The Prosocial AI Index offers business leaders a practical, auditable way to assess whether their AI systems are genuinely good, writes Wharton’s Cornelia Walther.
There is a management aphorism so deeply embedded in business culture that it rarely gets questioned: What gets measured gets managed. It is a useful heuristic — until it becomes a trap. For the better part of a decade, organizations have been deploying artificial intelligence at scale while measuring it almost exclusively through the lens of efficiency gains, cost reductions, and revenue lift. The instruments are precise. The picture they produce is radically incomplete. Amid the pervasiveness of AI, this reality patchwork is now amplified.
Existing dashboards do not capture whether an AI system is fair, whether it is eroding or building trust, whether it is making the people who use it more capable or quietly deskilling them, and whether its environmental footprint is accounted for or simply ignored. The gap between what we measure and what we should care about is not a technical failure. It is a values failure dressed up as a metrics problem.
The Prosocial AI Index proposes a practical answer to that failure. It gives executives, technologists, and governance teams a shared vocabulary and a structured scorecard for AI that is genuinely good — not just profitable in the short term, but durable, trustworthy, and aligned with the values an organization actually claims to hold.
