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AI adoption in business: real examples, risks and lessons from UK leaders
AI: The good, the bad, and the ugly
Over the last six months, I have had the privilege of sitting down - virtually and in person - with leaders from across UK industries: energy, utilities, construction, law, manufacturing, media, education, and beyond. Not to sell anything. Not to run a workshop. Just to have an honest conversation about where they are with AI adoption and implementation.
What I heard was revealing. Not because everyone was doing extraordinary things with AI (some were, some weren't), but because the honesty in those conversations cut through the noise that dominates most AI discourse
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Why AI projects need continuous evolution, not one-off implementation
One of the biggest misconceptions about AI development is that once you've built something, you're done.
In traditional software projects, there is often a clear distinction between building a feature and maintaining it - AI doesn't really work like that. The pace of change is so fast that maintaining and evolving AI systems is increasingly becoming just as important as building them in the first place.
I was reminded of this recently while working on a realtime AI application that relies on speech-to-speech conversations.
The application uses AI to hold natural conversations with users. Rather than typing prompts and waiting
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