Posts tagged with #governance set out how to ensure AI decisions align with organisational values through governance structures that balance agility with appropriate controls.
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.
Minimum lovable governance marks a shift from episodic compliance scrambles to continuous, embedded oversight that people actually want to use. In this article I explain how governance can achieve necessary guardrails whilst earning adoption rather than resistance — like an arbour that guides growth without constraining it. For Boards, minimum lovable governance presents a practical path: the operating principle that makes AI governance work when traditional approaches simply get routed around.
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.
Deloitte’s 2025 survey shows 69% of boards discuss AI regularly yet only 33% feel equipped to oversee it, whilst MIT finds workers at over 90% of companies already use shadow AI without governance – exposing the execution gap between strategy and action. In this article, I provide sequenced, mutually reinforcing actions that transform the Complete AI Framework from guiding policy into systematic execution, building compound advantage from Day 1 amnesty through Quarter 4 scaling rather than accumulating another collection of disconnected initiatives.
The true AI governance challenge isn’t pilot failures – it’s that Boards’ six core concerns demand simultaneous orchestration yet receive sequential attention through project-level adoption. In this article, I show how these interconnected priorities form the proper diagnostic lens for AI governance, revealing why addressing them together as a whole rather than individually determines the difference between transformation and yet another failure.
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.
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.