hebel. today · 15 November 20265 / 79

Arts · Design Thinking

Designers shift from doing to judging AI outputs

Judging AI-supported decisions. Patrizia Bertini argues that practitioners must now teach judgement as a core skill, as AI-enabled decisions risk becoming an assembly line where humans process recommendations they lack the time to examine.…

Judging AI-supported decisions. Patrizia Bertini argues that practitioners must now teach judgement as a core skill, as AI-enabled decisions risk becoming an assembly line where humans process recommendations they lack the time to examine (UX Collective). Ileana Marcut demonstrates this by reviewing a support chat where an AI promises a refund; designers, product owners, and engineers must hold a 20-minute shared review to confirm whether the system can actually verify the charge and execute the payment.

Bridging gaps with analogies. A home organizer struggling with marketing technology overcomes procrastination by hooking digital tasks to familiar physical organizational structures, a method David Ausubel calls learning by analogy (Growth Mindset). Chris Kalaboukis suggests that rather than mastering AI technology, users should identify one useful task and discover if AI can help them accomplish it to narrow the gap between intent and execution (Nomads 50+ Midlife Recalibrated, Travel…, Nomads 50+ Midlife Recalibrated, Travel…).

Refining prototypes as specifications. Designers at TLDR Design treat AI-generated prototypes as functional specifications that demand professional validation for accessibility and technical integration, as these models provide concrete expectations for stakeholders (TLDR Design). Ravi Mehta defines successful user experience design as the ability to apply usability principles and design patterns to make it easy for agents to access a product's full value, regardless of the interface doorway (Ravi Mehta from Top 1% Builder with Ravi).

Solving problems through perspective. A father helping his son with a geography puzzle discovers that the boy succeeds by looking at the problem from the other side, proving that the way a problem is defined determines the solutions available (Nobody). Bryant McGill notes that a committed decision produces consequences that deliberation cannot supply, making movement an instrument for learning (Bryant McGill).

Try today · 20 min

Run a 20-minute 'commitment review' on one AI-generated response in your product or workflow.

Gather one person from design, engineering, and policy to check if your system can actually support the promise the AI made.

You need

  • One specific AI response
  • A designer
  • An engineer
  • A policy or operations owner

Steps

  1. Write down the user request and the information available to the system.
  2. Identify the specific commitments made in the AI response.
  3. List the conditions that must be true for the response to hold.
  4. Confirm which conditions the system can establish today.
  5. Assign follow-up questions for missing capabilities.

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Sources

  1. UX Collective on hebel. →Patrizia Bertini outlines the need for teaching judgement and systems thinking to those signing off on AI-enabled decisions.

  2. Growth Mindset on hebel. →This Q&A session applies David Ausubel’s learning theory to help a home organizer navigate unfamiliar marketing technology.

  3. Bryant McGill on hebel. →Bryant McGill provides short lessons on how action generates feedback that deliberation cannot supply in complex environments.

  4. Nobody on hebel. →A story about a geography puzzle highlights how redefining a problem can reveal solutions from the other side.

  5. Ravi Mehta, Top 1% Builder with Ravi on hebel. → ✎ Written by a personRavi Mehta defines user experience design as the ability to apply usability principles to help humans or agents access value.

  6. Nomads 50+ Midlife Recalibrated, Travel… on hebel. →Chris Kalaboukis argues for focusing on useful tasks rather than deep technical knowledge when starting with AI tools.

  7. TLDR Design on hebel. →TLDR Design discusses treating AI prototypes as functional specifications that require professional refinement and validation.

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