We Built a Faster Factory. Now What Are We Producing?

When looking for a common thread in the GRIT report’s Hottest Methods, it's easy to think everyone is optimizing.  And that we all need to get on the faster, cheaper, leaner express train. Synthetic data (up) is fast and cheap. DIY tools like online communities (up) are fast and lean. 

But look closer and you will find a contradiction that’s hard to miss: if everyone is optimizing for things like speed and cost, then why are ethnographies still up across half of the segments? Why is crowd sourcing, an inherently longer and more strategic human-led method, on the rise?

It shows an industry trend of wanting to have our cake and eat it too. We want faster, cheaper, leaner. But perhaps, at the same time, we are all secretly holding on to our “traditionally” longer, deeper, human methods, for the fear that we are optimizing at the expense of good insights.

For the past few years, the research industry has been focused on optimizing the “factory.” Think of it like a modern car manufacturer: the factory is now AI-enabled, faster, and more agile than ever. But despite all the upgrades to the production line, it’s still producing the equivalent of a Model T… or in our case, largely the same basic insights. 

And that tension may be exactly why the industry can’t fully let go of its more human-centered methods. We want the efficiency of the modern factory, but we also want the breakthrough insight. We want automation without losing intuition. Scale without losing nuance. 

The GRIT Report data clearly shows we are all grappling with this. For every synthetic panel being adopted, another mobile ethnography is being launched. For every AI workflow being introduced, there’s renewed investment in methods built to uncover deeper human understanding.

It's a crucial time for our industry to acknowledge that we need to refocus not just on optimizing the factory, but on optimizing the output itself. We must ask ourselves: How do we take the everyday, model-T insights and turn them into the Rivian of insights?  Are we using all of our methods to the best of our abilities? Are we catering the right tools to the right audience? Are we asking the right questions? 

Take this as an early signal: Service-led suppliers with 500+ FTEs reported a +17% increase in introducing neuroscience into their offerings. It could be that they are looking for a way to do the best of both.

The data is talking. It's time to focus on the quality of the materials that build the car, not just the tools in the factory itself.