By Daniel Graff-Radford, CEO, Discuss
In April, I met with 40-plus brand-side and agency research leaders in New York to work through a question I can’t stop asking as CEO of Discuss: not whether AI would change market research, but who in the organization would actually benefit when it did. I wasn’t there to teach. The clearest answer came from a senior insights leader at a global CPG company whose organization manages research across dozens of brands and multiple regions. You’d be right to assume she was solving for speed. She told the room she was solving for access.
Her framework: one unified insight source, rendered at three different levels. The president of the Americas gets the strategic essence: the two or three things that should reshape how the company thinks about consumers in the next 18 months. Category VPs get the regional implications: what this means for their markets, their competitors, their customers’ specific behaviors. Brand managers get the granular tactical data: what to say in the next campaign, what packaging claim to test, what product improvement to lead with in the next launch.
Same research. Three audiences. Three different conversations.
At Discuss, our work has pointed consistently toward the same conclusion: the bottleneck in most insights functions has less to do with research quality or research speed and everything to do with having the infrastructure to get research to the right person at the right moment. Hearing it described by that insights leader, in her terms, influenced what I believe about why the industry keeps solving for the wrong variable.
Why Is the Insights Industry Still Optimizing for Speed Instead of Access?
The insights industry has spent most of the last decade chasing speed. How fast can we field? How quickly can we deliver? How many days between question and answer? These are legitimate problems, and I understand why we’ve focused on them. But faster delivery and broader access are not the same thing, and treating them as equivalent is where most insights functions get stuck.
I’ve talked to insights leaders who have dramatically compressed their research cycles and still watch their work get ignored. They deliver faster decks that more often than not sit unread. They field more studies that don’t connect to each other.
The CPG leader’s framework is the counterexample. When the same research reaches the C-suite as strategic direction, category VPs as regional implication, and brand managers as tactical instruction, the question of how fast it was fielded becomes secondary. The research didn’t get faster. It went further.
Is the Distinction Between Qualitative and Quantitative Research Disappearing?
The traditional distinction between qual and quant has always been partly a tool distinction and partly an organizational one. Quant teams owned one stack, qual teams owned another, and the synthesis happened in a PowerPoint deck that nobody had time to read. What’s changing isn’t just that AI can now process unstructured language at scale. It’s that the wall itself is becoming indefensible.
What’s happening on the ground is more instructive than the prediction. A senior insights leader at a major financial services company described how his organization spent years leaning toward qualitative work because it was faster and cheaper. This year, for their highest-stakes product bets, they ran at least one quantitative survey per development phase. Because someone decided quant was better? No. Because their internal product review team required validated data before work could advance to the next stage.
The governance process drove the change. And once quant data became a requirement for progression, the way teams sequenced their research shifted with it. They were moving from qual to quant rather than the other way around, because the question the institution needed answered had changed.
Organizations still managing two separate stacks, two separate teams, and two separate reporting cycles are going to find themselves explaining a structural disadvantage that no individual researcher’s skill can compensate for.
What Infrastructure Do Insights Teams Need to Serve Research at Multiple Levels?
The CPG leader’s unified insight framework might sound like a communication problem. I believe it’s an infrastructure problem.
Rendering the same research at three different resolutions requires that the underlying research actually lives somewhere queryable. You can’t serve the C-suite a strategic distillation on Monday and give a brand manager the granular tactical data on Tuesday if the research lives in a vendor’s platform that requires a project request and a three-week timeline to access. The research has to be connected, indexed, and accessible at the moment each stakeholder needs it.
The teams I’ve seen execute this well have done something that sounds obvious and is actually hard: they’ve stopped treating research as a series of projects and started treating it as infrastructure. Each study is an addition to the organizational knowledge base, not a standalone deliverable. Each new question is filtered first through what has already been learned.
The customer roundtable in New York made the case plainly: speed-to-insight is a distraction from the more important question of who in the organization can actually use the insight when they need it.
What Will Separate High-Impact Insights Leaders from the Rest Over the Next Three Years?
If insights leaders want to earn a permanent seat at the executive table over the next few years, they need to spend less time focused on speed and more time making research accessible to the right person at the right resolution, precisely when a business decision needs to be made.
That requires thinking like an architect, not a supplier. The researchers who make that shift won’t need to argue for a seat at the table.
Daniel Graff-Radford is the CEO of Discuss, an industry leader that unifies AI with qualitative depth and quantitative scale. Under his leadership, Discuss was named a Forrester Wave™ 2026 Leader in Experience Research Platforms. A named inventor on multiple technology patents, Daniel also serves as an Executive in Residence for the Mayo Clinic’s AI initiatives.
