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But It Won’t Look Like the Last One. You may have noticed that artificial intelligence has developed its own weather system. Every week brings another forecast. There is an AI storm brewing. A tidal wave is coming. The temperature is rising. The robots are at the gates. Occasionally, someone predicts the end of civilisation before lunch on Thursday. But there is another piece of AI weather terminology worth knowing: "the AI winter". An AI winter is what happens when the excit

Imagine we're sitting in a pub and I tell you I've built a machine that can predict what you'll order next. It hasn't met you before. It doesn't know whether you prefer beer, wine or something involving an umbrella and a slice of pineapple. But I've shown it thousands of previous orders from people who arrived at a similar time, sat in a similar part of the pub, came with a similar group and spent a similar amount. The machine looks at all that data and says, with 82 per cent

Let me put it this way... Imagine we’re sitting in a pub, two drinks in, and someone at the next table announces that artificial general intelligence is going to solve climate change, cure cancer, eliminate poverty and usher in an age of abundance. Five minutes later, someone else says it will destroy humanity, enslave us all and turn the planet into a server farm. Different prophecies. Same religion. That's the odd thing about the conversation surrounding AGI. It's started t

AI Is Not the Problem. AI Readiness Is. Across every industry, Boards are investing heavily in AI to improve productivity, performance, customer experience, operational efficiency, and shareholder returns. AI has become a Board-level mandate. Organisations are expected to embrace it, competitors are already deploying it, and investors increasingly expect to see measurable AI-driven advantage. Yet despite the enthusiasm, many AI initiatives fail to meet expectations. The reaso

I was talking to someone the other day about AI, as you do now, because apparently we’ve all become amateur futurists whether we asked for it or not. And he said something that sounded sensible enough: “Surely the winners are just going to be the companies with the best AI models?” Which is one of those statements that feels right for about eight seconds, until you actually think about how businesses work. Because the more I look at where AI is heading, the more obvious it be

There’s a slightly awkward truth sitting underneath the AI boom. Everyone is using it. Everyone is excited by it. Everyone is producing more with it. But a lot of it is still loss-making. Not because AI is useless. It clearly isn’t. Used properly, it can be extraordinary. But at the moment, a lot of AI is being used like a very expensive photocopier attached to a firehose. People are generating more emails, more documents, more meeting summaries, more reports, more content, m

The attention economy is the idea that human attention is now one of the most valuable resources in the world. Not money. Not data. Not even time, although time is obviously part of it. Attention. Because if a company, platform, brand, employer or app can get your attention, it has a chance to influence what you buy, what you believe, how you work, who you listen to and what you ignore. It started fairly innocently. Newspapers wanted your attention. Then radio. Then televisio

There’s a funny thing happening with AI at the moment. A year or two ago, most of the conversation was still about what the technology could do. Could it write? Could it summarise? Could it answer questions? Could it help a contact centre agent? Could it generate code, create images, analyse documents, or sit inside a workflow? Now that conversation already feels a bit dated. Across the latest reports from KPMG, Deloitte, McKinsey, Accenture, Microsoft, BCG, PwC, Stanford HAI

I was at a conference recently where people were talking about taxonomy, ontology, metadata, semantics and all the other words that can make someone’s eyes glaze over halfway through a sentence. And yet the funny thing is, the core issue is actually dead simple. Most organisations do not really have an AI problem. They have a knowledge problem. AI just happens to be the thing exposing it. That is what I kept coming back to. Everyone wants AI to give smart, reliable, explainab

AI governance often fails in the same places: weak controls, poor knowledge, unclear ownership, and limited oversight. Here’s where AI systems break, and how to prevent it.

GenAI is massively increasing the volume of content businesses produce: policies, procedures, work instructions, customer guidance, internal comms, “quick drafts” that quietly become “official.” That’s the part everyone celebrates. The part they miss is what happens next: the more content you create, the harder it becomes to know what’s correct, current, approved, and safe.

AI turns every organisation into a content factory, and most organisations were already struggling to keep their knowledge bases accurate before this wave hit. Now we are pouring petrol on it.

Everyone’s obsessing over the same question right now: “How do we get more people in the business using AI?” Because on paper it’s brilliant. It can bash out first drafts, summarise a mountain of info, untangle code, and generally take the boring weight off people’s shoulders so they can do the higher-value stuff.

Most organisations are treating “AI governance” like it’s a document-writing exercise. Write a policy. Form a committee. Add a disclaimer to the chatbot. Maybe run a training session. Job done. Except it isn’t. If you’re deploying AI in regulated or operational environments, the uncomfortable reality is this: compliance is going to be audited like finance . Not “do you have a policy?” but “show me the evidence.” And in practice, that evidence lives (or dies) inside your knowl

index is not “just an AI layer on top of your docs” If you’ve taken some time to look at what we do and thought, “Hang on… can’t we just add a copilot / RAG / search upgrade and call it done?”, you’re forgiven, because we've heard this a few times now - hence the blog post!

AI rarely fails because the model is “dumb”, it fails because the knowledge it’s pulling from is messy. If you’ve ever had an AI answer at work that sounded confident but made you think “hang on… is that actually true?”, you’ve hit the real issue: shaky inputs create shaky outputs. In this short video I explain why, in 2025/2026, enterprise AI is only as trustworthy as the knowledge base behind it, and why duplicates, contradictions and outdated docs quietly turn into errors,

AI Slop Is Everywhere. The Fix Is Not “Better AI”, It’s Better Provenance. You know what’s changed in the last year or two? It’s not just that AI can make content, it’s that it can make plausible content at industrial scale, and the incentives online reward whatever gets clicks and shares. That might be harmless entertainment, but it becomes a serious issue the moment it influences business decisions, customer conversations, internal comms, or even what teams treat as “true”

Here’s the reality in 2025/2026: there isn’t one universal “where does AI gets its facts from?” because most frontier labs don’t fully disclose their training mixes anymore. But we can anchor this in what’s publicly documented and what regulators/researchers keep pointing at. You know what people get wrong about AI? They think it “looks things up” like a clever librarian. Most of the time it doesn’t. Most of the time it’s answering from whatever got baked into it during trai

I recently saw a LinkedIn post that used an image as its eye-catcher to underline the article’s main point: a switch from hype to disciplined work creating real value. The switch depicted in the image, however, was physically incapable of making that change . The labels for what it could be flipped to were on the left and right, while the type of switch shown could only be flipped up and down. Do we switch it up, down, left, right? Does it always turn the writing to green and

If you’ve been following the AI boom, you’ve probably heard the term Vector Database tossed around. It sounds like something straight out of a physics textbook, but it’s actually the "secret sauce" making modern AI - like ChatGPT or advanced recommendation systems - work so efficiently. But what are they, and why should you care? Let’s break it down without the jargon. What is a Vector Database, anyway? Traditional databases (the ones we’ve used for decades) store informatio