• AI-powered FinOps tools work perfectly—95% accuracy, 3-minute anomaly detection, 70-85% actionable recommendations—yet 68% of identified savings never get implemented due to organizational bottlenecks, not technology limitations • Real example: AWS Cost Optimization Hub generated a perfect $200K/year savings recommendation in 12 minutes, but implementation took 7 weeks requiring CFO approval, engineering sign-off, and product team confirmation • AI cannot automate business context decisions (Black Friday traffic clusters need headroom despite low average utilization), stakeholder negotiation (VP Product doesn’t care about infrastructure costs), or application architecture changes (Lambda→Fargate migration requires 6 weeks of engineering time) • The 6% who succeed have three things: executive sponsorship (cost optimization in engineering OKRs), cross-functional accountability (teams own their cloud costs), and automated enforcement (Active Assist auto-apply, Azure Policy governance) • Decision framework: Adopt AI tools for multi-cloud billing chaos (Google FinOps Hub + FOCUS), weekly cost spike surprises (free AWS/GCP anomaly detection), or FinOps team spending ＞50% time on reporting—but NOT if you lack tagging/ownership/process or your problem is architectural (80% of cost in 2 services) • 90-day playbook: Days 1-30 audit and show leadership the $10M/year waste, Days 31-60 run free tool POC with one team, Days 61-90 roll out to 20% of org with measured impact • Monday morning actions: Enable AWS Cost Anomaly Detection (15 min, free), calculate waste (15 hrs/week × $150/hr × team size), evaluate one AI recommendation with business context, build leadership business case with 90-day plan