The numbers below are scoping targets, stated as assumptions - not observed results. Every engagement starts by measuring your actual baseline. Law firms deploying this automation typically target 25-40% reductions in intake-to-engagement timeline, directly improving client perception and engagement velocity. Non-billable administrative review time is scoped to drop 20-30%, freeing 200-300 partner hours annually for business development. Realization rates are targeted for a 15-25% lift because matter coding errors and billing miscategorizations decrease, and conflicts are caught at intake instead of mid-engagement. For a 50-person firm, the modeled recovery is $90,000 - $150,000 in billable capacity and reduced write-off exposure within the first six months.
The gains compound over 12 months as the system learns your firm's intake patterns and conflict-checking rules. The working assumption by month six is that each intake coordinator handles 30-40% higher volume with lower error rates. By month twelve, the AI has enough history to surface systemic routing issues - certain practice groups consistently miscategorizing matters, specific referral sources needing different intake protocols - enabling process fixes that further reduce review cycles. Secondary effects worth modeling: better associate leverage as junior staff spend less time on intake validation, and lower attrition risk as the administrative burden on intake coordinators drops. Check every one of these numbers against your own intake log before taking them at face value - that is what the baseline measurement is for.