Resources

AI is widely discussed, but rarely explained in a way that reflects how SMEs actually operate. This article breaks AI into practical categories and argues that the real challenge is not access, but applying the right approach to real operational problems.

AI is often framed as a technical capability, with discussions about skills centred on data scientists and machine learning engineers. This article explores why successful AI adoption depends on a much broader set of organisational capabilities, from data foundations and governance to domain expertise and change management.

In the UK, over 99% of businesses are SMEs, yet AI is still framed as an enterprise transformation agenda. This article challenges that narrative, arguing that for smaller organisations AI is not a board-level spectacle but a practical lever for productivity, margin protection and operational scale. The real risk is not lack of access, but unstructured adoption without commercial intent.

In conversations about software reliability, availability targets are often expressed with reassuring simplicity: “We’re aiming for five nines.” Yet behind that short phrase lies one of the most complex, expensive, and nuanced challenges in software engineering.

In many boardrooms, conversations about regulatory compliance and software sound deceptively simple: “We need to comply with GDPR.”, “Our system must support ISO 27001.”, “We have to meet FCA reporting requirements.”.

Building software isn’t just about writing code, it’s about delivering something that works reliably, scales effectively, and provides a great experience for users. Too often, testing is treated as a final checkbox before launch.