Automating qualitative research: Exploring the future role of AI
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We often talk about what AI is capable of in the here and now. Since ChatGPT’s launch in November 2022, the qualitative research community is becoming increasingly familiar with and open to utilizing AI technology in research. Of course, we have been evangelists of more complex Emotive AI such as voice, facial recognition, and next-level sentiment analysis since Jade Kite’s creation 4 years ago. In these 4 years we have tested and adapted multiple tools and refined our methodologies to optimize the use of this technology in order to return a quality of insight that give teams the confidence they need to move forwards at pace.

Today we are going to share with you the advances that we are most excited to see in the next 1-2 years. Warning, some of them may sound super sci-fi, but we promise in a few years this technology will seem normal to us all.


Autonomous Agents

While virtual assistants were some of the first AI applications to hit the mainstream, their limitation lies in their ability to identify and carry out individual tasks. As the next evolution of this phenomenon, autonomous agents will be able to research how to do complex tasks, break them down in sub-tasks, research these sub-tasks, and then implement the whole process. The possibilities are endless, but for qualitative researchers it could practically mean end-to-end project management support, including finding vendors, setting up research, filing, storage management, and more.


Blurring of the Qual/ Quant divide

While there are still significant leaps to be made in Qualitative AI, machine learning is coming closer and closer to replicating quant analysis in a flash. Chat GPT alone has shown it is capable of using python coding and statistical analysis plugins to look for interesting and unexpected patterns in data. Not only is it capable of returning a list of potential avenues of exploration, but it can also write full analysis reports based on points of exploration the user deems fit. For qualitative researchers, this has the potential to provide the skills needed for multi-method research at pace, without the need for quant expertise.

On the qual side, Emotive AI tools are enabling robust statistics to be attached to qual insights, demonstrating exactly what percentage of respondents demonstrated specific emotions in the context of relevant topics.

This greater accessibility to quant data and quantification of qual subjects will inevitably empower researchers to lean on mixed method research easily, leading to a blend of quant and qual across the majority of challenges.


Deep contextual briefings

Secondary research sets the tone for all research projects; it is where hypothesis are born and key questions derive from. Typically, this can take days or weeks of an associates time, however advances in AI in a range of fields such as creating key summaries, analyzing vast amounts of data and theme tagging will soon enable AI to create detailed contextual summaries. While some of these applications are still in their infancy and the ability to combine these tasks has yet to emerge, the technology in these areas is developing rapidly, and we expect to see this capability sooner rather than later.


As self-proclaimed AI nerds, we see it as our role to continue keeping one eye on the future, as well as informing our fellow researchers on emotive AI in the here and now.  We are constantly keeping tabs on the advances that will undoubtedly continue to change the face of research. If you want to understand how to stay ahead of AI technology with your insights methodologies, book a call with Sidi today.




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