Master the complexities of governing AI systems that make millions of decisions per second through frameworks designed for board-level oversight. These articles address the six key priorities boards face - from strategic alignment and ethical responsibility to risk management and stakeholder confidence. Learn how to establish accountability for AI decisions, implement human-in-the-loop processes, and create governance structures that enable innovation while ensuring transparency, compliance, and responsible AI deployment.
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.
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.
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.
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.
The fifth article in my AI Centre of Excellence series provides a comprehensive guide to building essential capabilities across the Five Pillars. Moving from governance frameworks to practical implementation, it details how to develop capabilities that match your multi-speed AI reality - from transforming shadow AI into governed innovation, to creating comprehensive literacy programmes. Complete with a 90-day implementation sprint, maturity assessment tools, and practical templates, this article transforms theoretical understanding into actionable capability development.
In the first three articles of this series, we’ve established why Boards need an AI Centre of Excellence (AI CoE), explored the eighteen essential functions that drive AI success, and used the AI CoE Simulator to reveal the multi-speed reality of AI adoption. Now comes the critical question: how do you structure an AI CoE that can effectively govern this complex, multi-speed landscape?
In the first two articles of this series, I’ve explored why boards need an AI Centre of Excellence (AI CoE) and detailed the eighteen functions that determine AI success. But before you can build effective governance, you need to understand where you actually are today - not where you think you are, or where you’d like to be. To help boards navigate this challenge, I’m sharing the AI CoE Simulator - a practical assessment tool taken from my AI governance toolkit that operationalises the AISA framework for real-world use.
Every AI Centre of Excellence (AI CoE) needs a clear operational mandate. Through my experience designing and building Cloud Centres of Excellence for AWS customers, extensive research, and practical implementation, I’ve identified the essential functions that provide comprehensive AI governance without creating bureaucratic overload. These functions, organised around the Five Pillars mechanism, ensure your AI CoE can effectively govern multi-speed adoption while building the capabilities needed for sustainable AI transformation. Understanding these functions, and how they interconnect is crucial for boards establishing effective AI governance.