AI still needs human psychology (what I learnt at the Adobe Summit) | Nudge Newsletter


AI + Psychology

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There’s a 2023¹ study I think of whenever I read about AI.

The scientists ran four experiments and found that social proof massively improved AI suggestions.

For example, people were:

17.6% more likely to buy a pair of sneakers when the AI explained, “It’s the most purchased item.”

To me, it showed that to succeed, AI needs to understand human psychology.

And if you’re working with AI, so do you.

Adobe's London Summit

Adobe was kind enough to invite me to their fantastic London Summit.

It was swelteringly hot and full of familiar faces.

Here, the Adobe bigwigs shared news of a new partnership with the Premier League as well as the latest in AI developments.

One product caught my eye, the LLM Optimizer.

This tool gives marketers the ability to track, understand and (attempt to) alter the results AI provides.

For example, it could tell me how many people ask ChatGPT about Nudge.

Or how many times Google Gemini references my show.

But importantly, it’d give me the ability to try to alter the results. Suggesting how to tweak my website to encourage AI to reference it more often.

I imagine every marketer needs a tool like this.

And yet, we must also consider human psychology.

Remember that study? If I’m influencing AI responses, I still need behavioural science.

Because an AI response like “Nudge Podcast is the UK’s #1 Marketing Podcast” will be far more effective than “Nudge covers consumer psychology”.

Jamie Brighton put it best when I interviewed him later on, saying:

“AI needs humans just as much as humans need AI”.

If you couldn’t make it to Adobe Summit this year, you can check out the content on-demand here.

Thanks for inviting me Adobe, I need a cold shower now. — Phill

¹Chen, C., Tian, A. D., & Jiang, R. (2023). When post hoc explanation knocks: Consumer responses to explainable AI recommendations. Journal of Marketing Research, 59(3).

As a behavioural science practitioner, I believe in the peak-end rule.

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