The Board in the Machine

Research from Tenable found that 63% of organisations lack any AI governance policies, yet McKinsey's research shows 88% using AI in at least one function. Behind that gap sits a mistaken assumption: that delegating work to AI delegates accountability for its quality. Courts, regulators, and clients consistently reject that assumption, and organisations planning to replace junior roles with AI are reducing the capacity needed to verify what AI produces.

In this episode of The Board in the Machine, Mario Thomas — Chartered Director and Fellow of the Institute of Directors — argues that AI is best understood as a brilliant graduate trainee: highly productive, short on judgement, and needing expert review before its work reaches a client. He explains why AI amplifies rather than reduces the need for subject matter expertise, and why a Board that approves deployment without matching investment in verification is approving the conditions for the failures now documented in court cases worldwide.

Mario works through the evidence: Stanford research finding that general-purpose AI models produce false information on legal queries between 58 and 82% of the time, research based on payroll data suggesting a 13% decline in AI-exposed entry-level roles, a BCG experiment finding productivity gains of 20 to 40% when AI augmented junior and senior staff, and PwC's AI Jobs Barometer showing three times the revenue growth per employee in AI-exposed industries, but only with skilled oversight. He sets out the five investments a Board should approve alongside any AI deployment and the questions to require answered first, including who has the expertise to verify the outputs today, and where the senior experts will come from in 2030.

This episode is for directors, chairs, and executives weighing proposals to capture AI efficiency gains by replacing junior roles, because the expertise pipeline those roles sustain takes a decade or more to rebuild once gone, if it can be rebuilt at all. Read the full article at mariothomas.com

McKinsey's 2025 State of AI report finds that 88% of organisations use AI in at least one function, yet only 23% have scaled agentic AI, and fewer still combine more than one AI discipline within it. The disciplines left outside the loop, machine learning, computer vision, and process automation among them, bring decades of proven use beside generative AI's two-year track record. Generative-only architectures carry a lock-in risk that becomes more expensive to escape the longer the move to multiple disciplines is deferred.

In this episode of The Board in the Machine, Mario Thomas — Chartered Director and Fellow of the Institute of Directors — takes the definition of agentic AI as generative AI in a loop and expands it: the AI in the loop need not be generative at all. He sets out the compound loop, an agentic system coordinating several AI disciplines in parallel on the same problem, as a person combines sight, pattern recognition, reasoning, and language without conscious effort. The counter-intuitive claim for a Board is that one coordinated loop can be simpler to govern than several separate systems, because audit trails, boundaries, and intervention points sit at a single coordination layer.

Mario works through the evidence: McKinsey's analysis reporting as much as a 40 to 60% reduction in governance overhead, BCG's research showing 20 to 30% efficiency gains over single-discipline approaches, California Management Review research demonstrating 20 to 30% inventory cost reductions, and PwC's AI Agent Survey reporting early adopters achieving up to 45% improvements in key metrics. The takeaway is a question for Boards: are the organisation's agentic initiatives single-discipline builds that will hit a capability ceiling, or are they integrating disciplines so value compounds?

This episode is for directors and executives whose organisations are piloting or scaling agentic AI, and who need to establish, while multi-discipline coordination is still rare, whether generative-only is an examined decision or an assumption. Read the full article at mariothomas.com