Key takeaways from Sprig’s latest Research Leaders Virtual Roundtable
If you're a researcher right now, you're probably tired.
Not the "I need a vacation" tired. But "the rules changed while I was mid-study and now my perfectly crafted research plan is already outdated" tired.
At our recent Research Leaders Roundtable, we heard from VPs, directors, and senior researchers across product orgs: the ground is moving. And not slowly by any means.
The Insights Shelf Life Problem
Here's a scenario that came up three separate times in our discussion:
You spend two weeks on foundational research. You synthesize beautifully. You build a deck that would make any researcher proud. You present it to stakeholders who nod enthusiastically and say "this is gold."
Two weeks later, someone asks, "Hey, didn't we do research on this?" And your perfectly crafted insights are... somewhere. In a deck. In a folder. Behind three clicks and a search query no one can quite remember.
The insight died in delivery
One participant noted that we’ve moved beyond archiving; now our job is to connect signals and ensure they continue to evolve.
So what does that actually look like in practice for today’s teams?
- Insight Slack channels that pipe research directly into where decisions happen (one team sees 3x more engagement this way)
- 15-second video clips from sessions that reignite empathy faster than any quote slide
- Lightweight research snapshots that travel better than 40-page decks. Think one-pagers that PMs actually reference in sprint planning
- Centralized insight hubs where anyone can ask "what do we know about x?" without Slacking the researcher at 4pm on Friday
Continuity is becoming the new backbone. Research flows through the org like a current, surfacing insight where and when it’s needed.
AI Is Here. And It's Complicated
There was zero debate about whether AI has entered the research workflow. Everyone's using it somewhere.
But the "how" is all over the map:
What's working:
- Drafting discussion guides in minutes (then adding the human nuance)
- Synthesizing 30 interviews into themes as a starting point for deeper analysis
- Creating synthetic stakeholders for practice runs before real conversations
- Generating prototype variations to test before committing to builds
What's breaking:
- The recruitment quality crisis.
AI has made it laughably easy for participants to game screeners. Multiple teams described the same nightmare: running a study, getting suspicious responses, and realizing they just paid for AI-generated feedback.
The trust problem is real and expensive.
This is exactly where integrated platforms matter. When you're recruiting from your actual user base—people already using your product—you skip the professional survey-taker economy entirely. You get real signal from real users without the quality tax.
The "Shift Left" Movement
One theme dominated the second half: getting into the room earlier.
Not because researchers are trying to grab more territory. Because by the time research gets called in to "validate" something, the train has often left the station. Teams are already emotionally invested. Timelines are set. And research becomes a checkbox instead of a compass.
What shifting left actually looks like:
- Researchers in weekly product planning rather than just quarterly planning
- Quick pulse checks before specs are written, not after
- "Is this the right problem?" questions before "Is this the right solution?" testing
One VP noted that their team has moved away from counting studies as the main success metric. What matters now is how quickly they can surface and shut down ideas that would have created downstream waste. The “graveyard” has become an essential signal of impact.
This requires different tooling. You can't do heavyweight studies every time someone has a question. You need continuous research infrastructure. Always-on access to users, fast-turnaround methods, and insights that stack over time instead of sitting in isolated reports.
The Impact Question No One Has Solved (But Everyone's Trying)
"How do you measure research impact?"
This question produced the longest pause of the entire session.
Because here's the truth: research influence is everywhere and nowhere. You prevent a terrible feature from shipping. How do you quantify the disaster that didn't happen? You provide strategic direction that gets diluted through three layers of telephone. How do you claim credit for what emerges?
Teams are getting creative:
- Fact bases that connect insights across studies, showing patterns over point-in-time findings
- Triangulation frameworks that combine user research, support tickets, analytics, and market data
- Quarterly "saves" reports highlighting engineering hours not wasted on the wrong thing
- Decision traceback that documents when research directly changed course
But honestly? Most admitted they're still figuring this out.
What everyone agreed on: the healthiest signal of research impact is twofold. Enabling good bets and preventing costly ones.
One Truth to Hold Onto
In the closing minutes, someone said something in a similar vein:
The real risk isn’t AI itself, but losing the depth and rigor of the craft in the rush to adopt new tools. The responsibility now is to use AI thoughtfully, letting it accelerate the work where it can, and relying on core research fundamentals where judgment and nuance matter most.
This is it. This is the new reality.
Research now plays an active role in shaping how organizations navigate change.
Continuing The Conversation
If any of this resonated, or if you're wrestling with your own version of these challenges, join us for the next virtual roundtable.
Research Leaders Roundtable: Moving the Needle on Research Impact
December 17, 2025 | 11:00 AM PST
We'll dig into:
- Impact frameworks that actually stick
- Democratization without dilution
- The "research as n of 1" approach
Register here →
Because the ground is moving. But you don't have to move alone.
