A practical approach to AI in design

Article summary

AI is already part of design work now. Not in some distant future sense — in the day-to-day reality of workshops, research, synthesis, writing, prototyping, and the admin around all of it. The question is no longer whether to use it. The better question is how to use it well.

My own view is simple: AI is most useful when it supports the work, not when it tries to replace the thinking. It can help speed up tasks that are time-heavy and repetitive. It can help surface patterns faster, organise messy material, draft options, and widen exploration. But it still needs context, direction, review, and a human being who knows what they are looking at.

In practice, I use AI in a few clear ways. After workshops, it can help turn recordings and transcripts into a faster first pass of notes, themes, and open questions. In research, it can help cluster observations, compare signals across interviews, and pull together rough drafts of synthesis that I can then review properly. In product and website work, it can help generate options, stress-test wording, and make documentation lighter to produce.

That matters because a lot of good design work gets slowed down by overhead. There is often too much material, too many conversations, and too many small tasks sitting around the real work. Used properly, AI helps cut some of that drag. It creates more room for judgement, better conversations, and clearer decisions.

What it should not do is lead the work. It should not replace strategy. It should not decide what matters most in a business, a product, or a service. It should not be left to interpret emotionally nuanced research on its own. It should not make ethical calls. And it should not be treated as if fluent output is the same thing as insight.

Related links

• Related service — AI discovery workshops & advisory
• Get in touch — Talk with Prequel about practical AI in product and design work.