic visibility, the permission model, the vendor relationship, and data lifecycle.

This episode is for directors and the Boards whose technology teams are connecting agents to systems, often by default. MCP is not a technology decision dressed up as a governance question; it is a governance question with a technology dimension. Read the full article at mariothomas.com

Five technology revolutions changed how organisations operate, but none of them restructured the essence of work itself. AI does, and the organisations that redesign how work is structured are already pulling away from those that bolt AI onto existing processes.

In this episode of The Board in the Machine, Mario Thomas, Chartered Director and Fellow of the Institute of Directors, defines the four irreducible dimensions of work that AI is remaking: how organisations think, decide, create, and deliver. He explains why this transformation is different in kind from the desktop, internet, web, mobile, and cloud waves that preceded it, and why the human residuals of judgement, accountability, taste, and relationships matter more as machine capability grows.

The argument draws on McKinsey's State of AI 2025 finding that 88% of organisations now use AI in at least one business function, BCG research showing that the roughly 5% of organisations achieving substantial financial gains from AI deliver three-year total shareholder returns around four times higher than laggards, the International Federation of Robotics' figures on industrial robot installations doubling in a decade, and the White House Council of Economic Advisers' January 2026 report on AI and the Great Divergence. The takeaway is that The Great Remaking is a race with compounding consequences, and late movers cannot close the gap through incremental catch-up.

This episode is for Boards, executives, and leadership teams deciding whether to treat AI as an enhancement or as a restructuring of how their organisation works, and who want a way to locate where AI is augmenting, restructuring, or substituting the work their organisation does today. Read the full article at mariothomas.com

Individuals are now paying thousands of pounds a year for personal AI agents: always-on systems that learn their workflows and encode their decision-making patterns. That capability belongs to the person who built and trained it, not to any employer, and it goes wherever they go.

In this episode of The Board in the Machine, Mario Thomas, Chartered Director and Fellow of the Institute of Directors, revisits the argument he made in June 2024, that organisations capturing worker expertise in their AI models would owe those workers a share of the ongoing value. Personal agents invert that assumption. When the worker owns the most capable AI in the building, the organisation has to negotiate access to it, and the employment relationship shifts from paying for time and knowledge to paying for access to capability infrastructure. The mechanisms for this do not exist yet, which is why Boards should consider it now.

Mario sets out the governance questions the inversion raises: where the organisation's intellectual property ends and the individual's begins when an agent is trained on expertise developed on the job, and whether the organisation bans personal agents that outperform its own provision or negotiates access and accepts a dependency it does not control. He describes an agent ownership premium splitting the workforce between those who own productive AI capital and those who do not, a retention problem when institutional knowledge leaves structured and ready to deploy elsewhere, and employment contracts, IP clauses, and non-compete agreements all drafted for a world where the organisation owned the tools. He is candid that the piece is about trajectory, not timetable.

This episode is for directors and Boards who would rather meet these questions in a scenario discussion than in a talent loss they did not anticipate. The most valuable intelligence in an organisation may soon belong to the people who work there. Read the full article at mariothomas.com

The demand signal for AI's next infrastructure phase sits in what consumers are voluntarily paying for always-on assistance. Heavy users of OpenClaw, an open-source agent that passed 100,000 GitHub stars within weeks of launch, report spending $10 to $25 a day to keep a personal agent running continuously: $3,650 to $9,125 a year, more than their combined entertainment subscriptions.

In this episode of The Board in the Machine, Mario Thomas, Chartered Director and Fellow of the Institute of Directors, examines the inference migration: the shift from episodic, query-response AI to persistent agents running around the clock, and what consumer spend signals for enterprise demand. He traces the ChatGPT precedent from consumer novelty in November 2022, through the shadow wave Menlo Security documented as 90 per cent of employees using AI outside formal controls, to formalisation in 2024, and applies the same three-year cycle to agentic AI: shadow agentic adoption in 2026, formalised enterprise platforms in 2027.

Mario then works through the objection Boards most often raise, that enterprise processes need determinism. He argues it covers a narrower slice of work than assumed: soft processes such as drafting and synthesis tolerate nondeterministic output, while hard processes such as compliance and financial reporting are better wrapped with predictive intelligence than replaced. He sets out what minimum lovable governance means for agents: proportionate controls, clear policies on persistent access, monitoring for unauthorised API connections, and an amnesty pathway from shadow use to governed capability. He also flags the energy multiplier: always-on inference removes the overnight lull, and with UK industrial electricity at roughly four times US rates, energy policy becomes AI policy.

This episode is for directors and Boards whose AI policies still address only episodic chatbots. The window is 2026: establish governance now, or discover shadow agentic AI spreading by the second half of the year and retrofit controls on adoption already underway. Read the full article at mariothomas.com

Boards steward buildings, machinery, and vehicles with condition assessments, named owners, maintenance budgets, and impairment tests. Data, which may now contribute more to enterprise value than the buildings that house it, receives none of them. Ownership fragments, quality degrades undetected, and nobody asks whether a dataset is fit for the purposes now being asked of it. A physical asset treated that way would be condemned.

In this episode of The Board in the Machine, Mario Thomas, Chartered Director and Fellow of the Institute of Directors, examines why data is invisible to governance and what that invisibility costs. Intangible assets now represent 90% of S&P 500 market value, yet IAS 38 prohibits capitalising internally generated intangibles such as databases, so the balance sheet never sees them. Poor data quality costs organisations an average of USD 12.9 million a year, 63% of organisations lack or are uncertain about the data management practices AI requires, and the Institute of Directors reports that a quarter of directors are concerned about the absence of an internal AI policy, strategy, or data governance framework.

Mario sets out the stewardship disciplines Boards already apply to physical assets and extends them to data: condition assessments, a named owner for each strategic dataset, preventive maintenance, impairment testing, and strategic value review. He places the work in the AI Centre of Excellence, reporting to the risk committee, and applies the minimum lovable governance principle: start with the proprietary datasets that matter most, plot them by strategic value against current quality, and govern in proportion to risk. Shadow AI raises the stakes, with 22% of files uploaded to generative AI tools containing sensitive content.

This episode is for directors and the Boards who approve AI budgets without assessing the data those investments depend on. When AI makes a decision, the Board is making that decision. When accounting standards lag, governance must lead. Read the full article at mariothomas.com