AI's democratisation of qual - the risks and rewards researchers need to know

I’ve been working in qual technology for 15 years now and this year has been the most exciting by far. AI has unleashed incredible new opportunities for qual research to be used by, and to positively impact, many more people around the world. But there are also numerous risks in this change to consider, both for researchers and the industry as whole.

Tom Woodnutt is one of the leading practitioners of online qual and someone deeply immersed in the adoption of AI in the industry. He has spent much of the last year using different generative AI qual tools, in experiments and on live projects for his award winning consultancy Feeling Mutual. He also speaks with other qual researchers, has presented on the topic at various industry conferences and writes regularly about it.

Today I am going to be asking him the questions about AI on every qual researcher’s mind. But before we get into these many hot topics, first, Tom could you please explain your interest in the topic and your motivation for this video series on AI’s impact on qual research?

AI's democratisation of qual - video introduction

Qual is entering a new age

I’ve done qual for over 22 years, and specialise in online and mobile methods. We’ve just entered an exciting new age for the practice as AI technology offers tools that can do qualitative research tasks increasingly effectively - not necessarily as well as expert humans can but certainly as good as - if not better than - novice or untrained researchers.

The AI toolkit for qual can already save time and effort and help manage larger scale work with less investment. However, as with every decision you make in qual, there is always a trade-off. We may gain AI efficiencies but then we lose some nuance, detail, proximity to the participants and tailoring in our design, moderation and recommendations. So I think it’s important to be aware of the trade offs when using and selling AI powered methods.

Democratisation - friend or foe?

Overtime I believe this wave of AI powered tools for qual will inevitably democratise access to qual skills - just as technology has disrupted other industries by making expert skills more accessible, whether that be web-design, holiday booking, video production or whatever.

While this may be a big opportunity for people who want to do qual but didn’t necessarily have the budget or expertise to do so before - it can also feel like a threat to those who work in the industry and whose expertise can feel devalued by the rise of the machines.

I think we need to acknowledge this tension and be honest and open about the benefits and limitations that AI and Humans bring to qualitative research (both independently and when working in tandem) so that we neither over nor underestimate the potential of AI-powered qual. This is in the interests of all parties from clients, current qual practitioners to tech providers and new entrants to the industry - ensuring everyone can maximise the rewards while minimising the risks.

There’s too much overclaim (on both sides of the pro and anti AI debate) and I think that balanced reflection will help us plot a path through it all.

Sharing what I have learned about AI

The intention in this video series is to share some of what I’ve learned using, reviewing and studying the application of AI to qual. I make some predictions for the future with a view to stimulating constructive conversation and helping us navigate through it all.

I’m not an AI evangelist blinded by shiny technology, equally I’m not closed to the potential it presents. In fact I feel somewhat conflicted towards AI myself. On the one hand I’m drawn to new ways to use technology to do better qual research. Way back in 2007 I collaborated with Liveminds to experiment with mobile video back when it took hours to upload a single clip! At the same time, I also prize old-school qual research values of rigour, validity and quality - having learned the craft over 20 years ago.

So when Generative AI and chat GPT burst on the scene promising short cuts, I felt a conflict between a focus on rigour and an appetite for innovation. I don’t think I’m alone in having this reaction - many researchers today feel excited at AI’s potential but also threatened at the idea of it doing bad job or even replacing us.

Giving you a balanced view

AI is able to mechanise knowledge and address inefficiencies in qual, but what if human researchers are the inefficiency it automates? This tension sits at the heart of our evolving relationship with AI. The relationship can feel somewhat Paradoxical - as the more researchers use AI qual tools, the better they get. And the better they get, the more likely it looks like they may replace us. We could call this the Uber paradox as Uber drivers benefit from the technology in the short term, despite the ultimate long-term stated goals of their CEO being to replace them with autonomous AI drivers.

This raises the question of whether by using AI for qual and helping it get better, qual researchers are sleepwalking into a future of self-driving autonomous robot researchers? But for me that’s not the question we should be asking.

Hearing your thoughts

Rather than seeing it as AI VS humans, I’m more interested in how businesses in general and qual researchers in particular can thrive because of AI tools. So in this series I’ll be sharing a balanced view on the risks, rewards, benefits and limitations in AI tools. I’d love to hear your thoughts from all sides of the debate so please feel free to share them!