This may be a controversial opinion: I’ve started to wonder if, with the explosion of AI, the research sector has turned in on itself too much. 

The conclusion I’ve reached is not that we shouldn’t debate issues like the value of ‘qual at scale’ (and what can be defined as this) or whether synthetic data is as good (or better, or worse) than survey data. Or even how well an AI moderator can really probe.

But I think there has been too much oxygen consumed at this point on definitions and comparisons; we’ve become more preoccupied than ever with methods; and with this we can lack attention towards the bigger issues and opportunities.

This has never been a sector weighed down by encoded definitions and fixed boundaries. Much as we wish to be – and should be – seen as professionals with a valuable skill set, there’s no prescriptive pathway into research or set credentials required. Sure, there are qualifications and standards, but rigidity and rules-based order have never been our way – we’ve always been too creative, open-minded, outward-looking.

When we debate ‘qual at scale’ or ‘synthetic vs real’, or ‘is AI moderation as good as a human?’ I see a defensive crouch. It’s fully understandable – what lies just beneath the surface is anxiety about redundancy. Redundancy of jobs, of skills. It’s a bitter pill to swallow if you’ve spent years honing your moderation expertise and you know you’re good at it, and now everyone seems to want AI to do it instead. As a sector, many of us care about the craft, and no-one wants to feel that their wisdom and capabilities no longer carry the same value.

We absolutely need to be clear on what AI can and can’t do, we do need to promote the value of the human in the loop, and we need to be certain about any data integrity issues we may or may not be opening ourselves up to.

It’s simply that we’ve become rather mired – at least this is my personal sense – in some of these debates, and we’ve got fixated on discussing methods. But methods are always a means to an end. What is our end?

Around us, the world doesn’t care so much. While we’re busy fretting over what you really get when you commission ‘qual at scale’, there are wars going on, people living in poverty, people feeling disenfranchised in society and anxious about the future.

Closer to home, there are client organisations and stakeholders who also don’t care so much. Many businesses have a mandate for AI adoption, there’s a general acceleration of timelines, and they might accept something 80% as good if it costs 20% of the price. And why shouldn’t they, most times? In a landscape of economic turmoil, and pressured margins for many industries and brands, 80% good is often way better than nothing, or 100% but at triple the price.

At many sector conferences, there’s so much emphasis on showcasing AI methodologies that – dare I say – it’s become boring. It’s not that we’re not talking about business issues, opportunities or impact, but the centre of gravity in the discourse is methodological. 

We can’t stop the AI train. We can choose to resist, adapt or embrace. Whatever our personal stance, at this point it feels like we need to make sure we’re lifting ourselves above method.

If we are to prove the value we bring as a sector – one which is metamorphosing with the drive towards in-housing client-side and the explosion of AI-based platforms – we need to expand our reflection and debate. 

If we wish to sustain relevance, we need to show impact. 

If we seek to be interesting, we must have a point of view.

If we want to avoid being frozen out, we have to earn our right to be at the table.

Above all, the thing I feel I’m missing is the thinking part. Personally, I want more brain food. I want to see how the smart individuals in this sector can come up with insights into culture, brands and people that reach outside what an AI tool can do. If we want to be the experts in insight, don’t we need to show how we’re insightful?

I also want to see how these same smart individuals can then deliver real business value – substantially above and beyond what anyone can do chatting in Claude or CoPilot and running a few self-serve interviews. 

The sector will continue to morph, and we will all have to choose how and where we wish to adapt, and what we want to hold on to, what strengths we promote.

To be frank, the sector may shrink, or at least some jobs at least may look very different in the future – that’s the other thing we’re missing in our emphasis on methods because all of that is in the ‘here and now’; honest dialogue around how we prepare ourselves for the future, whatever that looks like.

We need to start to look more outwards, and forwards; to look towards ideas, and impact.