FlatGPT – why AI is in danger of taking the heart out of research.

It is obvious Ai is transforming research at speed. That doesn’t take a genius to realise.

My outputs are streamlined, agile and efficient, and I now have unprecedented access to large-scale trend data and rapid feedback. I can even scrape the whole, entire internet (thanks, Claude!). But in the shift towards pace and efficiency, we’re losing something fundamental to what I adore about my job, and the work I take so much pride in. I’m talking about depth, nuance and understanding.

In the worlds of employer branding and education it’s never just ‘do you like this logo?” or ‘would you apply here?’ Instead, our questions are so much more layered. What makes a university feel credible, aspirational or inclusive? How do culture, identity and values show up in someone’s decision to join (or not join) a business? What unspoken beliefs and assumptions are being teased out in these discussions?

This work is rooted in nuance! It is complex and often vague. At the end of it all, it’s fundamentally human. And when you are dealing with culture, identity and ideology, human responses will never neatly fit into just one theme. People are contradictory. What they say doesn’t always reflect what they believe, and some opinions are hard to articulate. That key trend identified by ChatGPT may well be an amazing surface-level finding. But by flattening things out, we flatten meaning too, and this is where the juicy stuff can be found.

This is why I feel qualitative research should be the backbone of any project I do. It moves beyond that surface. It observes behaviours in context, and uncovers tensions and contradictions. It’s also where the ‘quality’ of our research starts to change.

AI and quantitative methodologies are mostly about scale, sample size and statistical confidence – all of which are important. But this isn’t enough.

As data becomes faster and cheaper, quality is no longer defined by how much information you have, but by how well you understand it.

For me, quality comes from depth of understanding, context and interpretation. It is about moving beyond metrics to explain meaning, because this leads to deeper understanding and better strategy for my clients.

If we continue to move towards synthetic data and the need for speed, the implications for organisations looking to conduct research are significant. Investment in deep, qualitative work will become a must, not as a supplementary layer to ‘illustrate the quant’, but as a core part of how understanding is built.

Quant and qual show their value when they inform each other. They give you faster answers, along with a proper understanding of the meaning behind them. In the employer branding and education marketing sectors, where identity and belief systems are so powerful, there’s a real competitive advantage to be gained. We just need to slow down a bit and let those all-important nuances come through.

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