Webinar

Most CAS firms have moved past asking whether to use AI. The real gap now is between firms still prompting tools one task at a time and firms running systems that initiate work, flag anomalies, and assemble client-ready output on their own.

That shift only works if governance comes first. Data quality standards, review policies, and accountability structures are what make AI output safe to put in front of a client, and they're what make value pricing defensible once delivery cost drops. Without governance, faster output is just faster risk.

This session covers what operationalized AI actually looks like inside a CAS practice, why realization and utilization can't tell you if the investment is paying off, and what metrics should replace them. We'll also look at how firms are using AI in industry-specific client work, with a solution demo showing what this looks like in practice.