Insights, articles and news

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.

Designing great software is not just about how it looks. It is about how it works for the people who use it. Regardless of the demographic of the user base it's important to create an experience that feels intuitive, accessible, and empowering from the very first click.

In software development, one of the most common traps organisations fall into is trying to build everything at once. The result is often a long, expensive project that takes months (or years) to deliver, and when it finally arrives, it’s bloated with features nobody actually uses.

Data is now central to how organisations operate, compete, and grow. But before you can turn that data into insight, you need the right foundation.

Data is now woven into the fabric of how organisations operate. It holds the answers to better decisions, smarter products, and new opportunities, but only if it’s organised in a way that makes those answers accessible.