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The biggest challenges in AI implementation aren't the headline-grabbing risks but the hidden ones stemming from human factors and organizational dynamics. Drawing parallels to aviation safety, successful AI deployment requires understanding how people interact with these tools in complex organizational settings.
• Hidden AI risks often come from human-system interactions rather than technical failures
• Real examples show how AI can create burnout, enable misinformation spread, or amplify existing biases
• Standard AI safety approaches often miss these subtle but critical issues
• The "Adopt, Sustain, Optimize" framework helps track the user journey through AI implementation
• Six categories of hidden risks include quality assurance, task-tool mismatch, and workflow challenges
• Proactive "pre-mortem" approaches are more effective than waiting for problems to emerge
• Human oversight only works when people have expertise, time, and authority to challenge AI outputs
• Successful implementation requires diverse teams, tailored training, and leadership understanding
• Measuring impact should go beyond efficiency to capture quality improvements and risk management
• Building resilient sociotechnical systems means designing for human realities, not just deploying technology