AI, Anxiety, and Actual Impact
summary
Claims concrete steps: turn executive hype into a roadmap, add measurable success criteria, and stop vague promises. Teams adopt specific tools—Figma components and LLM-powered rapid prototypes—to surface flaws in days not weeks; use AI for ops automation, research prep, QA and for new-product bets while leaving legacy systems measured. Anxiety spikes when AI in performance reviews appears, so the organization builds a playbook checklist, tracks adoption and impact metrics, and reports whether each prototype actually reduced time, improved quality, or failed.
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chapters (6)
- 0:00AI anxiety in design
- 3:17Cutting AI hype and uncertainty
- 8:50Habits for AI-driven design
- 16:56Reducing AI-related fear
- 22:09Designing actual impact
- 23:55Q&A on AI adoption
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