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Why AI projects fail (and what we can do about it)
Summary
Key Insights
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Most AI projects fail due to choosing the wrong problems rather than poor execution.
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The AI innovation gap occurs because data scientists focus on technically hard but low-value problems.
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User-centered design alone often identifies problems unsuitable for AI solutions.
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AI works best for low-risk, moderate accuracy (~90%), narrow tasks like spam filtering or smart text suggestions.
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Executives push AI fast to avoid falling behind, risking deployments without clear user or business value.
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Consequences scanning is a vital method to detect and mitigate ethical risks before AI product launch.
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Matchmaking AI capabilities with real user needs and organizational goals increases project success.
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Agents add new AI capabilities to sense, think, and act, requiring adapted design processes.
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AI lacks context, aesthetic taste, and common sense—designers must provide these to create effective AI products.
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There is currently little corporate or legal accountability for AI ethics; responsibility largely falls to product teams.
Notable Quotes
"Welcome to the AI party, we've got 10 years of research to share."
"Executives said it's better to go fast and fail than to go slow and succeed."
"AI can be magical, but it's also just not that smart."
"Don Norman was named the top accomplished woman in UX by AI, even though Don is a man."
"Companies hunt for AI innovation in technically challenging areas instead of simpler, more valuable places."
"Most AI fails because projects require near perfect accuracy, which AI can't reliably deliver."
"The traditional user-centered design process doesn't work well for AI problems."
"Matchmaking connects AI capabilities to user needs so you find the low hanging fruit projects."
"AI struggles with context, taste, and common sense, and designers bring that to the table."
"Consequences scanning helps detect unintended harms and risks before launching AI features."
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