Tagged with: #business-strategy

In June 2024, I proposed that organisations would need to compensate workers whose expertise became embedded in corporate AI models. The rise of personal AI agents inverts that assumption entirely: individuals are already investing thousands annually in always-on agents that encode their professional judgement, domain expertise, and decision-making patterns — capability that belongs to them, not their employer. This article explores what happens when the most valuable AI in your organisation walks in with the employee and walks out when they leave, and why the IP boundaries, contractual frameworks, and talent strategies needed to navigate this shift don’t yet exist.

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 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.

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.

AI governance is fragmenting into incompatible systems — Europe prioritising trust through transparency, America pursuing speed through scale, China maintaining control through integration — forcing Boards to choose rather than compromise. In this article, I explore the sovereignty trilemma and present three strategic stances for navigating these landscapes without fracturing your strategy.

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.

AI answer engines like Claude, ChatGPT, and Perplexity are fundamentally reshaping how value flows through information ecosystems. Unlike the web era’s simple traffic exchange, these systems synthesise and enhance proprietary data, creating entirely new possibilities for value creation. Bloomberg and the Financial Times demonstrate how organisations can transform this shift into competitive advantage through innovative AI models and sustainable value exchange frameworks. This article explores how Boards can leverage these lessons to build ecosystems where data owners, AI platforms, and users all benefit from the extraordinary value being created.

Traditional ROI calculations capture the obvious: cost savings, faster processes, fewer errors. Yet AI’s most powerful returns often emerge much later, as cascading second and third-order effects transform capabilities, business models, and competitive position. In this article I explore how Boards can identify and measure these hidden gains using leading, lagging, and predictive indicators, while ensuring governance frameworks balance opportunity with risk.

Last week in Washington DC, I hosted a dinner where Professor Ajay Agrawal presented data showing that individual AI training clusters will soon require more electricity than entire nations currently generate. Whilst the UK government announces plans to become an AI-first economy, the mathematics are stark: UK businesses face energy costs four times higher than US competitors, creating dangerous dependencies on foreign AI infrastructure.

Even the most meticulously crafted AI business case can fail at the final hurdle - securing Board buy-in. With research showing 88% of AI pilots never reach production, effective presentation isn’t just about gaining initial approval but establishing the path to full implementation. This final article in my series explores how to present AI investment proposals to Boards, addressing their six key areas of concern while building the stakeholder confidence necessary for successful transformation. By understanding Board dynamics, anticipating objections, and structuring presentations that balance strategic vision with implementation rigour, you can navigate the critical journey from business case to production-scale AI.