One of the core ideas behind agile development is that learning should happen continuously — not only at the beginning of a project or right before launch. Yet many product teams still struggle to get enough real exposure to users during development. Access is limited, recruitment takes time, and research often becomes something “special” instead of something habitual.
This is where synthetic users and AI-assisted research loops can become valuable.
The goal is not to replace real users. The goal is to increase the frequency of learning.
Modern teams can now run rapid “build → test → learn” loops with synthetic personas or digital twins that simulate likely user reactions, behaviors, and questions. This allows teams to expose ideas to feedback much earlier and much more often than traditional processes allow. Instead of waiting weeks for the next research cycle, teams can pressure-test flows, concepts, messaging, onboarding, or service logic continuously as they build.
After several synthetic rounds, teams still validate with real users in the field. That is the critical grounding mechanism. The difference between synthetic responses and actual human behavior becomes a calibration signal that continuously improves the synthetic personas over time.
This thinking aligns strongly with decades of UX and agile research. Nielsen Norman Group has long advocated iterative testing and continuous exposure to users as a core principle of good design. Jakob Nielsen’s classic work on iterative design emphasizes that usability improves through repeated cycles of testing and refinement, not through large upfront specification work.
NN/g also argues that smaller, repeated usability tests are often more effective than large infrequent studies. Their famous “5 users” principle was never really about minimizing research — it was about maximizing iteration frequency. The point was that teams should test early, learn quickly, adjust, and test again.
That mindset becomes even more relevant in AI-supported product development.
The more often a team exposes ideas to perspectives outside themselves, the less likely they are to accidentally design only for their own assumptions. Every research interaction — synthetic or real — creates an opportunity to notice blind spots, misunderstandings, edge cases, emotional reactions, or contextual realities that the team could not see alone.
In practice, many agile teams fail not because they lack talent, but because they spend too much time internally validating ideas with people who already understand the system. Over time, products begin reflecting organizational logic instead of human logic.
Frequent testing interrupts that pattern.
Research also becomes culturally important when it becomes lightweight and embedded into everyday work. Continuous UX research literature increasingly points toward integrating research directly into agile delivery instead of treating it as a separate phase.
The emerging opportunity with synthetic users is that teams may finally gain the ability to practice user-centered thinking daily instead of occasionally.
Not because synthetic users are perfect — they are not — but because they lower the threshold for reflection, experimentation, and feedback. And when continuously calibrated against reality, they may help teams stay connected to user perspectives between real-world research sessions.
The future may not be “AI instead of users.”
It may be a continuous double-loop system:
synthetic testing for speed and frequency, combined with real-world testing for grounding and truth.
try the tool here http://Mckayconsulting.dk/synthetic users
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