August 13, 2025
AI is changing how we access information, but most research tools are stuck retrofitting old workflows with shiny chat interfaces. To unlock real insight in the AI age, we need to rethink market research UX from the ground up—designing for how researchers think, not just how AI answers.c
Why we’re rethinking market research UX from first principles
The rise of large language models (LLMs) is transforming how we interact with information. From autocomplete emails to AI-authored memos, the barrier between question and answer is shrinking. In the consumer space, this feels like magic. In the enterprise, it's fueling a wave of optimism: that AI can automate the tedious, scale the complex, and turn mountains of data into clarity.
But when it comes to market research, reality hasn’t quite caught up to the hype.
Research—especially when it blends quant and qual—isn’t just about getting an answer. It’s about understanding humans: what they believe, what they want, and what they’ll do next. That kind of insight doesn’t live in a single dataset or summary. It requires triangulating between hard numbers and human nuance. It requires exploring contradictions, surfacing themes, and making meaning—not just finding facts.
LLMs, when applied thoughtfully, have the power to unlock this kind of understanding faster and more scalably than ever before. But today, many of the tools claiming to “revolutionize” research are simply retrofitting existing workflows with chatbots or AI-powered dashboards. They treat research like a task to complete, not a craft to sharpen.
Instead of enabling deeper thinking, they speed up shallow work.
At Knit, we think the opportunity is bigger. It’s not just about adding AI to research—it’s about reimagining what research could look like if it were built, from the ground up, for this new AI-native world.
In the early days of AI UX, chat was the obvious interface. It mimicked how we talk. It felt approachable, flexible, and lightweight—especially for tools aiming to feel magical. But as chat became the default interface for AI interaction, it also became a crutch.
Chat is great when your question is simple. When you want a concise answer, a quick summary, or a creative idea.
But market research is rarely simple.
Researchers don’t just ask a question and accept the answer at face value. They refine. They compare. They ask follow-ups. They validate and contextualize. And they do all of this while juggling stakeholders, timelines, and the messy realities of human data.
Chat isn’t built for that.
The result? Researchers are often left supplementing AI output with manual work anyway—cross-checking, reformatting, rewording—undoing the very efficiencies the tool promised to deliver.
And yet, the appetite for AI in research is real. It’s just that the interface—and the mental model behind it—is overdue for an upgrade.
If market research tools keep chasing speed without rethinking the experience, we risk replacing old inefficiencies with new ones. The real promise of AI in research isn’t just to answer questions faster—it’s to help us ask better ones, see connections we might have missed, and arrive at insight that is both robust and resonant. In an era where decision cycles are accelerating, the ability to move from raw data to meaning—without sacrificing rigor—will separate the teams who lead from those who lag.
The takeaway: AI will only transform research if we design for how researchers actually think and work, not just for how AI likes to respond.
At Knit, we’re building the next generation of Researcher-Driven AI—purpose-built to handle the nuance, complexity, and context that real insight demands. Our platform unites quant and qual in a single environment, makes it effortless to move between the big picture and the verbatim detail, and keeps you anchored in the “why” behind the numbers. We believe that when AI is designed for researchers, it doesn’t just make research faster—it makes it better. And in the AI age, better research means better decisions, made with confidence.
