AI didn’t write this story: How researchers use AI to unlock human truths in real research
Editor's note: This article is an automated speech-to-text transcription, edited lightly for clarity. For the full session, please watch the recording.
AI is not replacing researchers. The technology is simply serving as an assistant to allow the researcher to focus on the insights.
Alexis White, research lead at Knit, shares how AI is used by researchers in their day-to-day work. One way is through using AI to automate repetitious tasks freeing the researcher to focus on the business objectives.
Session transcript
Joe Rydholm
Hi everybody and welcome to our session, “AI didn’t write this story: How researchers use AI to unlock human truths in real research.”
I'm Quirk's Editor, Joe Rydholm. Before we get started, I just wanted to quickly go with the ways you can participate in today's discussion.
You can use the chat tab to interact with other attendees today during the session, and you can use a Q&A tab to submit questions for the presenter during the session and we'll answer as many questions as we have time for during the Q&A portion at the end.
Our session today is presented by Knit. Alexis, take it away!
Alexis White
Sure. Thank you so much and welcome everyone. Thanks for spending some time with me today. As mentioned, my name is Alexis White and I'm a research lead at Knit.
Today I'll be talking about how professional insights teams like me are actually using AI in real workflows to unlock human truths, not hypothetical use cases, not future promises, but what this looks like in practice.
And I want to be very clear from the start, this is not a story about how AI is replacing researchers. It's a story about how AI removes friction from the research process so that researchers like me can spend more time doing what we uniquely do best. So, thinking, interpreting and telling meaningful stories to ground us.
I'll start with a quick overview of Knit and how we think about AI and research.
For those of you who may be newer to Knit, here's the high-level picture. Knit is designed to be the simplest way to go from survey to story in days, not weeks. We combine the power of speed and power of AI with strategic guidance of expert researchers like me end-to-end. This includes AI assisted survey creations, automated analysis across quant and qual data, and dynamic editable reporting all in one platform.
The goal for us isn't just speed, it's removing the manual work without sacrificing rigor, depth nor trust. And an important part of that promise is the role that humans like myself play in this process. So, let's talk about that next.
Knit is built on the idea that AI should support researchers not replace them. Expert researchers guide and refine the work at every stage, really ensuring that the insights are grounded in business context and real human understanding. You get the benefit of a full service research experience, but with self-service speed, cost efficiency and most importantly, control over the experimental design.
AI is really great because it handles a lot of the repetition and the scale where researchers like me can handle the judgment, the nuances and the storytelling of the data.
So, what exactly does that mean across a real end-to-end workflow?
At Knit, we support the research lifecycle from survey creation to fielding in QA to analysis and reporting. The AI helps us draft and refine our questionnaires. It monitors data quality, analyzes our open-ends and helps us cluster themes and summarize our survey respondents.
What this does is it dramatically reduces the time researchers spend on mechanical tasks, which frees us up to focus on interpretation and most importantly, implications. One of the most powerful places this shows up is when quant and qual live together in the same study.
Knit brings together quant and qualitative data into a single study and a single dataset. Quant, as you guys know, really helps us understand what's happening at scale. Where qual gives us the language, emotions and motivation behind those behaviors. Because when those two data sets live together from the start, we're no longer stitching together insights after the fact. We're building a cohesive story from the beginning.
And when you show people AI can do all of that, it usually triggers the same question. If AI can analyze the data, what's left for the researcher?
This is the question that everyone is asking, including myself sometimes. If AI can write surveys, if it can analyze data, if it can summarize themes, what's left for us to do? Quite frankly, it's a fair question and answering that is really at the heart of today's conversation.
To answer it, we do have to be clear about the differences between output and insight. As we know, AI is very good at generating output, whether it's summaries, charts, dashboards, you name it. But insight is a little different.
Insight requires judgment, it requires context. It really requires deciding what matters and why it matters. And that's where human researchers like us are still essential and where AI is actually most powerful as a supporting tool.
This is exactly where the industry gets stuck. So, let's clear up this myth versus reality.
The myth is that AI is the author of insight, that full automation leads to faster and better decisions. The reality, as a lot of us are learning, is that faster output doesn't automatically equal better insight.
What AI is actually very good at is scale. As you guys know, it can scan massive data sets, identify patterns and cluster themes. It could really surface some of those signals that we can't actually do on a reasonable scale by ourselves.
But what it can't do is decide what matters. It doesn't understand business context. It doesn't understand which tensions are strategically meaningful for us. And honestly, that's where we come in as researchers.
One of the mental models that I find most helpful is that AI is a story finder, and we're storytellers. The AI is really good at surfacing those signals across data, but what we do is help shape those signals into meaningful judgment and narrative.
And to make this real, I want to walk you through a project where I use AI throughout the entire process and show you exactly where it helped and where my judgment mattered the most.