Tools are moments. Capabilities compound.
This video is featured in the AI Conference playlist.
Summary
As design organizations race to adopt AI, many still measure readiness through tool fluency — who can use ChatGPT, Claude, or Figma Make the fastest. But tools, as we’ve learned, are ephemeral. The deeper challenge is defining the durable human capabilities that remain valuable as AI fundamentally reshapes design work. This talk explores a new approach to AI readiness for design organizations: moving beyond tool proficiency to identify the underlying skills, competencies, and capabilities that scale across changing technologies. We’ll examine the distinction between skills and capabilities, the process of building an AI capability framework for design, and how we’re mapping designers against those competencies to establish a baseline and path forward. This session offers a practical, human-centered model for building resilient, AI-augmented design organizations — grounded in durable capability, not temporary tooling trends.
Key Insights
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Measuring AI fluency by tool usage is insufficient and often misleading; focusing on human judgment and observable behaviors offers deeper understanding.
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Vanessa distinguishes between skills (tool-specific, ephemeral), competencies (applied judgment using multiple skills), and capabilities (umbrella outcomes across competencies).
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A capability framework with six core areas and 21 competencies was built collaboratively with hundreds of Amazon design practitioners across roles.
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Creative exploration, quality and responsibility, insight and synthesis, systems and architecture, learning and adaptability, and AI-human collaboration are the framework’s six key capabilities.
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Gamified assessments with about 30 questions allow designers to baseline their fluency, receive diagnostics, and get an actionable growth map.
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Frequency of AI tool use doesn’t equate to fluency; depth of skill integration and confidence in directing AI matter much more.
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Patterns show common gaps in quality and responsibility and systems thinking, which are areas for targeted learning and development investments.
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Role assumptions are challenged—design program managers aren’t always stronger in systems thinking, for example, revealing real-world variance and learning needs.
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Providing a shared vocabulary around AI fluency helps practitioners articulate and connect their evolving AI skills beyond specific tools.
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Baseline assessments need to be challenging enough to create a growth ceiling, balancing accuracy and aspirational goal setting without skewing results.
Notable Quotes
"I actually don’t need to feel ready to learn, and I can be uncomfortable or scared and still learn."
"It feels like we’ve forgotten to ask what the humans were actually building while chasing the tools."
"The difference is putting the human and their agency back into the mix with the question: What can you do with AI?"
"Fluency is how deeply you can work with AI—the judgment and direction you bring, not just how often you use it."
"The skills are tied to the tool and ephemeral; capabilities are what belong to us and travel with us as tools change."
"We wanted to create something that’s not just a survey but a robust, gamified experience to baseline current skills and provide a growth map."
"We’re focusing on observable behaviors and outcomes, not specific tools or time saved, to measure AI fluency."
"Everyone is learning and evolving right now; they just need language and frameworks to name what they’re growing."
"If the bar is too low, everyone looks very capable, and that doesn’t help growth or trust in the mapping."
"AI is the instrument. You all are the musicians. Build the capabilities that make quality music."
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