Tagged with: #enterprise-ai

Forty-two percent of companies abandoned the majority of their AI initiatives this year — not because AI failed, but because organisations applied generative AI to problems better solved by traditional machine learning or deterministic automation. This article examines the recalibration underway as sophisticated adopters discover that LLMs excel at specific tasks but prove expensive and unreliable when mismatched to problem domains. For Boards, this shift presents an opportunity to right-size investments through hybrid architectures that match capabilities to problems, capturing value through strategic deployment rather than universal LLM adoption.

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.

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.

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.

McKinsey’s 2025 research shows whilst 88% of organisations use AI, only 23% have successfully scaled agentic systems — and even fewer integrate disciplines beyond generative, limiting value to linear gains rather than exponential growth. In this article, I expand the agentic AI definition from “generative AI in a loop” to compound loops that coordinate multiple AI disciplines simultaneously, creating interaction effects that multiply capabilities, simplify governance through unified frameworks, and enable Boards to tackle broader business challenges for lasting competitive advantage.

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.

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.

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.