Most AI governance focuses on outputs: compliance, explainability, and monitoring. These controls operate too late. The primary risk sits upstream—in what decisions AI is allowed to make, whose judgment it inherits, and which signals it amplifies.

Effective AI governance requires measuring decision capacity and quality before automation accelerates it. When decision signal is governed, AI stabilizes performance. When it is not, AI accelerates fragility.

This is where our work begins.

We measure decision capacity before AI accelerates it—identifying which decision patterns are high-signal, where dysfunction distorts judgment, and whose authority should govern automated systems. By making decision behavior visible upstream, we help organizations define what decisions AI is permitted to make, whose judgment it inherits, and where human oversight must remain. This governance happens before automation scales misalignment, not after.

Ready to Learn More?

Learn how we measure decision signal → What We Measure
Understand how it all fits together → Decision Coherence
Let’s talk→ Contact