AI Solutions Lab: AI fluency meets the Thundercorn - Jersey Evening Post

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AI Solutions Lab: AI fluency meets the Thundercorn

Sponsored by Prosperity 24/7 12 August 202611 August 2026

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Alex Honey, senior consultant at Prosperity 24/7

Alex Honey, senior consultant at Prosperity 24/7, explains what her son, a rainbow unicorn and 40 attempts taught her about AI fluency

MY son and I spent an afternoon creating Thundercorn, an armour-plated unicorn with a lightning-bolt belly and a rainbow mane longer than its body.

He drew it by hand, asked an AI image generator to turn it into a storyboard, then changed the plot about 40 times. The AI kept missing what he meant. Each image would come back wrong, he would shake his head, explain again and try once more.

As part of my role with Prosperity 24/7, I support organisations to adopt AI safely and effectively. Working with tools like Gemini, Copilot or Claude is not like using traditional software, and it is not really like search either. Search helps you find an answer. AI is more like directing an eager assistant who can move quickly, misunderstand you, invent things and quietly wander off.

As I watched my son chase a rainbow unicorn through 40 drafts, I realised he was already doing the thing we are teaching adults: handing over the right work, explaining what he wanted, judging what came back and owning the result. Maybe AI fluency is not something we need to learn from scratch. Maybe it is something we need to remember.

Before he touched the AI image generator, we had talked about doing the whole thing by hand. Sometimes that’s the right answer. Drawing it is fun and handing it over gives away the best part. This time he said that the story was what mattered, so we let the machine do the drawing and kept the storytelling. That decision is delegation: working out which part of the task should go to the AI. He’d arrived at the rule on his own. Give away the labour, never the joy.

Having chosen to direct rather than draw, he ran into description, which is harder than it looks. The AI could not see the picture in his head. It wandered, invented and quietly changed things. To close the gap, he had to say exactly what he meant, notice exactly how the result had drifted, and try again. Pictures make this easy to practise: the wrong-coloured mane, the extra leg, the cloud that should not be there. You can see the distance between what you wanted and what you got.

Then came the bit adults often skip. One image looked finished: clean, confident, professional. I was impressed. He was not. The armour was wrong and a cloud had crept in from nowhere. That is the trap with AI. It can sound, look or read as if it knows what it is doing, even when it has missed the point. Discernment is the critical eye that refuses to be persuaded by polish alone.

When it was done, I asked whose work it was. He took all the credit. The AI was just a tool, like a pencil. That is diligence: owning the output. In the office, it is tempting to hand the AI the work and the responsibility together. But the responsibility never moves. Every robot needs an owner.

None of this was taught. He simply would not accept a version that was not the one in his head, and that refusal was the skill. Somewhere between school and the office, many of us trade that instinct for something that looks like efficiency. We accept the first draft because it arrives looking finished.

Thundercorn is on the fridge. It took 40 goes, but the 40 goes were not the point. The machine would have made 400 and been content with all of them. My son was the only one in the room who knew what right looked like, and that is the part AI cannot do.

The AI Fluency Framework was developed by Rick Dakan and Joseph Feller, and published by Anthropic. It describes fluency as four connected habits: delegation, description, discernment and diligence.

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