Tagged with: #ai-adoption

AI coding tools don’t close the expertise gap — they amplify it. Research shows senior developers capture twice the productivity gains of juniors, while a randomised controlled trial found experienced developers actually worked slower with AI than without, the hidden taxes of verification offsetting initial speed. This article explores the verification premium — and why Boards should ask not “can we use AI to write code cheaper?” but “do we have the verification capability to ensure AI-generated code creates value rather than debt?”

Workers with genuine AI capabilities command premiums of 28-56%; those targeting AI-exposed roles without substantive skill development face a 29% earnings penalty. The same roles, opposite outcomes, and the difference lies in the quality of capability investment, not access to tools. This article examines why this bifurcation extends to the Boardroom itself, where the IoD now positions AI competence as a core NED responsibility. For Boards, the strategic question becomes: is your workforce developing verification and judgement, or just collecting certifications — and can you tell the difference?

AI’s primary value isn’t replacing people, it’s releasing the intellectual capital trapped in undifferentiated work. Yet in many Boardrooms, workforce reduction remains the default success metric for AI initiatives. This article makes the case for the redeployment dividend: redirecting freed human capacity toward outcome-impacting work, complex judgement, and innovation that AI cannot replicate. For Boards, the strategic question shifts from “how many roles disappear?” to “what valuable work aren’t we doing because our best people are buried in tasks they don’t need to do?”

As we return to our desks for 2026, the AI forces demanding attention aren’t distant possibilities but strategic choices already in motion. AI is embedding itself into enterprise applications faster than organisations can govern it, whilst simultaneously eroding the human capabilities needed to oversee it. In this article I examine five of these forces — AI’s shift from content generation to decision support, inference economics reshaping deployment strategy, embodied AI introducing physical-world liability, verification gaps exposing governance failures, and AI governance professionalising into systematic capability.

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.

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.

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.

Boards are approving AI initiatives at record pace – 92% of companies plan increased investment – yet only 1% have achieved AI maturity: the gap reveals a fundamental misconception about AI strategy. In this article, I expose why accumulating business cases creates fragmentation rather than transformation, and why Boards must shift from project-level approvals to orchestrating systematic AI capability before their disconnected pilots become an expensive collection of failures.

Following your AI amnesty programme, speed matters: employees who disclosed shadow AI usage expect enablement, not restriction - the post-amnesty window is critical. In this article, I provide a roadmap for transforming discoveries into governed capabilities that boost organisational productivity and reduce the risk of AI moving back into the shadows again.