What goes wrong when you ignore that incentive design as system change?

The most common reason fails: Designing incentives that optimize one dimension at the expense of others. Every metric creates pressure toward the measured dimension and neglect of unmeasured dimensions. A sales team incentivized on revenue will pursue revenue at the expense of profitability. An engineering team incentivized on velocity will pursue speed at the expense of quality. A support team incentivized on response time will pursue speed at the expense of resolution. The failure mode is not that the incentive is wrong — it is that the incentive is incomplete. Single-metric incentives always produce lopsided optimization. Balanced incentives require multiple metrics that together capture the full range of desired behavior.

The fix: Audit the incentive system for one role in your organization. List every metric that is measured, reported, or rewarded — both formally (performance reviews, bonuses, promotions) and informally (what gets praised in meetings, what gets attention from leadership, what gets criticized). For each metric, ask: If someone optimized exclusively for this metric, what behavior would they exhibit? Would that behavior serve the organization well? If optimizing for the metric produces dysfunctional behavior, the metric is misaligned — it measures something different from what the organization actually needs. Redesign the metric to align measurement with organizational need. The most common misalignment: measuring output (how much was produced) when the organization needs outcome (what impact was achieved).

The underlying principle is straightforward: What gets measured and rewarded determines what people actually do. Incentive design is the most powerful lever for systemic change because incentives operate continuously, automatically, and at scale — shaping behavior across the entire organization without requiring individual intervention. But incentives are also the most dangerous lever because poorly designed incentives produce precisely the behavior they measure, including the dysfunctional side effects of optimizing for the measured dimension at the expense of unmeasured dimensions. Goodhart's Law — "When a measure becomes a target, it ceases to be a good measure" — is the central challenge of incentive design.