Use Cases

GenAI Use Case Explorer

Research-backed success rates and risk profiles from 44 verified enterprise deployments

68
Total Cases
6
Use Case Types
2018-25
Time Period

Use Case Profiles

Process Automation

Low Risk18 cases

Automating repetitive tasks, document processing, workflow optimization

85%
success rate

Content Generation

Low Risk12 cases

Marketing copy, reports, code generation, creative content

86%
success rate

Analysis & Insights

Medium Risk10 cases

Data analysis, forecasting, trend detection, anomaly identification

75%
success rate

Human Augmentation

Low Risk14 cases

Copilot tools, decision support, employee productivity enhancement

82%
success rate

Chatbots & Interaction

High Risk10 cases

Customer service chatbots, virtual assistants, conversational AI

62%
success rate

Decision Support

Low Risk4 cases

Clinical decision support, risk scoring, recommendation engines

100%
success rate

Hiring Screens: 4 of 4 Documented Deployments Failed

Critical Severity4 documented cases

Documented Failures

Amazon (2018)

Resume screening discriminated against female candidates. Model trained on historical hiring data that reflected gender bias (CASE-010).

IBM (2018)

Intentional age bias in candidate filtering systems (CASE-011).

Major Financial Services Firm (2024)

University of Washington audit found the firm's AI screening disproportionately downgraded resumes from Black candidates; system abandoned (CASE-012).

iTutorGroup (2020 to 2024)

Age and gender discrimination in hiring. $365K EEOC settlement (CASE-013).

Root Cause

Hiring data inherently reflects historical discrimination. ML models amplify these patterns at scale through proxy variables (name patterns, school types, zip codes) that encode protected characteristics without explicitly using them.

If You Must Proceed: Required Safeguards

  • 1Implement independent fairness audits by third parties before deployment
  • 2Monitor for bias in proxy variables (name patterns, school types, zip codes)
  • 3Require human review of 100% of hiring decisions
  • 4Conduct legal review of liability exposure before launch
  • 5Consider non-discriminatory applications: learning recommendations, performance management, retention prediction

Assess how your organization scores across all five RAPID dimensions.

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