Scaling Research Without Scaling the Team
How the VP of Design at a publicly traded cybersecurity company used synthetic users to extend the reach of research across the organization
This case is anonymized due to client confidentiality.
Every UX research team faces the same challenge: reach.
No matter how good the research team is, they can only support a fraction of the decisions that would benefit from user insight.
Most organizations accept that constraint.
Kim, the VP of Design at a publicly traded cybersecurity company, didn’t.
Her team had spent years building a deep understanding of users through interviews, personas, workflow studies, and jobs-to-be-done research. Yet most of that knowledge only influenced decisions when a researcher was directly involved.
She saw an opportunity to change that.
The question wasn’t whether AI could make research faster.
It was whether AI could help research reach further.
Kim refused to accept this constraint, she saw a bigger opportunity
The natural instinct in most organizations would be to add more headcount.
But that is a temporary fix. The additional capacity only extends research’s reach incrementally.
Kim wanted to solve the underlying problem.
How to bring deep user insight to anyone in the organization, whenever they needed it? Without adding more researchers.
That question led her team to explore synthetic users: AI-generated personas built from years of interviews, personas, workflow studies, and jobs-to-be-done research.
Synthetic users, if done right, could help her achieve that vision
The idea was compelling. But compelling wasn’t the same as proven.
The team had experimented with synthetic users on their own and hit a wall. Responses were inconsistent. Quality varied in ways that were hard to predict or control. They couldn’t tell what was working and what wasn’t, or why.
That’s where Echo Point came in.
Not to advocate for synthetic users, but to determine under what conditions they were actually trustworthy. And build a system the team could rely on.
The team discovered three things, that changed everything
Over the next few weeks, the joint team tested synthetic users against real workflows: technical documents, Figma prototypes, screenshots, and recorded walkthrough videos.
Three findings changed how they approached the problem:
1. Trust came from curation, not volume
The most reliable synthetic users weren’t built from the largest amount of research. They were built from carefully selected research artifacts relevant to the task at hand. Too little context produced generic responses. Too much introduced noise. The best synthetic users were purpose-built.
2. Reliability depended on the workflow
Some use cases produced stronger signals than others. Synthetic users responding to text and images generated genuinely valuable insights. Video was more challenging and required additional care.
3. The real breakthrough was the process
What started as an experiment became a repeatable system for building, testing, and validating synthetic users. The team wasn’t just learning whether synthetic users worked. They were building internal protocols on when they worked, why, and how to scale.
What started off as an experiment quickly became a new capability
By the end of the engagement, the team had built two synthetic users grounded in real user research: a SOC Analyst and a Security Engineer.
But the personas themselves weren’t the most valuable outcome.
The bigger achievement was the system behind them.
The team now had a repeatable process for building, testing, and validating synthetic users. They understood how much context to include, which workflows produced reliable signals, and how to evaluate responses before trusting them.
What began as an experiment owned by a small group became a capability that could be scaled more broadly.
Kim’s vision was finally within reach
The rollout ahead was intentionally phased: a small internal team first, limited deployment of the two existing agents, enablement before expansion.
That sequencing matters.
Synthetic users deployed without a protocol aren’t a capability, they’re a liability.
The technical question has largely been answered. The organizational one is just beginning.
What this team built was a foundation rigorous enough to scale.
And for Kim, she was finally able to extend the reach of research.
so that more of the organization could make better decisions.
This case is anonymized due to client confidentiality. If you'd like to speak with Kim directly, I'm happy to make that introduction.
Echo Point works with product leaders who are trying to get more out of the product, design, and research teams. Happy to compare notes if useful.