The Speed Paradox
Tech companies face a problem they rarely talk about openly. Moving fast gets you to market, but moving without direction wastes resources. Waiting for perfect data means the window closes before you ship. This tension between speed and substance defines modern product development, and few people understand the balance better than researchers who have worked across multiple high-stakes environments.
Eric Morrison brings 13 years of experience leading user research at Google, TikTok, and Disney. He currently leads research at Google on the future of AI in the workplace, where velocity matters but getting it wrong has consequences. His background spans a history degree from Yale, where he earned the John Addison Porter Prize, and a Master’s in the Social Science of the Internet from Oxford, where his thesis on team diversity won the Oxford Internet Institute Prize. That interdisciplinary foundation shapes how he thinks about research as both rigorous and responsive.
“There is a hidden cost to moving fast without direction. But there is also a massive cost to moving too slowly. If you take six months to deliver an insight, the world has already moved on,” he explains. The question is not whether to prioritize speed or depth. The question is how to structure research so you get both.
Build for Repeatable Process, Not One-Off Wins
The mistake many teams make is treating research as a validation step rather than a discovery engine. They run a study, gather feedback, ship a feature, and hope it lands. When it works, they celebrate. When it fails, they wonder what went wrong. Neither approach builds institutional knowledge.
Morrison sees this differently. “Too often, product decisions are based on assumptions or one-off successes. Our job is to find the repeatable formula. If we understand the fundamental process of why a user finds value, we can engineer that success again and again,” he says. That means looking for patterns across users, contexts, and behaviors rather than chasing isolated wins.
His approach is rooted in breaking down complex outcomes into foundational processes. “I’ve always been fascinated by the underlying mechanics of how people adopt new tools. My goal isn’t just to observe behavior, but to decode the specific sequences that lead to a successful product launch,” Morrison notes. This mindset shifts research from reactive to proactive. Instead of asking whether users like a feature, you ask what conditions need to exist for adoption to happen at scale.
Match Your Method to the Question You Need Answered
Not every research question requires the same level of rigor. Some decisions need ethnographic depth. Others need directional signals fast. The key is knowing which tool fits the moment.
Survey work can provide quick reads on sentiment or prioritization across large user bases. Interviews reveal the why behind behaviors and uncover edge cases that quantitative data misses. Ethnography captures context and workflow in ways that lab settings cannot replicate. Morrison has used all three across his career, often in combination.
“History and the social sciences give me a unique approach to UX research. I believe that even the most complex outcomes can be broken down into foundational processes that can be replicated,” he says. That belief shapes how he structures research programs. When speed matters, he leans on rapid methods like surveys or short stakeholder interviews. When the stakes are high and the problem is novel, he invests in longer-term observation and analysis.
The mistake is using the same playbook every time. Teams that default to the same research format regardless of context either move too slowly or miss critical nuance. Velocity comes from knowing when to go deep and when to move fast.
Integrate Research into Decision-Making, Not Just Documentation
Research loses impact when it lives in slide decks instead of strategy discussions. The most common failure mode is not bad research. It is research that arrives too late, answers the wrong question, or gets buried under competing priorities.
Morrison has built his career on making research actionable at the executive level. That means understanding what decisions are on the table, when they need to be made, and what evidence will actually shift the outcome. “History taught me to ask why things happen the way they do. That curiosity still drives my work today,” he reflects. Applied to product strategy, that curiosity translates into research that informs roadmaps rather than just documenting user complaints.
Velocity increases when research is embedded in the decision cycle rather than bolted on afterward. This requires researchers to build relationships with product managers, engineers, and executives so they know what questions matter before the meeting happens. It also requires clear communication about what research can and cannot deliver within a given timeframe.
Maintain Rigor Without Sacrificing Relevance
Speed does not mean cutting corners. It means being disciplined about what you need to know and how you will know it. Rigor comes from clear hypotheses, representative sampling, and structured analysis. Relevance comes from aligning research questions with business goals.
Morrison emphasizes that velocity and quality are not opposites. They require calibration. “There is a hidden cost to moving fast without direction. But there is also a massive cost to moving too slowly,” he says. The balance point shifts depending on the product, the market, and the risk profile of the decision.
For early-stage exploration, smaller sample sizes and rapid iteration make sense. For major launches or policy changes, deeper validation is necessary. The mistake is treating all research as equally critical or equally disposable. Strategic velocity means investing time where it matters and moving quickly where it does not.
Trust and Transparency Keep Teams Aligned
When research contradicts assumptions or reveals uncomfortable truths, teams face a choice. They can dismiss the findings, debate the methodology, or adjust the plan. The outcome depends largely on trust.
“Users need to trust the systems they use. If they don’t understand what AI is doing, it won’t be effective. Clear communication and control are essential,” Morrison notes. The same principle applies internally. If stakeholders do not trust the research process, they will not act on the findings.
Building that trust requires transparency about methods, limitations, and confidence levels. It also requires consistency. Teams that see research inform good decisions repeatedly learn to rely on it. Teams that see research ignored or cherry-picked learn to work around it.
Velocity increases when everyone understands what research is designed to answer and what it cannot. That clarity prevents wasted cycles and ensures findings land when they matter most.
