Tagged with: #ai-implementation

World models mark AI’s shift toward true predictive power, allowing systems to simulate future scenarios and help businesses move from reacting to events to anticipating them. Drawing on emerging research, including Yann LeCun’s work on simulation-based intelligence, this article highlights the practical gains industries like aviation and finance are seeing in operational efficiency through these future-looking tools. For Boards, world models present a tantalising future: the opportunity to turn future insight into present advantage.

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.

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.

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.

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.

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.