The Future of Qual
Ray Poynter, 22 May 2025
Last week, I had the honour to be invited to attend the QRCA International Qualitative Conference in Berlin on behalf of ESOMAR. It was a fantastic event with leading-edge, innovative, and sometimes challenging presentations from North America, Africa, India and Europe. I also had the honour of being allowed to share my thoughts on The Future of Qual. I will share my message in this note and add a bonus insight I could not squeeze into my presentation.
Simple Research
My first message was that at some point in the future, all ‘simple’ projects will be handled directly by end clients (people like project managers, CMOs, designers, engineers, etc) via AI. This will be achieved via the next generation of the sorts of AI tools demonstrated at the conference, for example, by Abby Leafe, Corette Haf and Chris Arning. This will result in much more qual being done, in more decisions being made with the advantage of evidence, and a greater focus on customers.
This change will not be quick. The forces of inertia are enormous. I suspect that in four to five years, about 20% of qual research projects will be conducted by end clients directly via AI tools, including design, recruitment, interviewing, and analysis. By adopting AI now, qual practitioners can put themselves in a good position to navigate this change. The step-by-step approach to adopting AI shown by Laura Quinn is a great example of what people need to do.
What is Simple Research?
Simple research comprises two categories:
- When the answer is already known. We are all aware of situations where clients ask us to do projects and we tell them that we did that project for one of their colleagues just a few months ago. AI is enabling clients to access all their past research, search the web for additional information, listen to social media, and interpolate answers.
- Where how to get the answer is known. When we are asked to test 10 insurance claim letters, or evaluate three new soft-drink concepts, or evaluate a new menu, we know what the range of methods, we know who to recruit, we know what the discussion guide should look like, and we know how the analysis should run. This is being computerised, and it will be the natural provenance of AI soon.
The Future of Qual – LUUUA
The future of research will focus good qual, and is highlighted by five features, to which I have given the initials LUUUA.
Liminal
If simple research relates to things that are known, the liminal spaces are the places where things are not known. As society changes and traditional values are challenged, we need qual researchers to explore and discover. This is where empathy, innovation, and creativity, along with the lived experience of working with people, are essential. Daniel Plettenberg’s review of the impact of society (and hence of qual) of neo-liberalism was a great example of the liminal space, as was the review of Gen Z in India by Raji Bonala and Sunaina Bose.
Over time, these liminal experiences will become ‘known’ and amenable to AI, but new liminal spaces will be opening all the time. At the conference, we saw instances of these liminal spaces, for example, presentations about Gen Z in India reverting to more traditional roles, the consequences of neo-liberalism in the West, and ‘Queering’ of research.
Unwritten
AI is currently based on reading most of the world’s accessible text, mostly English. Unwritten things are not in its corpus. Qual research is needed to find and interpret these stories. One of the great things about conferences is getting time to talk to people, and I heard great examples of these unwritten contexts over lunch from Cynthia Harris (relating to hair) and Ella Fryer-Smith (marginalised groups).
Unheard
The are many groups who are outside the mainstream and who tend to be unheard. We need to actively hear these conversations, seeking them out and then learning to listen in new ways. Hannah Kaplan talking about Queering research & Peter Totman talking about some of the challenges in talking about fatherhood were good examples of this.
Unsaid
We have all heard parents say they prioritise healthy food for their children’s lunchboxes over their child’s preferences, we have all heard people say they prioritise quality over price, and we have seen how often the actions show this not to be true. At the moment AI has a disturbing habit of believing what is said in discussions. Qual researchers have bullshit detectors and methods of asking questions that help interpret what is said and understanding what is not said. For example, probing, projecting and seeking at the truths that are often unsaid. The deprivation research reported on by Eric Paice was a good example of exploring the unsaid – after all we don’t know what we like till it’s gone sometimes.
Advocacy
Data does not change somebody’s mind! If I don’t have an opinion or a position and you give me the results of some research, I am likely to believe you. But, if I already have an opinion, and what you are telling me does not fit, I will resist. Giving me more examples won’t change my mind and might even alienate me. To change my mind, you need to reach out at an emotional level to make me feel that what you are saying is right.
A qual researcher, grounded in a topic, is in a great position to advocate for their findings. We don’t just want presentations, we need advocacy, leveraging all the skills of the qualitative researcher to find ways to make the recipient feel that what they are hearing is right, and that they need to change their position and often change what they are doing. If I am a client, I am more likely to change my mind if faced with people who have experienced the truth, people like Dali Tembo from South Africa, rather than relying on dry data and reports.
The Rallying Call
So, my call to the qual researchers assembled in Berlin, and beyond is trial AI, utilise qual to focus in the unknown.
The bonus example – be more Monet, Van Gough and Frida Kahlo
I did not have time to share this thought in Berlin but I share it here.
Until the invention of photography, the main focus of art was to capture lifelike images of the real world.
Photography took off in the 19th Century and was widely established by 1900. Painters faced ruin. Their core skill was about to be automated, but something faster and more accurate.
The first response was Impressionism, in the 1870s, with painters such as Monet and Renoir. This was followed by post-Impressionists, such as Van Gough, by surrealists like Magritte and Dali, by the innovation of Picasso, and by the magical realism such as Frida Kahlo.
Most of today’s most loved 20 painters were at their peak after photography arrived. The automation of image capturing did not end painting; it freed it from the mere copying of images to interpreting the world.
This is the change we need for qual.
Sorry to all the other great speakers.
I have not mentioned everybody who spoke, but they were all great. But I will highlight one more speaker, somebody I suspect is going to be a name to watch out for over the next few years, Bryony Hancock, a new face, but against very good competition, the winner of the overall best presentation award.
And finally a big thank you to QRCA for inviting me, it was an excellent event.