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Mapping the landscape of gen-AI product user experience (Interconnected)

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One question is always: how should users use this? Or rather, how could they use this, because the best design patterns haven’t been invented yet? And what we want to do is to look at prior art. We can’t look at existing users because it’s a new product. So what UX challenges can we expect and how have others approached them? The problem is that there are so many AI products. Everything overlaps and it’s all so noisy – which makes it hard to have a conversation about what kind of product you want to build. So I’ve been working on mapping the landscape. As a workshop tool, really. You’ll recognise the map if you saw me speak at Future Frontend in Helsinki or UX London. I’ve also been testing this landscape recently with clients. It’s a work in progress, but I think ready to share. Let me show you… To start, let’s look at the first generation of AI products that came out right after large language models got good enough (i.e. GPT-3) with a public API and sufficient market interest. So we

Mapping the landscape of gen-AI product user experience 16.32, Friday 19 Jul 2024 Link to this post I talk with a lot of clients and startups about their new generative AI-powered products. One question is always: how should users use this? Or rather, how could they use this, because the best design patterns haven’t been invented yet? And what we want to do is to look at prior art. We can’t look at existing users because it’s a new product. So what UX challenges can we expect and how have others approached them? The problem is that there are so many AI products. Everything overlaps and it’s al

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