AI became a much better listener in 2017, when a breakthrough paper introduced the transformer model—the foundation for tools like GPT. For the first time, models could focus on meaning and context, not just word order. That shift made today’s language tools possible, but it also means we need to stay sharp. Because while AI is getting better at understanding us, it’s up to us to keep thinking clearly.
Every organisation has policies - good and bad - but how do you avoid them gathering dust, and what will make your AI policy one of the good ones?
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Three terms cropped up repeatedly this year: knowledge graphs, metadata, and AI governance. This post is one of our occasional explainers, designed to save you a heap of further reading, and to help you understand why this next wave matters now, not later.
A reflection on two years of prompting, progress, and curiosity.
Every time we introduce a new bit of AI kit someone asks: “How will we know if it’s working?” It’s usually shorthand for: “Will this make us faster/better/cheaper?”
How to think about the price tag, the procurement pitfalls, and the value behind the hype.
Continuing our occasional series of blogs where we apply our favourite retro management theories to AI, it’s time to tip our hats to Everett Rogers’ Diffusion of Innovations model, aka the change adoption bell curve.
We've been thinking a bit more about our fantasy Early Adopter Club™ and why we think the membership includes you (and us). Here's a little update on how tech adoption tends to involve making a leap across the chasm between innovators and the majority.
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