The robot war is no longer a distant fantasy, but the battle is quieter than we’ve depicted it on screen. With AI changing the landscape of so many industries, we are challenged to find the balance between utilizing automation for productivity, and keeping humans, their skills, and their jobs intact. As a tool to cut down on the more tedious aspects of many jobs, AI can be invaluable. But this tool has limitations and is often met with skepticism. At Response:AI, we strive to create a symbiotic relationship between our human team and our robot friends. So when a fellow researcher poses the question over social media, “can you automate market research?,” we were ready to answer.
To understand the challenges of AI in market research, we first need to understand an AI’s limitations. Market research is driven by data, statistics, graphs, and charts. Survey structure is often formulaic and repetitive, key words are easily identifiable, and trends become easily recognizable patterns to our Hal 9000 pals. But most clients utilizing market research are not experts in survey scripting. The process of survey drafting, editing, and programming is time consuming and nuanced. There is often substantial back and forth between client and researcher to understand the goals behind the questions, and how to anticipate and analyze human behavior within surveys. Understanding a survey taker’s capacity for survey length, anticipating the difficulty of reaching target groups, and knowing how to address sensitive or polarizing topics takes a uniquely human touch. At what point does an AI go from time saver to time suck? Is constant monitoring and editing more labor-intensive than simply doing the work manually? That depends both on the human and the AI.
After the survey comes the data. We at R:AI let our binary besties shine here, turning tables, charts, and percentages into beautiful, coherent, and engaging presentations. Gone are the days of ugly templates or creating graphs from scratch. We tell the robots what to look for and they make it look good, complete with your custom branding. This saves hours, if not days, of some of the most traditionally time-consuming work in the industry. But can AI make conclusions from that data? Our programs have some ability to self-comment basic observations from standard questions. But in general, the answer is “no.” And that might just be the beauty of it.
In the words of Response:AI CEO, Frederick Barber,
“The benefit of automation is the ability to handle the tedious and error prone tasks of questionnaire creation, tabulation and report building. Automation platforms (like our Response:AI platform) have libraries of questions you can drag and drop into a survey. They auto-generate the survey link and API to sample. They automate common data hygiene / QC tasks, and then automate graph and table production from the collected data. That can save you days of typing numbers into Excel to make graphs that you paste into PowerPoint. And it removes the risk of mistyping a number. But automation will never replace the ‘intellectual value add’ of a skilled researcher. It doesn’t know how to translate vague client briefs into clear research questions, how to finesse the best question phrasing, or how to tease out the real insights from the findings. That is what researchers do. Automation just handles the repetitive and error prone tasks to allow the researcher to focus on the research.”
While we often think of AI as a tool to enhance human performance, the inverse may be more accurate. Human insight shapes and polishes AI data. So in short, a good robot friend knows how to do the boring stuff fast, so that talented researchers can delve into the data for the insights you need and deserve. It’s a match made in heaven…or the cloud.
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