AI (or Artificial Intelligence) is an increasingly prevalent topic. I don't think it can't completely replace a researcher and how we think and function (thankfully or we'd all be searching for new jobs!) because it's becoming more popular within the market research industry; and on top of that, I don't think it should.
Companies produce more data than ever before these days, so don't get me wrong, anything that makes our lives simpler is fantastic-and as Helene Protopapas mentioned, it is beneficial to us all that AI can help speed up science.
There are several reasons, however, why I firmly believe that AI is not the 'produce insight' button that we have all secretly needed and that you do need a researcher to provide the insight that customers want and anticipate.
Questioning the Data
As Harmony Crawford (http://www.theguardian.com/media-network/2016/may/06/data-dashboards-insight-customers-marketing) points out, if you just send a bunch of numbers to someone, they're going to drown in them. Although they're going to have the details, they're not going to have insight. You need a human brain to query the data in order to get the above.
A researcher may ask certain questions that AI instruments may not; they may dig deeper into the results of the survey, ask certain related questions to extract the 'why' from the 'what'. They will provide you with real insight. To be able to fully understand the story, the researcher will assess will cross-tabs to run, the filters they need to add.
For instance, instead of taking it at face value, a researcher will see the NPS score; they will look to analyse the variables that have led to it, construct the history of the past and determine how the organization can make improvements forward.
You could measure the NPS score easily and reliably with AI, but behind it there would be little depth of understanding and the organization would not know how they could boost the score-they would have the outcome, but not the knowledge.
What About Qual?
AI is not intelligent enough at the moment to be able to manage consistency.
With quantum ventures, it will help out and certainly run your simple frequencies. But it might be nice to have a helping hand in the case of qualitative studies, where you may have tens of thousands of words to read through as part of your review. You may need a researcher in these situations.
It will assist with the bulk of analysis from online live chats, online research groups where the amount of content is staggering, once AI has been further evolved to be able to manage thematic, sentiment analysis and other primary qualitative analysis techniques.
But then with qual, which did with quant, the same problem would arise. You will also need someone to go through the patterns and feelings arising from the research to make sense of them.
If you have applied one of the fundamental concepts of science (in particular triangulation), you would have to look at a researcher to be able to combine the results to give you the perspective from all the various elements of the analysis.
Bringing the Story to Life
There may be some fantastic tools available for data visualization that offer you a fast look at the data in charts and tables, but this ties back to the point I made earlier about giving people reams of data to go through; this is not what consumers want and the insight will not leap out at them.
How do you get it? There is a need to bring to life the story of the study. You need someone who has been through, asking the right questions about the right part of the information, delving into the right parts of the report, and then taking the time presenting it in a visually impactful way to get the real insight out, and unfortunately. At the moment, AI can't have this.
You Can't Replace Experience
To provide insight into the study results, providing an understanding of the organization and the problems they face is something that is invaluable. It is something that only an accomplished researcher can do to be able to see the issues, apply the knowledge and tell the organization what it really means to them. Although AI may be intelligent and may save the researcher time, I can't see it being able to substitute their expertise and experience accumulated during their career by research.
I've come back to wanting a researcher at each point in this article to be able to carry out the knowledge from analysis. AI will assist with aspects of the method of study, but you need the brain of a researcher to provide perspective.
If AI can be implemented by doing some initial number crunching or thematic and sentiment analysis of the qualities to make our lives as researchers simpler, then great; but there is a time and a place for a researcher to take over, ask certain questions that automation cannot and provide the insight. How do you think that artificial intelligence will influence the business sector, and how do you think that the position of researchers will change over the next few years?