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Leading through ambiguity: Supporting a design team relearning their craft
Summary
Our team had been designing AI features for some time before we were forced to confront what that meant for our own craft. At Articulate, we build tools that help people create learning at scale. As we introduced AI-augmented creation workflows, we focused on helping learning designers move faster and generate ideas more easily. But those same forces began reshaping how our own design team worked under the pressure of sustained ambiguity. What started as workflow optimization became real-time field research into how designers learn, resist, and adapt when the act of creation itself changes. Familiar practices began to break, observing where problem framing eroded, critique expanded instead of converging, and experience stopped guaranteeing great judgment. This talk is an ongoing case study in leading teams through that shift. It also reflects on how our internal experience designing with AI became a critical lens for understanding the people we build for.
Key Insights
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AI-generated content often hides a reasoning gap where the AI's assumptions diverge from the creator's original intentions.
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Polished AI outputs create a false sense of completion, making it harder to question or critique the work effectively.
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Humans are poor judges of how much AI is actually helping; confidence from AI can be misleading.
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Visible reasoning features in AI tools often do not accurately reflect the model's true decision-making process.
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Design critique collapses when teams fail to name clear criteria for judgment and when responsibility for AI outputs is unclear.
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Adaptation to AI is less about acquiring new tools and more about clearing away noise and reclaiming existing core skills.
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The role identity of designers is shifting from deeply hidden craft to more visible communication and reasoning skills akin to management.
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Leadership must recognize when team members struggle to adapt and balance support with holding expectations to maintain impact.
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Rapid AI tool adoption increases the volume of outputs faster than teams can inspect or align on underlying reasoning.
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Building bridges across the reasoning gap requires shared language and explicit articulation of decisions during AI-assisted workflows.
Notable Quotes
"The gap between what the output says and what the intention was is what we're going to call the reasoning gap."
"AI doesn't make bad ideas more dangerous. It makes unfinished ideas more convincing."
"The number of crossings between intention and generation is increasing faster than our ability to examine what happened between them."
"Between your prompt and prototype, the AI made 40 decisions that you can't trace."
"The shown reasoning wasn't the actual reasoning. It was just a pretty story to get us to trust it."
"If you're presenting a concept, you're endorsing it. You need to be able to say why."
"What AI is asking out of every individual contributor is to take on the skill set of a manager."
"When tired becomes the norm, people get playful again because they stop being married to the tools and start being curious about what's next."
"Your job as a leader isn't just empathy. It's naming what the new system requires and holding the line."
"The work right now isn't about outrunning the reasoning gap. The goal is to build bridges across it."
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