Why Your AI Adoption Scorecard Is A False Proxy

The episode warns that many firms treat AI adoption scorecards as a proxy for performance, but they often capture only superficial tool usage. Dr. Jonathan explains that a proxy is useful only when it strongly correlates with the underlying outcome—here, better, evidence‑based decisions. By equating hours logged, login frequency, or report count with success, organizations ignore the real goal: faster, higher‑quality decision making that drives revenue, efficiency, or strategic advantage. This misalignment mirrors earlier diversity‑inclusion metrics that prized representation over genuine inclusion.

Measuring the wrong variable creates perverse incentives. Employees learn to game visible metrics, producing a flood of AI‑generated reports that no one reads, while managers reward volume over impact. The host cites a scenario where an employee who generates ten low‑value reports scores higher than a colleague who makes one strategic decision using AI. Such “AI slop” inflates productivity numbers without improving outcomes, eroding trust in analytics and wasting resources. In 2026, the proxy problem has shifted from DEI to AI, but the underlying risk remains identical.

The fix is to redesign scorecards around decision quality and measurable business results. Ask, ‘to what effect?’ instead of ‘how much?’ stress‑test any metric: can a high score be achieved without any positive impact? Align AI metrics with KPIs such as revenue uplift, cost reduction, or time‑to‑insight. By ensuring a strong correlation between the proxy and the desired outcome, organizations incentivize meaningful AI use and avoid performative activity. Listeners are encouraged to audit their own AI adoption frameworks and prioritize impact over usage.