Tagged with: #ai-adoption

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.

With a 68% surge in shadow AI usage and 54% of employees saying they would use AI tools even if they were not authorised by the company, Boards face a governance challenge traditional compliance cannot solve. This article presents AI amnesty as an important first step to minimum lovable governance - transforming hidden risks into strategic assets whilst capturing employee-validated innovation. When 95% of enterprise AI pilots fail to deliver measurable ROI yet shadow AI thrives everywhere, the path forward isn’t enforcement but structured disclosure programmes that build trust and position early adopters as governance standard-setters.

New research from MIT provides compelling validation for the AI adoption challenges I’ve been highlighting since 2024: whilst organisations are investing billions of dollars in generative AI, only 5% successfully move from pilot to production. The study confirms what I’ve observed first-hand — the difference between transformation and experimentation lies in coherent governance, not technology capability.