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Monday, July 15, 2024

How to use AI-based tools as a designer effectively from the beginning

Utilizing Jakob Nielsen’s astounding forecast that AI will revolutionize user experience

I learnt this week that Jakob Nielsen, the dean of Web Page Usability and co-founder of the Nielsen Norman company, had made a statement regarding the future of user experience and artificial intelligence.

I anticipated that many more significant enterprises would have an opinion now that ChatGPT has been operational for almost a year. I didn’t expect him to be so encouraging, though; he forecast a shortage of UX professionals with two years of expertise in AI usability in 2025, with each individual receiving 500 job offers.

At the same time, there is a lot of buzz about the development of AI-based learning tools. There are extremely few resources available for non-technical folks, hidden between get-rich-quick schemes and scholarly theses on AI. Many Junior Designers might not know how to acquire this expertise as a result.

My experience with AI and data-informed design, however, suggests that the solution is simpler than you might expect. You must first adopt a new mental model centered on the four words “Garbage In, Garbage Out” in order to learn AI-driven tools.

Having the right mental model for AI is crucial

I’ve worked in industries where data is king for the majority of my UX career. I’ve had to modify my design methodologies to incorporate analytics, metrics, and even AI-informed insights into my process, from healthcare and federal UX to B2B/SaaS applications.

This is why I’ll tell you that the mental model that designers should adopt for AI and data should be their first point of focus. To help you with that, I’ll provide you the four principles that, for almost 80 years, have guided computer science (and related fields): Waste Not, Want Not.

In other words, if the input, data, or AI prompt quality is poor, your output will be inferior as well.

This expression originally caught my attention when I began studying data visualization, where it was absolutely essential. In the end, it didn’t matter if I created the best data visualization in the world; if it was built on shoddy data, it was a shoddy visualization.

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