Tagged with: #ai-transformation

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.

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.

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.

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.