What happens to the research function when the product organization no longer needs to wait for you?
It’s a question that’s been top of mind for me since joining Sprig. Historically, stakeholders waited for research. They might have done so impatiently, but they waited. Today, they have AI. Automated, "good enough" data is readily available to fill any gap, instantly.
The core challenge facing research leaders right now isn't whether our teams need to move faster, because we obviously do. The real challenge is how to move faster without crossing the line that makes our findings actively misleading.
We recently brought together a group of research leaders for a virtual roundtable to swap notes on how they are navigating this exact tension. Here is a peek at what we discussed, what’s working on the ground, and how AI is completely rewriting the equation.
Quick Summary: What is Minimum Viable Rigor (MVR)?
Minimum Viable Rigor (MVR) is a product research framework coined by Carl Pearson, PhD, that calibrates the depth of a research study against the business risk of the decision being made. It prevents teams from falling into "rigor theater" (over-researching low-stakes decisions) or under-researching high-stakes product changes.
What is the Minimum Viable Rigor (MVR) Framework?
Carl Pearson defines MVR using a beautifully simple concept:
Rigor of Insight - Decision Risk
To make this work in practice, you have to separate the roles:
- The Researcher owns the rigor assessment: How clean is the methodology? How valid are the findings? What corners did we cut, and why?
- The Stakeholder owns the risk tolerance: How much is on the line if this decision goes wrong? What is the business blast radius?
When you keep these two jobs separate, you maintain credibility while moving at warp speed. It reframes the conversation entirely. It’s not about "how do we do less research," but rather "how do we calibrate the right level of research to what’s actually at stake."
When you miscalibrate, you hit one of two failure modes:
- Too little rigor: Your insights actively mislead the business, leading to massive product landmines down the road.
- Too much rigor (Rigor Theater): You run an elaborate, pristine methodology on a low-stakes call, only to arrive after the decision has already been made.
Both are a loss for the business.
How Do Research Leaders Categorize Study Rigor and Decision Risk?
During the roundtable, we pushed past high-level principles to find out how leaders are actually drawing the line between MVR and poor research.
1. Mapping the Negative Blast Radius
One research leader shared how their team categorizes study rigor based on the negative blast radius of a bad decision:
- Low Risk decisions (like button colors or minor UI tweaks): Move it straight to A/B testing territory.
- Medium Risk decisions (like feature architecture): A quick, unmoderated study fits the bill.
- High Risk decisions (like strategic business direction): Full-rigor, research-led deep dives.
Another leader built on this, noting that rigor should scale based on uncertainty thresholds and cross-functional impact. If a decision forces multiple teams to shift their roadmap or impacts broader business strategy, the threshold for rigor automatically skyrockets.
2. Flipping the Intake Model to Focus on Decisions
One leader shared how their enterprise research team shifted to a coaching model. Instead of taking orders for projects, they frame everything around the business decision rather than the research question:
“Tell me the decision you need to make, and I’ll tell you the rigor you need.”
In this model, when product teams choose to run feature-level research independently, they also take on the responsibility for that study's rigor. This shifts the core research team out of the gatekeeper role and into a consultative partnership. It ensures that product teams feel supported but accountable for the data driving their day-to-day choices, while freeing up researchers to focus on cross-team, highly strategic initiatives.
3. Balancing Institutional Timelines with Data Quality
Of course, theory is easier than practice. One research leader pointed out a common institutional gap: while internal planning guidelines might suggest a two-week runway for a project, the reality on the ground is often getting the green light just six hours before interviews start.
The dangerous part? As another roundtable attendee pointed out, stakeholders are looking for solutions to business problems, not just usability tweaks. If we rush and deliver bad data, the negative effects won’t show up for months or even years.
How is AI Changing the Minimum Viable Rigor Equation?
Everything gets significantly more complicated when you inject AI into the MVR workflow. It cuts both ways.
On one hand, AI compresses the time cost of running a rigorous study. Tools that automate study design, make surveys conversational and adaptive, and synthesize findings in real time are collapsing timelines that used to take days into mere hours. When the setup and analysis friction drops, the threshold for running a study drops too. In theory, you can afford to be more rigorous, more often, on more decisions.
But here is the part that doesn't get talked about enough. AI is also introducing entirely new threats to validity that the original framework didn't have to account for:
- Traditional MVR Risk: Human bias and slow timelines.
- AI-Extended MVR Risk: The illusion of accuracy and highly credible hallucinations.
When AI synthesizes qualitative data, it can surface patterns that aren't real, or it can completely miss the human nuance that an expert analyst would catch. A synthesized report can look perfectly credible, read cleanly, have themes and quotes, and land in a stakeholder deck without anyone questioning it. But it can be subtly, fundamentally wrong.
As several leaders noted during the session, we are seeing a massive rise in stakeholders throwing raw transcripts into AI models without any validation. They get a clean output and feel like they have "data."
This creates a dangerous illusion of answers. The perceived need for professional research drops, even though the actual decision stakes remain incredibly high. In this new landscape, MVR becomes the researcher’s primary argument for why an unvalidated LLM output is not the same as research, and why it falls dangerously below the safe line for the decision at hand.
Conclusion: Shifting from Research Executor to Orchestrator
If there is one takeaway from this roundtable, it’s this: Carl Pearson’s MVR framework has always been a cornerstone for research leaders, but the rise of AI gives it an entirely new layer of urgency.
AI has given everyone the tools to generate data, but it has also given them the illusion of certainty. Our job as researchers is shifting. We are no longer just the gatekeepers of data collection; we are the defenders of validity. We have to know exactly where the MVR line is for our business, hold that line, and make the case clearly when the room thinks a machine has already given them the answer. That is harder now, but it's also more important than ever.
This tension, between what AI can execute and what researchers must fundamentally own, is exactly where we are heading next.
On Wednesday, June 24th, I’ll be leading our next Virtual Roundtable: On the Loop: The Shift from Executor to Orchestrator. We’ll dive deep into what research mastery looks like when your role shifts away from manual execution and toward designing automated workflows, setting guardrails, and making the crucial judgment calls that a machine simply cannot replicate.
Spaces are limited to keep the discussion highly collaborative. Secure your spot for Session 02 here and let’s figure out together how to lead in this next era of research.
