AI Failure Analysis: Essential Reading

MIT's research on GenAI reveals 95% of pilots fail to reach production. This deep analysis explains why generative AI pilots face unique scaling challenges and...

RAND's research reveals that 80%+ of AI projects fail, but the reasons are predictable and preventable. This analysis breaks down exactly what's driving the...

Navigate the complex legal landscape of AI liability. Understand product liability, professional negligence, algorithmic accountability, and emerging AI-specific liability frameworks across jurisdictions.

Healthcare AI faces a 79% failure rate. This analysis reveals the data privacy constraints, clinical validation requirements, and EHR integration challenges...

Most AI failures show warning signs 3–6 months before collapse. Learn the diagnostic framework to catch problems while they're fixable.

71% of AI tools fail to integrate into daily workflows, abandoned within 6 months despite technical success. Learn why integration—not technology—determines AI adoption and how to design for workflow fit.

67% of organizations regret their AI vendor selection within 12 months. Learn the 15 critical mistakes that lead to poor vendor choices and how to avoid costly procurement failures.

Comprehensive checklist covering readiness, data quality, vendor selection, governance, adoption, integration, ROI, scope, technical debt, and security. Use this master framework to prevent the failures that stop 70% of AI projects.

47% of struggling AI projects can be saved with structured recovery protocols. Learn the diagnostic framework, intervention strategies, and decision criteria for rescuing or killing failing AI initiatives.

50 essential security questions for AI vendor evaluation across data handling, security controls, compliance, and AI-specific concerns. Includes red flag answer indicators.

Identify warning signs early during AI vendor evaluation. Covers security evasiveness, unrealistic claims, and financial instability indicators.

Understand the root causes behind AI project failures, backed by research from McKinsey, Gartner, and MIT. Learn the top 12 reasons why AI initiatives stall and how to avoid them.

Real case studies of AI project failures across industries—from Amazon's hiring algorithm to IBM Watson Health. Learn what went wrong and how to avoid similar mistakes.

62% of AI training programs fail to drive adoption. Learn why technical training without context fails.

68% of AI projects fail to meet ROI expectations within 2 years. Learn why financial projections miss reality, how to avoid the 7 ROI calculation mistakes, and what successful organizations do differently.

78% of AI systems show measurable bias, yet only 31% of organizations test for it. Learn from real bias failures costing companies millions in lawsuits, fines, and reputation damage.