Tagged with: #board-governance

In 2025 Boardrooms saw a collective shift in how they thought about AI’s role. What they spent 2023 and 2024 reacting to became a question of strategic investment in organisational infrastructure. They moved from “what can it do?” and “should we use it?” to “how do we navigate competing pressures and make this core to how we operate?” In this article, I examine the five interconnected inflections that drove this shift — and what they mean for Boards entering 2026.

Forty-two percent of companies abandoned the majority of their AI initiatives this year — not because AI failed, but because organisations applied generative AI to problems better solved by traditional machine learning or deterministic automation. This article examines the recalibration underway as sophisticated adopters discover that LLMs excel at specific tasks but prove expensive and unreliable when mismatched to problem domains. For Boards, this shift presents an opportunity to right-size investments through hybrid architectures that match capabilities to problems, capturing value through strategic deployment rather than universal LLM adoption.

America’s 19GW power shortfall by 2028 is forcing hyperscalers to build their own generation, but the strategic insight is what happens next: surplus capacity transforms AI infrastructure operators from energy consumers into grid actors. This article examines how distributed generation reshapes the relationship between technology companies and national grids, exploring whether the UK’s smaller system enables transformation or creates concentration risk. For Boards, this evolution demands governance frameworks that address not just AI deployment but grid participation — before the transition forces answers upon them.

Boards frequently overestimate AI maturity by focusing on tool deployments rather than genuine capabilities, mistaking isolated pilot successes for systemic organisational readiness. This article exposes the three patterns that create the illusion—tool-centric thinking, pilot success traps, and hype-driven metrics—and provides a diagnostic framework to reveal true position and enable targeted advancement.

Minimum lovable governance marks a shift from episodic compliance scrambles to continuous, embedded oversight that people actually want to use. In this article I explain how governance can achieve necessary guardrails whilst earning adoption rather than resistance — like an arbour that guides growth without constraining it. For Boards, minimum lovable governance presents a practical path: the operating principle that makes AI governance work when traditional approaches simply get routed around.

World models mark AI’s shift toward true predictive power, allowing systems to simulate future scenarios and help businesses move from reacting to events to anticipating them. Drawing on emerging research, including Yann LeCun’s work on simulation-based intelligence, this article highlights the practical gains industries like aviation and finance are seeing in operational efficiency through these future-looking tools. For Boards, world models present a tantalising future: the opportunity to turn future insight into present advantage.

While organisations transfer decision-making agency to AI systems, accountability remains with humans, yet boards approve AI deployment without investing in the verification capability needed to ensure it. In this article, I demonstrate why this creates a strategic choice with measurable consequences: augmentation preserves expertise pipelines whilst achieving efficiency gains, but replacement destroys capabilities that cannot be rebuilt, turning apparent cost reduction into systematic competitive disadvantage.

McKinsey’s 2025 research shows whilst 88% of organisations use AI, only 23% have successfully scaled agentic systems — and even fewer integrate disciplines beyond generative, limiting value to linear gains rather than exponential growth. In this article, I expand the agentic AI definition from “generative AI in a loop” to compound loops that coordinate multiple AI disciplines simultaneously, creating interaction effects that multiply capabilities, simplify governance through unified frameworks, and enable Boards to tackle broader business challenges for lasting competitive advantage.

Agentic AI has become this year’s poster child, dethroning generative AI as the technology everyone wants to discuss. Yet fundamental misunderstandings about what agentic systems actually do create barriers to successful adoption. This article demystifies the hype by revealing the core truth: agentic AI is generative AI in a loop, where the machine drives iteration instead of a human, making the strategic question not about technology sophistication but where to consciously transfer decision-making agency from people to systems, and at what scale.

Deloitte’s 2025 survey shows 69% of boards discuss AI regularly yet only 33% feel equipped to oversee it, whilst MIT finds workers at over 90% of companies already use shadow AI without governance – exposing the execution gap between strategy and action. In this article, I provide sequenced, mutually reinforcing actions that transform the Complete AI Framework from guiding policy into systematic execution, building compound advantage from Day 1 amnesty through Quarter 4 scaling rather than accumulating another collection of disconnected initiatives.