Why AI projects fail (and what we can do about it)

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