Multinational exec ditches headcount savings for KPI-based AI ROI

The post from Reddit user /u/Reds_PR describes their role auditing AI benefits in a large multinational. They found that initial AI proposals relied on rosy headcount reduction estimates, assuming that aggregate time savings (e.g., 2000 hours) automatically translate into job cuts. The author argues this is unrealistic because operational decisions depend on many independent variables, making year-over-year cost/revenue spreadsheets an unreliable measure of AI impact. The core issue: companies swallow the sizzle (promised savings) but rarely check whether they actually got the steak.

To fix this, the author recommends replacing financial predictions with KPI predictions—like 'cost per widget' or 'revenue per headcount'—and then measuring actual changes against a baseline at 3, 6, and 12 months post-launch. To make results comparable across departments, they propose converting improvements into Z-scores, standardizing success as rate of improvement. The author notes that leadership is now pumping the brakes on AI hype, asking for hard evidence of value. The post concludes with a call for other professionals to share their own cost/benefit measurement strategies.