Most enterprises measure AI progress by number of models live or use cases launched. This tracks activity, not whether revenue rose or costs fell in the accountable business unit.
When a sales leader's bonus depends on CRM feature adoption instead of pipeline conversion, the team optimises for clicks. The model becomes another checkbox rather than a decision engine.
Finance teams approve AI spend against project milestones. Delivery happens on time, the model sits idle, and no one recalibrates the original assumption that the process needed changing.
Procurement and risk functions add approval layers measured by policy compliance. Speed drops, shadow tools proliferate, and the original productivity case evaporates before first production run.
Boards receive dashboards of model accuracy scores and training hours. These numbers rise while operating margins in the affected divisions stay unchanged or worsen.
Fix it by moving every AI initiative onto an outcome owner with P&L accountability. Tie their variable pay to measurable deltas in cost, revenue or cycle time within six months, not deployment dates.
Kill activity-based scorecards. Replace them with a short set of outcome metrics reviewed monthly by the same people who sign the budget, not by a central AI office that owns nothing.