The failure isn't technological—it's categorical. Organizations deploy AI for problems it can't solve while ignoring where it excels. Through analysis of disasters from IBM Watson's $4 billion failure to Uber's fatal autonomous vehicle crash, you'll learn the specific patterns that predict failure. The book provides the exact assessment questions that companies like Cleveland Clinic and JPMorgan now use to avoid catastrophic AI deployments. Skip the multimillion-dollar mistakes and get the framework that actually works.

SCAR (Safety, Complexity, Accountability, Resilience) transforms AI decisions from expensive gambles into systematic evaluations. You'll get the complete assessment worksheets, scoring matrices, and decision trees that reveal whether AI belongs in your specific context. This isn't theory—it's the same framework now preventing disasters in emergency dispatch, medical diagnosis, and financial services. One assessment can save your organization from becoming the next cautionary tale. No more vendor promises or consultant handwaving—just clear, defensible decisions.

Aviation's greatest disasters and triumphs reveal why the most sophisticated AI often fails while "boring" algorithms save lives. You'll discover why Garmin's life-saving Autoland system uses zero AI, why Tesla's Autopilot failures were predictable, and what the Boeing 737 MAX crashes teach about accountability. These aren't just stories—they're blueprints for making defensible technology decisions when failure means litigation, bankruptcy, or death. Learn why the FAA refuses to certify machine learning for flight control and what that means for your industry.

One bad AI deployment can end careers—ask the executives who championed Watson for Oncology or approved Uber's fatal autonomous vehicle program. This book arms you with the documentation, assessment criteria, and decision frameworks that protect you when AI goes wrong elsewhere. You'll learn exactly how to document your AI decisions to withstand regulatory scrutiny, litigation discovery, and board investigations. When your competitors' AI fails spectacularly, you'll have the paper trail proving why you made better choices. Career insurance has never been this comprehensive.

Skip the theory and get straight to implementation with ready-to-use SCAR assessment worksheets, risk matrices tailored to your industry, and decision documentation templates that satisfy regulators. Every tool has been field-tested in real deployments across healthcare, transportation, and financial services. You'll get the exact questions to ask vendors, the red flags that should stop any deployment, and the "courageous conversation" scripts for telling leadership when AI won't work. Download the templates, customize for your organization, and start making better decisions immediately. These tools alone are worth 10x the book's price.

When AI fails—not if, but when—investigators, regulators, and lawyers will demand answers. This book shows you exactly what documentation courts require, what regulators actually check, and how to prove you acted responsibly even when systems fail. Learn from real legal cases including the Uber fatality prosecution, Tesla Autopilot litigation, and EU AI Act enforcement actions. You'll get the specific phrases that protect you legally, the audit trails that demonstrate due diligence, and the assessment documentation that shifts liability away from your decisions. Don't wait until depositions to discover what you should have documented.