Guest Blog Post by Jim White, Founding Partner and President at RealityCheck
The ability of AI platforms to synthesize and summarize data into written market research reports is remarkable. When ChatGPT came onto the scene about a year and a half ago, the internet was buzzing with examples of professionals in many areas using the platform to transform notes and rough drafts into finished written documents.
In our business, market research, the ability of AI to turn raw data – such as qualitative interview and focus group transcripts, text from asynchronous online studies, even some numerical data – into written summaries promises to save researchers time and effort.
You might wonder, in such a world, do writing skills still matter?
My answer is a definitive yes! (Big surprise, right?)
Here are three reasons why writing still matters in an AI world.
1. Writing is Thinking
One of my favorite quotes about the craft of writing is from Pulitzer Prize-winning historian David McCullough, who said, “Writing is thinking. To write well is to think clearly. That’s why it’s so hard.”
Leaning on AI for writing misses one of the great benefits of the writing process. Writing is a rigorous way to think through a problem and work out your ideas. Writing is not just an end product. The writing process is a pathway to discovery, no matter what your discipline. Nothing forces clarity of thought like writing. When I sit down to write a research report, I never know how it’s going to go. Because I don’t know where the writing process will take me.
Conceptual relationships I thought I saw in the data often don’t hold up under the scrutiny of the written word. Insights I thought were impactful lose their import when expressed in narrative form. But other conceptual connections emerge. And other insights gather steam through the process of writing the story.
My spouse and I have a running joke whenever I start to write a report. My wife Cheri – who, by the way, is a far better writer than me – asks, “How long will this one take?” My response is always the same. “I have no idea.”
And therein lies the dangerous temptation of AI. The last line of McCullough’s quote is perhaps the most important. Writing is not easy. It requires focus. Concentration. Hard work. Because of this, the AI easy-button is tempting.
But if you are in a business like I am that, at its core, is about ideas, don’t take the bait. Forgo the easy button and do the hard work of thinking through writing.
Another of my favorite quotes about writing is attributed to the sportswriter Red Smith. When asked if writing a daily newspaper column was hard, he said “Why no. You simply sit down at the typewriter, open your veins, and bleed.”
2. Writing is Analysis
Writing is not something you do after analysis. Writing is part of the analytical process.
In the market research business, those who use AI to summarize data and write reports are missing another crucial benefit that writing offers. The critical relationship between writing and analysis is something many anthropologists and academic researchers know well.
Fieldnotes are critical to the anthropologist’s work. As the famed anthropologist Clifford Geertz once wrote: “I’ve often been accused of making anthropology into literature, but anthropology is also field research. Writing is central to it.”
Even the anthropologist’s endpoint – the ethnography – has writing at its core. The literal translation of the term ethnography is writing (graphy) culture (ethno).
Similarly, the best qualitative researchers write “analytic memos” as they code, categorize and analyze data. In his book, “The Coding Manual for Qualitative Researchers,” Johnny Saldana writes, “…whenever anything related to and significant about the coding or analysis of data comes to mind, stop whatever you are doing and write a memo about it immediately. The goal is not to summarize the data, but to reflect and expound on them.” Put more simply, the sociologist Adele Clarke wrote, “Memos are sites of conversation with ourselves about the data.”
The craft of data analysis should be one in which the analyst pings back-and-forth between the data and the written word; between statistics and narrative; between tabulation, correlation, categorization, and story. Writing can and should be much more than summarizing and reporting your analysis. It should be part of your analytical journey.
3. Writing is Personal
Finally, writing—your writing—still matters in an AI world because writing reflects your unique voice. The more AI becomes the primary author of content in a given discipline, the more all deliverables in that discipline will begin to sound the same.
I believe my profession is both art and science. My guess is if you’ve read this far, you see your profession that way too. If the analysis and meaning you bring to your job is important, then expressing your insights through your own voice is something no AI platform can fully replicate. (And if it can, I say you’re not putting enough of your experience, expertise and unique perspective into what you do.)
This last point is important. If you use AI to replace yourself as a writer, you’re also replacing yourself as an analyst, a thinker, a meaning-maker, a unique and important voice in your field.
I don’t know about you, but I’m not ready to give up that ground to the machines just yet.
As always, I’d love to know your thoughts. How do you feel about AI doing the writing for you? What do you think is the proper use of AI in writing reports for market research or other disciplines?
You can find this article and other great content at www.realitycheckinc.com/blog/.
