Tagged with: #leadership

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.

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.

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.

When investment banks dedicate significant research to power constraints and markets reward energy-backed infrastructure with substantial valuations, UK Boards operating with energy costs four times higher than competitors need frameworks for navigating this validated reality. This article examines how Goldman Sachs’ institutional analysis transforms energy sovereignty from policy concern to strategic imperative, exploring practical approaches for UK Boards seeking competitive positioning in an energy-constrained AI landscape.

The EU’s General-Purpose AI (GPAI) Code of Practice, effective August 2025, signals a new era of regulatory divergence. While the EU sets transparency and systemic risk guardrails, the U.S. accelerates through deregulation. For Boards, the challenge isn’t choosing sides but mastering dual-track governance — turning regulatory complexity into strategic advantage.

In the race to deploy agentic AI, organisations face a fundamental paradox: they’re building tomorrow’s autonomous systems on yesterday’s infrastructure. Drawing from the cloud transformation journey, this article explores how the same legacy architectures that constrain agentic AI also present an unprecedented opportunity. By retiring technical debt, organisations can clear the path for technological change that will define the next era of business competition. For Boards, the choice is clear: deploy agents within existing constraints, or use them to architect the foundation for future competitive advantage.

You’ve built your AI Centre of Excellence. It’s governing multi-speed adoption, delivering value, and - as we explored in the previous article - scaling beyond pilots to enterprise transformation. But here’s the uncomfortable truth: the AI landscape will look radically different in eighteen months. Multi-agent systems, decentralised agent ecosystems, embodied AI, neurosymbolic reasoning, quantum-AI hybrids, cross-modal intelligence, federated AI networks, and artificial superintelligence will challenge every governance framework you’ve carefully constructed. Having achieved scale, this final article tackles the strategic imperative of continuous evolution: how to future-proof your AI CoE to govern these disruptive technologies whilst building the adaptive capacity to thrive on change rather than being disrupted by it.