“What LLM should we standardise on?” It’s one of the first questions organisations ask as they begin adopting AI; it’s also, I think, becoming the wrong question. Over the past few years, we’ve become accustomed to comparing models through benchmarks, leaderboards and feature announcements. Every few months there’s another “best” model. Better reasoning. Larger context
A few months ago, I wrote about applying the Sniff Test to AI-generated content. The premise was simple. As Generative AI becomes increasingly capable, we should be diligent in maintaining a degree of healthy scepticism: The article resonated because most experienced professionals have encountered the same phenomenon. AI can produce highly persuasive answers, confidently presented
Applying a “Sniff Test” or carrying out a “Sanity Check”, has always been an important part of any analysis or calculation. It should be your first check before diving deeper, and especially before sharing results with others. I am not saying it was easy or foolproof, or even always possible; however, the onus was on
A few months ago, I found myself wondering whether the technology industry is genuinely changing faster than it used to, or whether I am simply paying more attention. Rather than relying on memory, I decided to look at the data. Using Google Trends, I examined worldwide search interest between 2010 and 2025 across a range
Business that operate on a Membership Business model have different objects compared to say a Distributor or Retailer Model. This post is about how analytics supports this type of business
Many data platforms fail for the same reason. They were designed to answer yesterday’s questions. Business priorities change. Regulations evolve. Organisations acquire new products and services. Customer expectations shift. More recently, Generative AI has introduced entirely new ways of consuming and analysing information. Yet many organisations continue to build data platforms that assume stability. The
