The AI Criticality Index: Measuring AI Dependence

Corinium’s Vanessa Jalleh spoke to Grant Case about the concept of the AI Criticality Index (ACI) and why organisations need a new way to measure the true impact, dependency, and risk of AI. As AI adoption accelerates, the conversation explores how leaders can move beyond surface-level metrics to better understand where AI is truly critical to business operations and strategy.

As organisations scale AI, traditional metrics like adoption rates and token usage fail to capture the true business impact and risk exposure. This conversation introduces the AI Criticality Index (ACI) as a practical framework for understanding dependency, resilience, and strategic importance of AI across the enterprise.

Here’s what we dive into in this interview:

• Why traditional AI metrics like adoption dashboards, token counts, and productivity gains fall short in capturing true business impact and risk.

• What the AI Criticality Index (ACI) is, where the idea came from, and the gap it fills in today’s AI measurement landscape.

• How the ACI works in practice, including what inputs organisations need and how leaders like CDOs and CDAOs can apply it.

• What risks are exposed if a major AI provider is suddenly unavailable, and how ACI helps organisations understand dependency and resilience.

• Practical guidance on how to run an ACI-style assessment, including where to start and how to get early value quickly.

   

   

Where AI measurement is heading next, and why understanding criticality will become increasingly important for governance, strategy, and risk oversight.

If you are interested to speak at our CDAO and CDAIO series in Australia and New Zealand, reach out to Vanessa Jalleh for more information.