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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

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.

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.

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

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

You ask an AI a question (ChatGPT or other - they're all as guilty as one and other) and it replies instantly with something that sounds like it came from a well read expert who also happens to be polite, structured, and suspiciously confident. It's fluent. It's tidy. It even gives you bullet points. And then you discover one awkward detail. It is wrong.

We’ve all spent the last 18 months talking about “AI for customer service” like it’s a magic trick. Better bots. Smarter assistants. RAG for everything. And yet… handle times barely move, recontacts creep back up, agents still ask in Slack: “Which article is actually correct?” It’s not that the AI is bad. It’s that we’re asking it to reason on top of knowledge that’s fundamentally broken. Messy, contradictory, duplicated, outdated, scattered across Confluence, SharePoint, Se

"Ontology is lining up to be the buzzword of 2026" I'm hearing that everywhere now, but really? Palantir’s rise has put ontological modelling back in the spotlight – their Foundry platform is built on it. Microsoft is now moving ontology into Fabric. The race is on. It makes sense. Ontologies give generative AI something it desperately needs: grounding . LLMs are brilliant at pattern-matching and language, but terrible at enforcing logic. They’ll happily smooth over contradi