What Figma Learned About Fidelity-Driven Prioritization
TRIGGER
Feature ideas languish in backlogs because static mockups and PRDs fail to generate executive buy-in—stakeholders can't viscerally understand the value proposition from wireframes or written specs alone, leading to slow alignment and theoretical debates about theoretical features.
APPROACH
Maven Clinic's Product Design Manager Loric Avanessian used Figma Make to resurrect a deprioritized feature: a map-based fertility clinic finder for their Maven Managed Benefit program. Input: existing mockups or rough designs fed into Figma Make with design system constraints. Output: interactive prototypes that feel production-quality, enabling stakeholder demos that generate buy-in and compress alignment cycles from weeks to days. She fed initial designs into Figma Make to create a high-fidelity interactive prototype that looked like it belonged in their product. The prototype was shared on Slack, attracted CEO attention, and moved the feature back onto the roadmap after languishing in the backlog for two years. Another designer iterated on the design before bringing it back into Make for deeper cross-functional feedback. Because they started at 45% instead of zero, the team designed, developed, tested, and launched the clinic finder MVP in less than four sprints.
PATTERN
“The same feature pitched as a mockup vs. a working prototype gets different prioritization outcomes. Stakeholders discount abstract value propositions but respond to tangible artifacts they can interact with—fidelity drives decisions more than logic does.”
✓ WORKS WHEN
- Feature value is experiential and hard to convey through static descriptions or wireframes
- Decision-makers need to 'feel' the product to understand its impact (maps, interactions, flows)
- Feature has been deprioritized due to unclear ROI rather than technical infeasibility
- Design system exists that AI tools can leverage for consistent, production-quality output
- Prototype creation cost is under 1 day of designer/PM time
- Feature involves interaction nuances that are 'almost impossible to describe' in words
✗ FAILS WHEN
- Feature value is primarily backend or performance-based (faster load times, better algorithms)
- Stakeholders already understand and agree on the feature's value but lack resources
- No established design system to constrain AI output, resulting in off-brand prototypes
- Feature requires real data or integrations that can't be mocked convincingly
- Organization makes decisions based on quantitative business cases rather than demos
- Stakeholders treat prototype as final spec rather than conversation starter
- Regulatory or compliance requirements demand formal written documentation as audit trail