Data foundation for assessing AI impact
Law firms are under increasing pressure to demonstrate the effect of AI on billable hours. Clients want evidence that efficiency gains are being passed on. Pricing teams need hard data to refine their models. And as firms push harder into alternative fee arrangements (AFAs), understanding exactly how AI is reshaping time spent on matters becomes critical.
Why task and phase coding matters – but falls short
Task and phase coding is a vital first step. It allows firms to break work into digestible chunks and start spotting where automation and AI could be reducing time. Without that granularity, it’s impossible to have a meaningful conversation about efficiency.
But coding alone doesn’t tell the whole story. Two tasks labelled the same may, in practice, be worlds apart depending on the type of matter, the client, the side represented, the complexity, or the specific commercial dynamics at play. If firms want to truly isolate the impact of AI, they must compare like for like matters – and that requires richer contextual data than most systems capture today.
The missing piece: contextual matter data
Better contextual data means knowing not just the phase of work, but the circumstances around it. For example:
- Was the firm acting for buyer or seller?
- Was the deal contested or straightforward?
- What key features, complexities, or market pressures shaped the engagement?
Only with this richer picture can firms properly benchmark how much time AI saves against comparable matters. Otherwise, you risk comparing apples with oranges and drawing flawed conclusions about impact.
Why starting early matters
Capturing contextual matter data now puts firms ahead of the curve. As AI adoption accelerates, those already measuring impact robustly will be able to respond quickly to client demands, refine their pricing strategies with confidence, and negotiate AFAs from a position of strength. Firms that wait will find themselves scrambling to retrofit data – a costly and imprecise exercise.
The Ayora approach
Ayora is the only system that closes the loop end to end:
- Contextual matter enrichment – automatically capturing the detail that makes matters comparable
- Task and phase reconstruction – breaking down time into meaningful, analysable chunks
- AI impact assessment – quantifying the difference AI is making across like for like matters
That full cycle is what turns raw data into actionable insight – and what ultimately enables firms to prove, not just assume, that AI is transforming their billable model.
