Governance & strategy

The Hidden Cost of Waiting on AI

What's the risk of doing nothing? Richard McLean explores why staying on the sidelines might be the most costly choice.

The Importance of Explainability in AI

With generative AI now routine, businesses need to weigh up when a transparent 'glass box' model is the better choice over a powerful but opaque one, especially in finance, healthcare, legal, and education where decisions carry real weight.

AI Governance Series: To Build or to Buy?

How boards should approach the build-or-buy question for AI, covering trade-offs around speed, control, talent, maintenance, and capex versus opex, plus the often non-binary middle ground of building on top of pre-trained models.

AI Governance Series: Keeping regulation and compliance concerns front of mind during AI project planning

AI Governance Series: Preparing for and Avoiding Roadblocks to Adoption

Part three of the AI Governance series. Three roadblocks that quietly kill AI projects: data preparedness, organisational culture, and change management, plus the questions a board should ask before greenlighting a project.

AI Governance Series: Consideration of key stakeholders in the board decision making process

Part two of the AI Governance series. How directors should think about the impact of an AI project on customers, employees, shareholders, the wider community, and government regulators before committing to it.

What is Explainable AI, and Why Does Your Business Need It?

An introduction to Explainable AI (XAI), covering why it matters, the difference between global and local explanations, and the methods like LIME and SHAP that make complex models legible to humans, regulators, and end users.

Does Artificial Intelligence deserve a place on the board agenda?

Part one of the AI Governance series. Why AI belongs on every board's agenda, the questions directors should be asking, and what readiness looks like for organisations considering an AI investment.

Defence against Big Tech

Three strategies for legacy businesses to compete against tech-first disruptors using AI and data: move up the value chain by productising data, automate operations to improve efficiency, and differentiate on customer experience.

When AI Goes Wrong

Like any emerging technology, AI can be vulnerable to exploitation and attacks.