Figr Design Blog

Most AI accessibility tools just scan for WCAG violations and spit out long error lists, but they don’t understand design intent, your system, or which fixes actually matter for users. The real unlock is guardrail-based tools that prevent violations during design, suggest brand-safe compliant alternatives, and prioritize issues by user impact. Accessibility success then stops being “we passed an audit” and becomes “people with disabilities can actually use what we ship.”

Most AI dashboard tools spit out pretty layouts but ignore data models, states, and responsive behavior, so teams hit a wall when engineering asks, "how does this actually work?" The real unlock is context-aware prototyping that understands your schema, constraints, and design system, and outputs specs (data, states, interactions, components), not just visuals. Teams using these data-first, spec-complete tools cut dashboard cycles from weeks to days because the first prototype is actually buildable.

Most AI interview tools stop at clean transcripts and pretty theme clusters, so teams still guess what to build. The real value is mapping raw quotes to specific product flows and turning them into concrete design changes. If it still ends in a Notion doc instead of shipped fixes, you do not have a research problem, you have a research-to-design translation problem.

Most AI tools jump from prompt to pretty wireframes without doing the hard work: flows, states, and constraints. That makes teams feel fast while actually baking in bad assumptions, causing endless redesigns once engineering and users hit reality. The real unlock is bottom-up, flow-first tools (like Figr) that ingest product context and generate grounded wireframes that need far fewer revisions.

Most A/B testing tools are great at measuring results but do nothing to help you generate strong, behavior-grounded hypotheses, so teams burn weeks testing tiny, low-impact tweaks. AI flips this by generating production-ready variants grounded in patterns and analytics, expanding the hypothesis space and making bold experiments cheap. The real future of A/B testing is not better stats, it is smarter hypothesis generation and impact modeling, so you learn faster and ship meaningfully better products.

Most AI feedback tools stop at clustering comments, so teams still rely on gut feel to decide what to build. The real unlock is tying feedback to user segments, behavior, and business impact, then turning that into concrete design recommendations. Tools like Figr shrink the loop from “user complained about X” to “shipped fix for X,” so feedback becomes a design input instead of a prettier backlog.