AI’s Journey in Trendspotting

Generative AI isn’t great at trend forecasting. It’s not even very good at identifying cultural moments that are currently or have recently been trending. At least that’s what I concluded when I first started dabbling in AI tools about a year ago. But that’s starting to change.  

As the manager of Weber Shandwick’s Cultural Insiders — a community of trend spotters who help clients break their echo chamber, add value for audiences, and inform cultural understanding of specific communities — I was eager to see how AI could add value to our work. But when testing different tools, I ran into some pretty major barriers.  

The first issue lies in the data cut-off of most LLMs. Chat GPT for example famously only includes information as recent as 2021, which might as well be 2001 in the highly time-sensitive world of culture spotting. In order for AI to be useful in the context of urgent client needs and predicting shifting cultural trends, data would need to be nearly real-time.   

The second obvious problem lies in the fact that culture spotting is a highly nuanced practice. Contextualizing relevant insights requires deep expertise from the communities who are shaping culture directly, a principle core to the creation of the Cultural Insider community. Because most AI is trained on a very wide range of data, it lacks the specialization required to understand the complex nuances of various communities.  

Of course, innovators have recognized both of these problems and are actively building for solves. Recent developments from tools like NextAtlas Generate are starting to fill the gap. Crucially, the platform specializes in forecasted trends, which is evident in relevance to the type of responses it creates when compared to general tools. The product can also generate specific cases and visualization tools for data interpretation, something you simply won’t find in other AI tools.  

For example, when developing dig sites for a major pet insurance provider looking to reach more cat owners, we turned to Generate to uncover relevant trends. The platform was able to identify trending language, memes, and products relevant to cat owners, as well as provide a predictive graph. From there, Cultural Insiders passionate about cats can further ideate and make connections based on these sparks.  

Humans are still crucial for the highly nuanced practice of trendspotting. But AI may serve an increasingly helpful role as it becomes more timely, specialized, and capable of the nuance. We’ve already seen incredible developments from where the technology was just a year ago.  

– Julia Dixon

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